Bayesian modeling to assess populated areas impacted by radiation from Fukushima

NASA Astrophysics Data System (ADS)

Hultquist, C.; Cervone, G.

2017-12-01

Citizen-led movements producing spatio-temporal big data are increasingly important sources of information about populations that are impacted by natural disasters. Citizen science can be used to fill gaps in disaster monitoring data, in addition to inferring human exposure and vulnerability to extreme environmental impacts. As a response to the 2011 release of radiation from Fukushima, Japan, the Safecast project began collecting open radiation data which grew to be a global dataset of over 70 million measurements to date. This dataset is spatially distributed primarily where humans are located and demonstrates abnormal patterns of population movements as a result of the disaster. Previous work has demonstrated that Safecast is highly correlated in comparison to government radiation observations. However, there is still a scientific need to understand the geostatistical variability of Safecast data and to assess how reliable the data are over space and time. The Bayesian hierarchical approach can be used to model the spatial distribution of datasets and flexibly integrate new flows of data without losing previous information. This enables an understanding of uncertainty in the spatio-temporal data to inform decision makers on areas of high levels of radiation where populations are located. Citizen science data can be scientifically evaluated and used as a critical source of information about populations that are impacted by a disaster.

Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013.

PubMed

Wang, Lizhe; Chen, Lajiao

2016-07-05

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present a dataset on Chinese population distribution and its driving factors over a remarkably long period, from 1949 to 2013. Driving factors of population distribution were selected according to the push-pull migration laws, which were summarized into four categories: natural environment, natural resources, economic factors and social factors. Natural environment and natural resources indicators were calculated using Geographic Information System (GIS) and Remote Sensing (RS) techniques, whereas economic and social factors from 1949 to 2013 were collected from the China Statistical Yearbook and China Compendium of Statistics from 1949 to 2008. All of the data were quality controlled and unified into an identical dataset with the same spatial scope and time period. The dataset is expected to be useful for understanding how population responds to and impacts environmental change.

Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013

NASA Astrophysics Data System (ADS)

Wang, Lizhe; Chen, Lajiao

2016-07-01

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present a dataset on Chinese population distribution and its driving factors over a remarkably long period, from 1949 to 2013. Driving factors of population distribution were selected according to the push-pull migration laws, which were summarized into four categories: natural environment, natural resources, economic factors and social factors. Natural environment and natural resources indicators were calculated using Geographic Information System (GIS) and Remote Sensing (RS) techniques, whereas economic and social factors from 1949 to 2013 were collected from the China Statistical Yearbook and China Compendium of Statistics from 1949 to 2008. All of the data were quality controlled and unified into an identical dataset with the same spatial scope and time period. The dataset is expected to be useful for understanding how population responds to and impacts environmental change.

Integrative Spatial Data Analytics for Public Health Studies of New York State

PubMed Central

Chen, Xin; Wang, Fusheng

2016-01-01

Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State. PMID:28269834

Discovering network behind infectious disease outbreak

NASA Astrophysics Data System (ADS)

Maeno, Yoshiharu

2010-11-01

Stochasticity and spatial heterogeneity are of great interest recently in studying the spread of an infectious disease. The presented method solves an inverse problem to discover the effectively decisive topology of a heterogeneous network and reveal the transmission parameters which govern the stochastic spreads over the network from a dataset on an infectious disease outbreak in the early growth phase. Populations in a combination of epidemiological compartment models and a meta-population network model are described by stochastic differential equations. Probability density functions are derived from the equations and used for the maximal likelihood estimation of the topology and parameters. The method is tested with computationally synthesized datasets and the WHO dataset on the SARS outbreak.

A comprehensive population dataset for Afghanistan constructed using GIS-based dasymetric mapping methods

USGS Publications Warehouse

Thompson, Allyson L.; Hubbard, Bernard E.

2014-01-01

This report summarizes the application of dasymetric methods for mapping the distribution of population throughout Afghanistan. Because Afghanistan's population has constantly changed through decades of war and conflict, existing vector and raster GIS datasets (such as point settlement densities and intensities of lights at night) do not adequately reflect the changes. The purposes of this report are (1) to provide historic population data at the provincial and district levels that can be used to chart population growth and migration trends within the country and (2) to provide baseline information that can be used for other types of spatial analyses of Afghanistan, such as resource and hazard assessments; infrastructure and capacity rebuilding; and assisting with international, regional, and local planning.

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

NASA Astrophysics Data System (ADS)

Gaitan, S.; ten Veldhuis, J. A. E.

2015-06-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

The interplay between human population dynamics and flooding in Bangladesh: a spatial analysis

NASA Astrophysics Data System (ADS)

di Baldassarre, G.; Yan, K.; Ferdous, MD. R.; Brandimarte, L.

2014-09-01

In Bangladesh, socio-economic and hydrological processes are both extremely dynamic and inter-related. Human population patterns are often explained as a response, or adaptation strategy, to physical events, e.g. flooding, salt-water intrusion, and erosion. Meanwhile, these physical processes are exacerbated, or mitigated, by diverse human interventions, e.g. river diversion, levees and polders. In this context, this paper describes an attempt to explore the complex interplay between floods and societies in Bangladeshi floodplains. In particular, we performed a spatially-distributed analysis of the interactions between the dynamics of human settlements and flood inundation patterns. To this end, we used flooding simulation results from inundation modelling, LISFLOOD-FP, as well as global datasets of population distribution data, such as the Gridded Population of the World (20 years, from 1990 to 2010) and HYDE datasets (310 years, from 1700 to 2010). The outcomes of this work highlight the behaviour of Bangladeshi floodplains as complex human-water systems and indicate the need to go beyond the traditional narratives based on one-way cause-effects, e.g. climate change leading to migrations.

Asynchrony in the inter-annual recruitment of lake whitefish Coregonus clupeaformis in the Great Lakes region

USGS Publications Warehouse

Zischke, Mitchell T.; Bunnell, David B.; Troy, Cary D.; Berglund, Eric K.; Caroffino, David C.; Ebener, Mark P.; He, Ji X.; Sitar, Shawn P.; Hook, Tomas O.

2017-01-01

Spatially separated fish populations may display synchrony in annual recruitment if the factors that drive recruitment success, particularly abiotic factors such as temperature, are synchronised across broad spatial scales. We examined inter-annual variation in recruitment among lake whitefish (Coregonus clupeaformis) populations in lakes Huron, Michigan and Superior using fishery-dependent and -independent data from 1971 to 2014. Relative year-class strength (RYCS) was calculated from catch-curve residuals for each year class across multiple sampling years. Pairwise comparison of RYCS among datasets revealed no significant associations either within or between lakes, suggesting that recruitment of lake whitefish is spatially asynchronous. There was no consistent correlation between pairwise agreement and the distance between datasets, and models to estimate the spatial scale of recruitment synchrony did not fit well to these data. This suggests that inter-annual recruitment variation of lake whitefish is asynchronous across broad spatial scales in the Great Lakes. While our method primarily evaluated year-to-year recruitment variation, it is plausible that recruitment of lake whitefish varies at coarser temporal scales (e.g. decadal). Nonetheless, our findings differ from research on some other Coregonus species and suggest that local biotic or density-dependent factors may contribute strongly to lake whitefish recruitment rather than inter-annual variability in broad-scale abiotic factors.

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

PubMed

Moerel, Michelle; De Martino, Federico; Kemper, Valentin G; Schmitter, Sebastian; Vu, An T; Uğurbil, Kâmil; Formisano, Elia; Yacoub, Essa

2018-01-01

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T 2 * weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T 2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T 2 * weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T 2 * weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T 2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked. Copyright © 2017 Elsevier Inc. All rights reserved.

Toward robust estimation of the components of forest population change: simulation results

Treesearch

Francis A. Roesch

2014-01-01

This report presents the full simulation results of the work described in Roesch (2014), in which multiple levels of simulation were used to test the robustness of estimators for the components of forest change. In that study, a variety of spatial-temporal populations were created based on, but more variable than, an actual forest monitoring dataset, and then those...

Redistribution population data across a regular spatial grid according to buildings characteristics

NASA Astrophysics Data System (ADS)

Calka, Beata; Bielecka, Elzbieta; Zdunkiewicz, Katarzyna

2016-12-01

Population data are generally provided by state census organisations at the predefined census enumeration units. However, these datasets very are often required at userdefined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

Evaluation of Uncertainty in Precipitation Datasets for New Mexico, USA

NASA Astrophysics Data System (ADS)

Besha, A. A.; Steele, C. M.; Fernald, A.

2014-12-01

Climate change, population growth and other factors are endangering water availability and sustainability in semiarid/arid areas particularly in the southwestern United States. Wide coverage of spatial and temporal measurements of precipitation are key for regional water budget analysis and hydrological operations which themselves are valuable tool for water resource planning and management. Rain gauge measurements are usually reliable and accurate at a point. They measure rainfall continuously, but spatial sampling is limited. Ground based radar and satellite remotely sensed precipitation have wide spatial and temporal coverage. However, these measurements are indirect and subject to errors because of equipment, meteorological variability, the heterogeneity of the land surface itself and lack of regular recording. This study seeks to understand precipitation uncertainty and in doing so, lessen uncertainty propagation into hydrological applications and operations. We reviewed, compared and evaluated the TRMM (Tropical Rainfall Measuring Mission) precipitation products, NOAA's (National Oceanic and Atmospheric Administration) Global Precipitation Climatology Centre (GPCC) monthly precipitation dataset, PRISM (Parameter elevation Regression on Independent Slopes Model) data and data from individual climate stations including Cooperative Observer Program (COOP), Remote Automated Weather Stations (RAWS), Soil Climate Analysis Network (SCAN) and Snowpack Telemetry (SNOTEL) stations. Though not yet finalized, this study finds that the uncertainty within precipitation estimates datasets is influenced by regional topography, season, climate and precipitation rate. Ongoing work aims to further evaluate precipitation datasets based on the relative influence of these phenomena so that we can identify the optimum datasets for input to statewide water budget analysis.

A Spatially Distinct History of the Development of California Groundfish Fisheries

PubMed Central

Miller, Rebecca R.; Field, John C.; Santora, Jarrod A.; Schroeder, Isaac D.; Huff, David D.; Key, Meisha; Pearson, Don E.; MacCall, Alec D.

2014-01-01

During the past century, commercial fisheries have expanded from small vessels fishing in shallow, coastal habitats to a broad suite of vessels and gears that fish virtually every marine habitat on the globe. Understanding how fisheries have developed in space and time is critical for interpreting and managing the response of ecosystems to the effects of fishing, however time series of spatially explicit data are typically rare. Recently, the 1933–1968 portion of the commercial catch dataset from the California Department of Fish and Wildlife was recovered and digitized, completing the full historical series for both commercial and recreational datasets from 1933–2010. These unique datasets include landing estimates at a coarse 10 by 10 minute “grid-block” spatial resolution and extends the entire length of coastal California up to 180 kilometers from shore. In this study, we focus on the catch history of groundfish which were mapped for each grid-block using the year at 50% cumulative catch and total historical catch per habitat area. We then constructed generalized linear models to quantify the relationship between spatiotemporal trends in groundfish catches, distance from ports, depth, percentage of days with wind speed over 15 knots, SST and ocean productivity. Our results indicate that over the history of these fisheries, catches have taken place in increasingly deeper habitat, at a greater distance from ports, and in increasingly inclement weather conditions. Understanding spatial development of groundfish fisheries and catches in California are critical for improving population models and for evaluating whether implicit stock assessment model assumptions of relative homogeneity of fisheries removals over time and space are reasonable. This newly reconstructed catch dataset and analysis provides a comprehensive appreciation for the development of groundfish fisheries with respect to commonly assumed trends of global fisheries patterns that are typically constrained by a lack of long-term spatial datasets. PMID:24967973

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

USGS Publications Warehouse

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods

PubMed Central

Shandas, Vivek; Voelkel, Jackson; Rao, Meenakshi; George, Linda

2016-01-01

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice. PMID:27527205

Spatial and temporal synchrony in reptile population dynamics in variable environments.

PubMed

Greenville, Aaron C; Wardle, Glenda M; Nguyen, Vuong; Dickman, Chris R

2016-10-01

Resources are seldom distributed equally across space, but many species exhibit spatially synchronous population dynamics. Such synchrony suggests the operation of large-scale external drivers, such as rainfall or wildfire, or the influence of oasis sites that provide water, shelter, or other resources. However, testing the generality of these factors is not easy, especially in variable environments. Using a long-term dataset (13-22 years) from a large (8000 km(2)) study region in arid Central Australia, we tested firstly for regional synchrony in annual rainfall and the dynamics of six reptile species across nine widely separated sites. For species that showed synchronous spatial dynamics, we then used multivariate follow a multivariate auto-regressive state-space (MARSS) models to predict that regional rainfall would be positively associated with their populations. For asynchronous species, we used MARSS models to explore four other possible population structures: (1) populations were asynchronous, (2) differed between oasis and non-oasis sites, (3) differed between burnt and unburnt sites, or (4) differed between three sub-regions with different rainfall gradients. Only one species showed evidence of spatial population synchrony and our results provide little evidence that rainfall synchronizes reptile populations. The oasis or the wildfire hypotheses were the best-fitting models for the other five species. Thus, our six study species appear generally to be structured in space into one or two populations across the study region. Our findings suggest that for arid-dwelling reptile populations, spatial and temporal dynamics are structured by abiotic events, but individual responses to covariates at smaller spatial scales are complex and poorly understood.

Status update: is smoke on your mind? Using social media to assess smoke exposure

NASA Astrophysics Data System (ADS)

Ford, Bonne; Burke, Moira; Lassman, William; Pfister, Gabriele; Pierce, Jeffrey R.

2017-06-01

Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

Spatial and temporal dynamics of multidimensional well-being, livelihoods and ecosystem services in coastal Bangladesh.

PubMed

Adams, Helen; Adger, W Neil; Ahmad, Sate; Ahmed, Ali; Begum, Dilruba; Lázár, Attila N; Matthews, Zoe; Rahman, Mohammed Mofizur; Streatfield, Peter Kim

2016-11-08

Populations in resource dependent economies gain well-being from the natural environment, in highly spatially and temporally variable patterns. To collect information on this, we designed and implemented a 1586-household quantitative survey in the southwest coastal zone of Bangladesh. Data were collected on material, subjective and health dimensions of well-being in the context of natural resource use, particularly agriculture, aquaculture, mangroves and fisheries. The questionnaire included questions on factors that mediate poverty outcomes: mobility and remittances; loans and micro-credit; environmental perceptions; shocks; and women's empowerment. The data are stratified by social-ecological system to take into account spatial dynamics and the survey was repeated with the same respondents three times within a year to incorporate seasonal dynamics. The dataset includes blood pressure measurements and height and weight of men, women and children. In addition, the household listing includes basic data on livelihoods and income for approximately 10,000 households. The dataset facilitates interdisciplinary research on spatial and temporal dynamics of well-being in the context of natural resource dependence in low income countries.

Spatial and temporal dynamics of multidimensional well-being, livelihoods and ecosystem services in coastal Bangladesh

PubMed Central

Adams, Helen; Adger, W. Neil; Ahmad, Sate; Ahmed, Ali; Begum, Dilruba; Lázár, Attila N.; Matthews, Zoe; Rahman, Mohammed Mofizur; Streatfield, Peter Kim

2016-01-01

Populations in resource dependent economies gain well-being from the natural environment, in highly spatially and temporally variable patterns. To collect information on this, we designed and implemented a 1586-household quantitative survey in the southwest coastal zone of Bangladesh. Data were collected on material, subjective and health dimensions of well-being in the context of natural resource use, particularly agriculture, aquaculture, mangroves and fisheries. The questionnaire included questions on factors that mediate poverty outcomes: mobility and remittances; loans and micro-credit; environmental perceptions; shocks; and women’s empowerment. The data are stratified by social-ecological system to take into account spatial dynamics and the survey was repeated with the same respondents three times within a year to incorporate seasonal dynamics. The dataset includes blood pressure measurements and height and weight of men, women and children. In addition, the household listing includes basic data on livelihoods and income for approximately 10,000 households. The dataset facilitates interdisciplinary research on spatial and temporal dynamics of well-being in the context of natural resource dependence in low income countries. PMID:27824340

Accurate population genetic measurements require cryptic species identification in corals

NASA Astrophysics Data System (ADS)

Sheets, Elizabeth A.; Warner, Patricia A.; Palumbi, Stephen R.

2018-06-01

Correct identification of closely related species is important for reliable measures of gene flow. Incorrectly lumping individuals of different species together has been shown to over- or underestimate population differentiation, but examples highlighting when these different results are observed in empirical datasets are rare. Using 199 single nucleotide polymorphisms, we assigned 768 individuals in the Acropora hyacinthus and A. cytherea morphospecies complexes to each of eight previously identified cryptic genetic species and measured intraspecific genetic differentiation across three geographic scales (within reefs, among reefs within an archipelago, and among Pacific archipelagos). We then compared these calculations to estimated genetic differentiation at each scale with all cryptic genetic species mixed as if we could not tell them apart. At the reef scale, correct genetic species identification yielded lower F ST estimates and fewer significant comparisons than when species were mixed, raising estimates of short-scale gene flow. In contrast, correct genetic species identification at large spatial scales yielded higher F ST measurements than mixed-species comparisons, lowering estimates of long-term gene flow among archipelagos. A meta-analysis of published population genetic studies in corals found similar results: F ST estimates at small spatial scales were lower and significance was found less often in studies that controlled for cryptic species. Our results and these prior datasets controlling for cryptic species suggest that genetic differentiation among local reefs may be lower than what has generally been reported in the literature. Not properly controlling for cryptic species structure can bias population genetic analyses in different directions across spatial scales, and this has important implications for conservation strategies that rely on these estimates.

Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, Robert M.

2012-01-01

Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model's assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models (M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects.

Communicating and Evaluating the Causes of Seismicity in Oklahoma Using ArcGIS Online Story Map Web Applications

NASA Astrophysics Data System (ADS)

Justman, D.; Rose, K.; Bauer, J. R.; Miller, R., III; Vasylkivska, V.; Romeo, L.

2016-12-01

ArcGIS Online story maps allows users to communicate complex topics with geospatially enabled stories. This story map web application entitled "Evaluating the Mysteries of Seismicity in Oklahoma" has been employed as part of a broader research effort investigating the relationships between spatiotemporal systems and seismicity to understand the recent increase in seismicity by reviewing literature, exploring, and performing analyses on key datasets. It offers information about the unprecedented increase in seismic events since 2008, earthquake history, the risk to the population, physical mechanisms behind earthquakes, natural and anthropogenic earthquake factors, and individual & cumulative spatial extents of these factors. The cumulative spatial extents for natural, anthropogenic, and all combined earthquake factors were determined using the Cumulative Spatial Impact Layers (CSILs) tool developed at the National Energy Technology Laboratory (NETL). Results show positive correlations between the average number of influences (datasets related to individual factors) and the number of earthquakes for every 100 square mile grid cell in Oklahoma, along with interesting spatial correlations for the individual & cumulative spatial extents of these factors when overlaid with earthquake density and a hotspot analysis for earthquake magnitude from 2010 to 2015.

Relationships between brightness of nighttime lights and population density

NASA Astrophysics Data System (ADS)

Naizhuo, Z.

2012-12-01

Brightness of nighttime lights has been proven to be a good proxy for socioeconomic and demographic statistics. Moreover, the satellite nighttime lights data have been used to spatially disaggregate amounts of gross domestic product (GDP), fossil fuel carbon dioxide emission, and electric power consumption (Ghosh et al., 2010; Oda and Maksyutov, 2011; Zhao et al., 2012). Spatial disaggregations were performed in these previous studies based on assumed linear relationships between digital number (DN) value of pixels in the nighttime light images and socioeconomic data. However, reliability of the linear relationships was never tested due to lack of relative high-spatial-resolution (equal to or finer than 1 km × 1 km) statistical data. With the similar assumption that brightness linearly correlates to population, Bharti et al. (2011) used nighttime light data as a proxy for population density and then developed a model about seasonal fluctuations of measles in West Africa. The Oak Ridge National Laboratory used sub-national census population data and high spatial resolution remotely-sensed-images to produce LandScan population raster datasets. The LandScan population datasets have 1 km × 1 km spatial resolution which is consistent with the spatial resolution of the nighttime light images. Therefore, in this study I selected 2008 LandScan population data as baseline reference data and the contiguous United State as study area. Relationships between DN value of pixels in the 2008 Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) stable light image and population density were established. Results showed that an exponential function can more accurately reflect the relationship between luminosity and population density than a linear function. Additionally, a certain number of saturated pixels with DN value of 63 exist in urban core areas. If directly using the exponential function to estimate the population density for the whole brightly lit area, relatively large under-estimations would emerge in the urban core regions. Previous studies have shown that GDP, carbon dioxide emission, and electric power consumption strongly correlate to urban population (Ghosh et al., 2010; Sutton et al., 2007; Zhao et al., 2012). Thus, although this study only examined the relationships between brightness of nighttime lights and population density, the results can provide insight for the spatial disaggregations of socioeconomic data (e.g. GDP, carbon dioxide emission, and electric power consumption) using the satellite nighttime light image data. Simply distributing the socioeconomic data to each pixel in proportion to the DN value of the nighttime light images may generate relatively large errors. References Bharit N, Tatem AJ, Ferrari MJ, Grais RF, Djibo A, Grenfell BT, 2011. Science, 334:1424-1427. Ghosh T, Elvidge CD, Sutton PC, Baugh KE, Ziskin D, Tuttle BT, 2010. Energies, 3:1895-1913. Oda T, Maksyutov S, 2011. Atmospheric Chemistry and Physics, 11:543-556. Sutton PC, Elvidge CD, Ghosh T, 2007. International Journal of Ecological Economics and Statistics, 8:5-21. Zhao N, Ghosh T, Samson EL, 2012. International Journal of Remote sensing, 33:6304-6320.

Insights from Modelling the Spatial Dependence Structure of Hydraulic Conductivity at the MADE Site Using Spatial Copulas

NASA Astrophysics Data System (ADS)

Haslauer, Claus; Bohling, Geoff

2013-04-01

Hydraulic conductivity (K) is a fundamental parameter that influences groundwater flow and solute transport. Measurements of K are limited and uncertain. Moreover, the spatial structure of K, which impacts the groundwater velocity field and hence directly influences the advective spreading of a solute migrating in the subsurface, is commonly described by approaches using second order moments. Spatial copulas have in the recent past been applied successfully to model the spatial dependence structure of heterogeneous subsurface datasets. At the MADE site, hydraulic conductivity (K) has been measured in exceptional detail. Two independently collected data-sets were used for this study: (1) ~2000 flowmeter based K measurements, and (2) ~20,000 direct-push based K measurements. These datasets exhibit a very heterogeneous (Var[ln(K)]>2) spatially distributed K field. A copula analysis reveals that the spatial dependence structure of the flowmeter and direct-push datasets are essentially the same. A spatial copula analysis factors out the influence of the marginal distribution of the property under investigation. This independence from the marginal distributions allows the copula analysis to reveal the underlying similarity between the spatial dependence structures of the flowmeter and direct-push datasets despite two complicating factors: 1) an overall offset between the datasets, with direct-push K values being, on average, roughly a factor of five lower than flowmeter K values, due at least in part to opposite biases between the two measurement techniques, and 2) the presence of some anomalously high K values in the direct-push dataset due to a lower limit on accurately measureable pressure responses in high-K zones. In addition, the vertical resolution of the direct-push dataset is ten times finer than that of the flowmeter dataset. Upscaling the direct-push data to compensate for this difference resulted in little change to the spatial structure. The objective of the presented work is to use multidimensional spatial copulas to describe and model the spatial dependence of the spatial structure of K at the heterogeneous MADE site, and evaluate the effects of this multidimensional description on solute transport.

Global Data Spatially Interrelate System for Scientific Big Data Spatial-Seamless Sharing

NASA Astrophysics Data System (ADS)

Yu, J.; Wu, L.; Yang, Y.; Lei, X.; He, W.

2014-04-01

A good data sharing system with spatial-seamless services will prevent the scientists from tedious, boring, and time consuming work of spatial transformation, and hence encourage the usage of the scientific data, and increase the scientific innovation. Having been adopted as the framework of Earth datasets by Group on Earth Observation (GEO), Earth System Spatial Grid (ESSG) is potential to be the spatial reference of the Earth datasets. Based on the implementation of ESSG, SDOG-ESSG, a data sharing system named global data spatially interrelate system (GASE) was design to make the data sharing spatial-seamless. The architecture of GASE was introduced. The implementation of the two key components, V-Pools, and interrelating engine, and the prototype is presented. Any dataset is firstly resampled into SDOG-ESSG, and is divided into small blocks, and then are mapped into hierarchical system of the distributed file system in V-Pools, which together makes the data serving at a uniform spatial reference and at a high efficiency. Besides, the datasets from different data centres are interrelated by the interrelating engine at the uniform spatial reference of SDOGESSG, which enables the system to sharing the open datasets in the internet spatial-seamless.

Wildlife disease ecology from the individual to the population: Insights from a long-term study of a naturally infected European badger population.

PubMed

McDonald, Jenni L; Robertson, Andrew; Silk, Matthew J

2018-01-01

Long-term individual-based datasets on host-pathogen systems are a rare and valuable resource for understanding the infectious disease dynamics in wildlife. A study of European badgers (Meles meles) naturally infected with bovine tuberculosis (bTB) at Woodchester Park in Gloucestershire (UK) has produced a unique dataset, facilitating investigation of a diverse range of epidemiological and ecological questions with implications for disease management. Since the 1970s, this badger population has been monitored with a systematic mark-recapture regime yielding a dataset of >15,000 captures of >3,000 individuals, providing detailed individual life-history, morphometric, genetic, reproductive and disease data. The annual prevalence of bTB in the Woodchester Park badger population exhibits no straightforward relationship with population density, and both the incidence and prevalence of Mycobacterium bovis show marked variation in space. The study has revealed phenotypic traits that are critical for understanding the social structure of badger populations along with mechanisms vital for understanding disease spread at different spatial resolutions. Woodchester-based studies have provided key insights into how host ecology can influence infection at different spatial and temporal scales. Specifically, it has revealed heterogeneity in epidemiological parameters; intrinsic and extrinsic factors affecting population dynamics; provided insights into senescence and individual life histories; and revealed consistent individual variation in foraging patterns, refuge use and social interactions. An improved understanding of ecological and epidemiological processes is imperative for effective disease management. Woodchester Park research has provided information of direct relevance to bTB management, and a better appreciation of the role of individual heterogeneity in disease transmission can contribute further in this regard. The Woodchester Park study system now offers a rare opportunity to seek a dynamic understanding of how individual-, group- and population-level processes interact. The wealth of existing data makes it possible to take a more integrative approach to examining how the consequences of individual heterogeneity scale to determine population-level pathogen dynamics and help advance our understanding of the ecological drivers of host-pathogen systems. © 2017 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Ontology for Transforming Geo-Spatial Data for Discovery and Integration of Scientific Data

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.

2013-12-01

Discovery and access to geo-spatial scientific data across heterogeneous repositories and multi-discipline datasets can present challenges for scientist. We propose to build a workflow for transforming geo-spatial datasets into semantic environment by using relationships to describe the resource using OWL Web Ontology, RDF, and a proposed geo-spatial vocabulary. We will present methods for transforming traditional scientific dataset, use of a semantic repository, and querying using SPARQL to integrate and access datasets. This unique repository will enable discovery of scientific data by geospatial bound or other criteria.

Spatio-temporal dynamics of a fish predator: Density-dependent and hydrographic effects on Baltic Sea cod population

PubMed Central

Bartolino, Valerio; Tian, Huidong; Bergström, Ulf; Jounela, Pekka; Aro, Eero; Dieterich, Christian; Meier, H. E. Markus; Cardinale, Massimiliano; Bland, Barbara

2017-01-01

Understanding the mechanisms of spatial population dynamics is crucial for the successful management of exploited species and ecosystems. However, the underlying mechanisms of spatial distribution are generally complex due to the concurrent forcing of both density-dependent species interactions and density-independent environmental factors. Despite the high economic value and central ecological importance of cod in the Baltic Sea, the drivers of its spatio-temporal population dynamics have not been analytically investigated so far. In this paper, we used an extensive trawl survey dataset in combination with environmental data to investigate the spatial dynamics of the distribution of the Eastern Baltic cod during the past three decades using Generalized Additive Models. The results showed that adult cod distribution was mainly affected by cod population size, and to a minor degree by small-scale hydrological factors and the extent of suitable reproductive areas. As population size decreases, the cod population concentrates to the southern part of the Baltic Sea, where the preferred more marine environment conditions are encountered. Using the fitted models, we predicted the Baltic cod distribution back to the 1970s and a temporal index of cod spatial occupation was developed. Our study will contribute to the management and conservation of this important resource and of the ecosystem where it occurs, by showing the forces shaping its spatial distribution and therefore the potential response of the population to future exploitation and environmental changes. PMID:28207804

Who, What, When, Where? Determining the Health Implications of Wildfire Smoke Exposure

NASA Astrophysics Data System (ADS)

Ford, B.; Lassman, W.; Gan, R.; Burke, M.; Pfister, G.; Magzamen, S.; Fischer, E. V.; Volckens, J.; Pierce, J. R.

2016-12-01

Exposure to poor air quality is associated with negative impacts on human health. A large natural source of PM in the western U.S. is from wildland fires. Accurately attributing health endpoints to wildland-fire smoke requires a determination of the exposed population. This is a difficult endeavor because most current methods for monitoring air quality are not at high temporal and spatial resolutions. Therefore, there is a growing effort to include multiple datasets and create blended products of smoke exposure that can exploit the strengths of each dataset. In this work, we combine model (WRF-Chem) simulations, NASA satellite (MODIS) observations, and in-situ surface monitors to improve exposure estimates. We will also introduce a social-media dataset of self-reported smoke/haze/pollution to improve population-level exposure estimates for the summer of 2015. Finally, we use these detailed exposure estimates in different epidemiologic study designs to provide an in-depth understanding of the role wildfire exposure plays on health outcomes.

Tree-based approach for exploring marine spatial patterns with raster datasets.

PubMed

Liao, Xiaohan; Xue, Cunjin; Su, Fenzhen

2017-01-01

From multiple raster datasets to spatial association patterns, the data-mining technique is divided into three subtasks, i.e., raster dataset pretreatment, mining algorithm design, and spatial pattern exploration from the mining results. Comparison with the former two subtasks reveals that the latter remains unresolved. Confronted with the interrelated marine environmental parameters, we propose a Tree-based Approach for eXploring Marine Spatial Patterns with multiple raster datasets called TAXMarSP, which includes two models. One is the Tree-based Cascading Organization Model (TCOM), and the other is the Spatial Neighborhood-based CAlculation Model (SNCAM). TCOM designs the "Spatial node→Pattern node" from top to bottom layers to store the table-formatted frequent patterns. Together with TCOM, SNCAM considers the spatial neighborhood contributions to calculate the pattern-matching degree between the specified marine parameters and the table-formatted frequent patterns and then explores the marine spatial patterns. Using the prevalent quantification Apriori algorithm and a real remote sensing dataset from January 1998 to December 2014, a successful application of TAXMarSP to marine spatial patterns in the Pacific Ocean is described, and the obtained marine spatial patterns present not only the well-known but also new patterns to Earth scientists.

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors derived are evaluated using independent high spatial resolution datasets that reveal the pattern and health of vegetation at metre scales. We also use climate variables to support the interpretation of these data. We conclude that the spatio-temporal patterns in Darfur vegetation and climate datasets suggest that labelling the conflict a climate-change conflict is inaccurate and premature.

Patterns of genetic diversity in the polymorphic ground snake (Sonora semiannulata).

PubMed

Cox, Christian L; Chippindale, Paul T

2014-08-01

We evaluated the genetic diversity of a snake species with color polymorphism to understand the evolutionary processes that drive genetic structure across a large geographic region. Specifically, we analyzed genetic structure of the highly polymorphic ground snake, Sonora semiannulata, (1) among populations, (2) among color morphs (3) at regional and local spatial scales, using an amplified fragment length polymorphism dataset and multiple population genetic analyses, including FST-based and clustering analytical techniques. Based upon these methods, we found that there was moderate to low genetic structure among populations. However, this diversity was not associated with geographic locality at either spatial scale. Similarly, we found no evidence for genetic divergence among color morphs at either spatial scale. These results suggest that despite dramatic color polymorphism, this phenotypic diversity is not a major driver of genetic diversity within or among populations of ground snakes. We suggest that there are two mechanisms that could explain existing genetic diversity in ground snakes: recent range expansion from a genetically diverse founder population and current or recent gene flow among populations. Our findings have further implications for the types of color polymorphism that may generate genetic diversity in snakes.

Sampling errors for a nadir viewing instrument on the International Space Station

NASA Astrophysics Data System (ADS)

Berger, H. I.; Pincus, R.; Evans, F.; Santek, D.; Ackerman, S.; Ackerman, S.

2001-12-01

In an effort to improve the observational charactarization of ice clouds in the earth's atmosphere, we are developing a sub-millimeter wavelength radiometer which we propose to fly on the International Space Station for two years. Our goal is to accurately measure the ice water path and mass-weighted particle size at the finest possible temporal and spatial resolution. The ISS orbit precesses, sampling through the dirunal cycle every 16 days, but technological constraints limit our instrument to a single pixel viewed near nadir. We discuss sampling errors associated with this instrument/platform configuration. We use as "truth" the ISCCP dataset of pixel-level cloud optical retrievals, which acts as a proxy for ice water path; this dataset is sampled according to the orbital characteristics of the space station, and the statistics computed from the sub-sampled population are compared with those from the full dataset. We explore the tradeoffs in average sampling error as a function of the averaging time and spatial scale, and explore the possibility of resolving the dirunal cycle.

Phylogenetic congruence of lichenised fungi and algae is affected by spatial scale and taxonomic diversity.

PubMed

Buckley, Hannah L; Rafat, Arash; Ridden, Johnathon D; Cruickshank, Robert H; Ridgway, Hayley J; Paterson, Adrian M

2014-01-01

The role of species' interactions in structuring biological communities remains unclear. Mutualistic symbioses, involving close positive interactions between two distinct organismal lineages, provide an excellent means to explore the roles of both evolutionary and ecological processes in determining how positive interactions affect community structure. In this study, we investigate patterns of co-diversification between fungi and algae for a range of New Zealand lichens at the community, genus, and species levels and explore explanations for possible patterns related to spatial scale and pattern, taxonomic diversity of the lichens considered, and the level sampling replication. We assembled six independent datasets to compare patterns in phylogenetic congruence with varied spatial extent of sampling, taxonomic diversity and level of specimen replication. For each dataset, we used the DNA sequences from the ITS regions of both the fungal and algal genomes from lichen specimens to produce genetic distance matrices. Phylogenetic congruence between fungi and algae was quantified using distance-based redundancy analysis and we used geographic distance matrices in Moran's eigenvector mapping and variance partitioning to evaluate the effects of spatial variation on the quantification of phylogenetic congruence. Phylogenetic congruence was highly significant for all datasets and a large proportion of variance in both algal and fungal genetic distances was explained by partner genetic variation. Spatial variables, primarily at large and intermediate scales, were also important for explaining genetic diversity patterns in all datasets. Interestingly, spatial structuring was stronger for fungal than algal genetic variation. As the spatial extent of the samples increased, so too did the proportion of explained variation that was shared between the spatial variables and the partners' genetic variation. Different lichen taxa showed some variation in their phylogenetic congruence and spatial genetic patterns and where greater sample replication was used, the amount of variation explained by partner genetic variation increased. Our results suggest that the phylogenetic congruence pattern, at least at small spatial scales, is likely due to reciprocal co-adaptation or co-dispersal. However, the detection of these patterns varies among different lichen taxa, across spatial scales and with different levels of sample replication. This work provides insight into the complexities faced in determining how evolutionary and ecological processes may interact to generate diversity in symbiotic association patterns at the population and community levels. Further, it highlights the critical importance of considering sample replication, taxonomic diversity and spatial scale in designing studies of co-diversification.

Within-population spatial synchrony in mast seeding of North American oaks.

Treesearch

A.V. Liebhold; M. Sork; O.N. Peltonen; Westfall R. Bjørnstad; J. Elkinton; M. H. J. Knops

2004-01-01

Mast seeding, the synchronous production of large crops of seeds, has been frequently documented in oak species. In this study we used several North American oak data-sets to quantify within-stand (10 km) synchrony in mast dynamics. Results indicated that intraspecific synchrony in seed production always exceeded interspecific synchrony and was essentially constant...

Summary of current knowledge of the size and spatial distribution of the horse population within Great Britain

PubMed Central

2012-01-01

Background Robust demographic information is important to understanding the risk of introduction and spread of exotic diseases as well as the development of effective disease control strategies, but is often based on datasets collected for other purposes. Thus, it is important to validate, or at least cross-reference these datasets to other sources to assess whether they are being used appropriately. The aim of this study was to use horse location data collected from different contributing industry sectors ("Stakeholder horse data") to calibrate the spatial distribution of horses as indicated by owner locations registered in the National Equine Database (the NED). Results A conservative estimate for the accurately geo-located NED horse population within GB is approximately 840,000 horses. This is likely to be an underestimate because of the exclusion of horses due to age or location criteria. In both datasets, horse density was higher in England and Wales than in Scotland. The high density of horses located in urban areas as indicated in the NED is consistent with previous reports indicating that owner location cannot always be viewed as a direct substitute for horse location. Otherwise, at a regional resolution, there are few differences between the datasets. There are inevitable biases in the stakeholder data, and leisure horses that are unaffiliated to major stakeholders are not included in these data. Despite this, the similarity in distributions of these datasets is re-assuring, suggesting that there are few regional biases in the NED. Conclusions Our analyses suggest that stakeholder data could be used to monitor possible changes in horse demographics. Given such changes in horse demographics and the advantages of stakeholder data (which include annual updates and accurate horse location), it may be appropriate to use these data for future disease modelling in conjunction with, if not in place of the NED. PMID:22475060

A comparison of two global datasets of extreme sea levels and resulting flood exposure

NASA Astrophysics Data System (ADS)

Muis, Sanne; Verlaan, Martin; Nicholls, Robert J.; Brown, Sally; Hinkel, Jochen; Lincke, Daniel; Vafeidis, Athanasios T.; Scussolini, Paolo; Winsemius, Hessel C.; Ward, Philip J.

2017-04-01

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

A Geospatial Database that Supports Derivation of Climatological Features of Severe Weather

NASA Astrophysics Data System (ADS)

Phillips, M.; Ansari, S.; Del Greco, S.

2007-12-01

The Severe Weather Data Inventory (SWDI) at NOAA's National Climatic Data Center (NCDC) provides user access to archives of several datasets critical to the detection and evaluation of severe weather. These datasets include archives of: · NEXRAD Level-III point features describing general storm structure, hail, mesocyclone and tornado signatures · National Weather Service Storm Events Database · National Weather Service Local Storm Reports collected from storm spotters · National Weather Service Warnings · Lightning strikes from Vaisala's National Lightning Detection Network (NLDN) SWDI archives all of these datasets in a spatial database that allows for convenient searching and subsetting. These data are accessible via the NCDC web site, Web Feature Services (WFS) or automated web services. The results of interactive web page queries may be saved in a variety of formats, including plain text, XML, Google Earth's KMZ, standards-based NetCDF and Shapefile. NCDC's Storm Risk Assessment Project (SRAP) uses data from the SWDI database to derive gridded climatology products that show the spatial distributions of the frequency of various events. SRAP also can relate SWDI events to other spatial data such as roads, population, watersheds, and other geographic, sociological, or economic data to derive products that are useful in municipal planning, emergency management, the insurance industry, and other areas where there is a need to quantify and qualify how severe weather patterns affect people and property.

Exploring the potential of geocoding the impact of disasters: The experience of global and national databases

NASA Astrophysics Data System (ADS)

Guha-Sapir, Debarati; Davis, Rhonda; Gall, Melanie; Wallemacq, Pascaline; Cutter, Susan

2015-04-01

As extreme climate events such as precipitation driven flooding, storms and droughts are increasingly devastating, assessing impacts accurately becomes critically important in guiding decisions and investments on disaster risk reduction. Capturing disaster impacts includes not only quantitative information such as the economic and human effects but also the determination of where and when the impacts occurred. Among the most commonly used impact indicators are the number of deaths and the number of people affected or homeless, and the economic damages. Unfortunately, these figures are typically used in their raw form and conclusions are drawn without due consideration to denominators. For example, key parameters such as the population base or the size of the region affected are often not factored in when judging the severity of the event or calculating increases or decreases in an indicator. To increase the meaningfulness and comparability of disaster impacts across time and space, however, it is important to mathematically standardize indicators and utilize common denominators such as number of population exposed, area affected, GDP, and so forth. Geospatial techniques such as geo-referencing and spatial overlays are coming into greater use to facilitate this process. In 2013, EM-DAT, one of the main providers of global disaster impact data, launched an effort to enhance its contents through spatial analyses. The challenge was to develop a sustainable methodology and protocol for a large dataset and to systematically collect and enter geocoded profiles for each event that is registered in EM-DAT. Along with specialists in geography from different institutions EM-DAT launched an effort to geocode each disaster event working backwards in time starting from the most recent. For geo-referencing purposes, EM-DAT requires a standardized dataset of sub-national administrative boundaries. Though a number of such initiatives exist, the Food and Agriculture Organization's (FAO) Global Administrative Unit Layers (GAUL) was selected as the most appropriate since the data are updated annually, disputed areas are labelled as such and not assigned to a national entity, and the FAO uses a community-based approach whereby users of the dataset can provide updated administrative boundaries and related shapefiles. Geocoding the impact areas of disaster events not only allows for more accurate spatial analyses and mapping but it also enhances the interoperability of EM-DAT data with other spatially explicit data (such as population or land use data), or with nationally-developed loss datasets such as SHELDUS. Most importantly, geocoding permits the monitoring of key parameters of disaster impacts such as exposure, vulnerable populations, effectiveness of disaster risk reduction measures, as well as the investigation of linkages and ripple effects between a catastrophic event and other external factors. For example, the intersection between extreme malnutrition and the spatial extent of droughts or floods aids in the identification of hot spots and facilitates strategic delivery of nutrition interventions. With the need for tracking and monitoring progress towards sustainable development and disaster risk reduction gaining in importance, the ability to express disaster impacts in standardized terms such as ratios or percentages per some unit area will increase transparency and comparability of disaster management programmes.

Global distribution of urban parameters derived from high-resolution global datasets for weather modelling

NASA Astrophysics Data System (ADS)

Kawano, N.; Varquez, A. C. G.; Dong, Y.; Kanda, M.

2016-12-01

Numerical model such as Weather Research and Forecasting model coupled with single-layer Urban Canopy Model (WRF-UCM) is one of the powerful tools to investigate urban heat island. Urban parameters such as average building height (Have), plain area index (λp) and frontal area index (λf), are necessary inputs for the model. In general, these parameters are uniformly assumed in WRF-UCM but this leads to unrealistic urban representation. Distributed urban parameters can also be incorporated into WRF-UCM to consider a detail urban effect. The problem is that distributed building information is not readily available for most megacities especially in developing countries. Furthermore, acquiring real building parameters often require huge amount of time and money. In this study, we investigated the potential of using globally available satellite-captured datasets for the estimation of the parameters, Have, λp, and λf. Global datasets comprised of high spatial resolution population dataset (LandScan by Oak Ridge National Laboratory), nighttime lights (NOAA), and vegetation fraction (NASA). True samples of Have, λp, and λf were acquired from actual building footprints from satellite images and 3D building database of Tokyo, New York, Paris, Melbourne, Istanbul, Jakarta and so on. Regression equations were then derived from the block-averaging of spatial pairs of real parameters and global datasets. Results show that two regression curves to estimate Have and λf from the combination of population and nightlight are necessary depending on the city's level of development. An index which can be used to decide which equation to use for a city is the Gross Domestic Product (GDP). On the other hand, λphas less dependence on GDP but indicated a negative relationship to vegetation fraction. Finally, a simplified but precise approximation of urban parameters through readily-available, high-resolution global datasets and our derived regressions can be utilized to estimate a global distribution of urban parameters for later incorporation into a weather model, thus allowing us to acquire a global understanding of urban climate (Global Urban Climatology). Acknowledgment: This research was supported by the Environment Research and Technology Development Fund (S-14) of the Ministry of the Environment, Japan.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015.

PubMed

Abatzoglou, John T; Dobrowski, Solomon Z; Parks, Sean A; Hegewisch, Katherine C

2018-01-09

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

NASA Astrophysics Data System (ADS)

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

A new urban landscape in East-Southeast Asia, 2000-2010

NASA Astrophysics Data System (ADS)

Schneider, A.; Mertes, C. M.; Tatem, A. J.; Tan, B.; Sulla-Menashe, D.; Graves, S. J.; Patel, N. N.; Horton, J. A.; Gaughan, A. E.; Rollo, J. T.; Schelly, I. H.; Stevens, F. R.; Dastur, A.

2015-03-01

East-Southeast Asia is currently one of the fastest urbanizing regions in the world, with countries such as China climbing from 20 to 50% urbanized in just a few decades. By 2050, these countries are projected to add 1 billion people, with 90% of that growth occurring in cities. This population shift parallels an equally astounding amount of built-up land expansion. However, spatially-and temporally-detailed information on regional-scale changes in urban land or population distribution do not exist; previous efforts have been either sample-based, focused on one country, or drawn conclusions from datasets with substantial temporal/spatial mismatch and variability in urban definitions. Using consistent methodology, satellite imagery and census data for >1000 agglomerations in the East-Southeast Asian region, we show that urban land increased >22% between 2000 and 2010 (from 155 000 to 189 000 km2), an amount equivalent to the area of Taiwan, while urban populations climbed >31% (from 738 to 969 million). Although urban land expanded at unprecedented rates, urban populations grew more rapidly, resulting in increasing densities for the majority of urban agglomerations, including those in both more developed (Japan, South Korea) and industrializing nations (China, Vietnam, Indonesia). This result contrasts previous sample-based studies, which conclude that cities are universally declining in density. The patterns and rates of change uncovered by these datasets provide a unique record of the massive urban transition currently underway in East-Southeast Asia that is impacting local-regional climate, pollution levels, water quality/availability, arable land, as well as the livelihoods and vulnerability of populations in the region.

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

PubMed Central

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast. PMID:22205868

Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor: LTDR, GIMMS, FASIR, and PAL-II.

PubMed

Alcaraz-Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José; Cabello, Javier

2010-01-01

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations' carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982-1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.

Framework for National Flood Risk Assessment for Canada

NASA Astrophysics Data System (ADS)

Elshorbagy, A. A.; Raja, B.; Lakhanpal, A.; Razavi, S.; Ceola, S.; Montanari, A.

2016-12-01

Worldwide, floods have been identified as a standout amongst the most widely recognized catastrophic events, resulting in the loss of life and property. These natural hazards cannot be avoided, but their consequences can certainly be reduced by having prior knowledge of their occurrence and impact. In the context of floods, the terms occurrence and impact are substituted by flood hazard and flood vulnerability, respectively, which collectively define the flood risk. There is a high need for identifying the flood-prone areas and to quantify the risk associated with them. The present study aims at delivering flood risk maps, which prioritize the potential flood risk areas in Canada. The methodology adopted in this study involves integrating various available spatial datasets such as nightlights satellite imagery, land use, population and the digital elevation model, to build a flexible framework for national flood risk assessment for Canada. The flood risk framework assists in identifying the flood-prone areas and evaluating the associated risk. All these spatial datasets were brought to a common GIS platform for flood risk analysis. The spatial datasets deliver the socioeconomic and topographical information that is required for evaluating the flood vulnerability and flood hazard, respectively. Nightlights have been investigated as a tool to be used as a proxy for the human activities to identify areas with regard to economic investment. However, other datasets, including existing flood protection measures, we added to identify a realistic flood assessment framework. Furthermore, the city of Calgary was used as an example to investigate the effect of using Digital Elevation Models (DEMs) of varying resolutions on risk maps. Along with this, the risk map for the city was further enhanced by including the population data to give a social dimension to the risk map. Flood protection measures play a major role by significantly reducing the flood risk of events with a specific return period. An analysis to update the risk maps when information on protection measures is available was carried out for the city of Winnipeg, Canada. The proposed framework is a promising approach to identify and prioritize flood-prone areas, which are in need of intervention or detailed studies.

A Compilation of Spatial Datasets to Support a Preliminary Assessment of Pesticides and Pesticide Use on Tribal Lands in Oklahoma

USGS Publications Warehouse

Mashburn, Shana L.; Winton, Kimberly T.

2010-01-01

This CD-ROM contains spatial datasets that describe natural and anthropogenic features and county-level estimates of agricultural pesticide use and pesticide data for surface-water, groundwater, and biological specimens in the state of Oklahoma. County-level estimates of pesticide use were compiled from the Pesticide National Synthesis Project of the U.S. Geological Survey, National Water-Quality Assessment Program. Pesticide data for surface water, groundwater, and biological specimens were compiled from U.S. Geological Survey National Water Information System database. These spatial datasets that describe natural and manmade features were compiled from several agencies and contain information collected by the U.S. Geological Survey. The U.S. Geological Survey datasets were not collected specifically for this compilation, but were previously collected for projects with various objectives. The spatial datasets were created by different agencies from sources with varied quality. As a result, features common to multiple layers may not overlay exactly. Users should check the metadata to determine proper use of these spatial datasets. These data were not checked for accuracy or completeness. If a question of accuracy or completeness arise, the user should contact the originator cited in the metadata.

Statistical and Spatial Analysis of Bathymetric Data for the St. Clair River, 1971-2007

USGS Publications Warehouse

Bennion, David

2009-01-01

To address questions concerning ongoing geomorphic processes in the St. Clair River, selected bathymetric datasets spanning 36 years were analyzed. Comparisons of recent high-resolution datasets covering the upper river indicate a highly variable, active environment. Although statistical and spatial comparisons of the datasets show that some changes to the channel size and shape have taken place during the study period, uncertainty associated with various survey methods and interpolation processes limit the statistically certain results. The methods used to spatially compare the datasets are sensitive to small variations in position and depth that are within the range of uncertainty associated with the datasets. Characteristics of the data, such as the density of measured points and the range of values surveyed, can also influence the results of spatial comparison. With due consideration of these limitations, apparently active and ongoing areas of elevation change in the river are mapped and discussed.

Hierarchical population monitoring of greater sage-grouse (Centrocercus urophasianus) in Nevada and California—Identifying populations for management at the appropriate spatial scale

USGS Publications Warehouse

Coates, Peter S.; Prochazka, Brian G.; Ricca, Mark A.; Wann, Gregory T.; Aldridge, Cameron L.; Hanser, Steven E.; Doherty, Kevin E.; O'Donnell, Michael S.; Edmunds, David R.; Espinosa, Shawn P.

2017-08-10

Population ecologists have long recognized the importance of ecological scale in understanding processes that guide observed demographic patterns for wildlife species. However, directly incorporating spatial and temporal scale into monitoring strategies that detect whether trajectories are driven by local or regional factors is challenging and rarely implemented. Identifying the appropriate scale is critical to the development of management actions that can attenuate or reverse population declines. We describe a novel example of a monitoring framework for estimating annual rates of population change for greater sage-grouse (Centrocercus urophasianus) within a hierarchical and spatially nested structure. Specifically, we conducted Bayesian analyses on a 17-year dataset (2000–2016) of lek counts in Nevada and northeastern California to estimate annual rates of population change, and compared trends across nested spatial scales. We identified leks and larger scale populations in immediate need of management, based on the occurrence of two criteria: (1) crossing of a destabilizing threshold designed to identify significant rates of population decline at a particular nested scale; and (2) crossing of decoupling thresholds designed to identify rates of population decline at smaller scales that decouple from rates of population change at a larger spatial scale. This approach establishes how declines affected by local disturbances can be separated from those operating at larger scales (for example, broad-scale wildfire and region-wide drought). Given the threshold output from our analysis, this adaptive management framework can be implemented readily and annually to facilitate responsive and effective actions for sage-grouse populations in the Great Basin. The rules of the framework can also be modified to identify populations responding positively to management action or demonstrating strong resilience to disturbance. Similar hierarchical approaches might be beneficial for other species occupying landscapes with heterogeneous disturbance and climatic regimes.

Finding Spatio-Temporal Patterns in Large Sensor Datasets

ERIC Educational Resources Information Center

McGuire, Michael Patrick

2010-01-01

Spatial or temporal data mining tasks are performed in the context of the relevant space, defined by a spatial neighborhood, and the relevant time period, defined by a specific time interval. Furthermore, when mining large spatio-temporal datasets, interesting patterns typically emerge where the dataset is most dynamic. This dissertation is…

Recruitment synchrony of yellow perch (Perca flavescens, Percidae) in the Great Lakes region, 1966–2008

USGS Publications Warehouse

Honsey, Andrew E.; Bunnell, David B.; Troy, Cary D.; Fielder, David G.; Thomas, Michael V.; Knight, Carey T.; Chong, Stephen; Hook, Tomas O.

2016-01-01

Population-level reproductive success (recruitment) of many fish populations is characterized by high inter-annual variation and related to annual variation in key environmental factors (e.g., climate). When such environmental factors are annually correlated across broad spatial scales, spatially separated populations may display recruitment synchrony (i.e., the Moran effect). We investigated inter-annual (1966–2008) variation in yellow perch (Perca flavescens, Percidae) recruitment using 16 datasets describing populations located in four of the five Laurentian Great Lakes (Erie, Huron, Michigan, and Ontario) and Lake St. Clair. We indexed relative year class strength using catch-curve residuals for each year-class across 2–4 years and compared relative year-class strength among sampling locations. Results indicate that perch recruitment is positively synchronized across the region. In addition, the spatial scale of this synchrony appears to be broader than previous estimates for both yellow perch and freshwater fish in general. To investigate potential factors influencing relative year-class strength, we related year-class strength to regional indices of annual climatic conditions (spring-summer air temperature, winter air temperature, and spring precipitation) using data from 14 weather stations across the Great Lakes region. We found that mean spring-summer temperature is significantly positively related to recruitment success among Great Lakes yellow perch populations.

Research on Grid Size Suitability of Gridded Population Distribution in Urban Area: A Case Study in Urban Area of Xuanzhou District, China.

PubMed

Dong, Nan; Yang, Xiaohuan; Cai, Hongyan; Xu, Fengjiao

2017-01-01

The research on the grid size suitability is important to provide improvement in accuracies of gridded population distribution. It contributes to reveal the actual spatial distribution of population. However, currently little research has been done in this area. Many well-modeled gridded population dataset are basically built at a single grid scale. If the grid cell size is not appropriate, it will result in spatial information loss or data redundancy. Therefore, in order to capture the desired spatial variation of population within the area of interest, it is necessary to conduct research on grid size suitability. This study summarized three expressed levels to analyze grid size suitability, which include location expressed level, numeric information expressed level, and spatial relationship expressed level. This study elaborated the reasons for choosing the five indexes to explore expression suitability. These five indexes are consistency measure, shape index rate, standard deviation of population density, patches diversity index, and the average local variance. The suitable grid size was determined by constructing grid size-indicator value curves and suitable grid size scheme. Results revealed that the three expressed levels on 10m grid scale are satisfying. And the population distribution raster data with 10m grid size provide excellent accuracy without loss. The 10m grid size is recommended as the appropriate scale for generating a high-quality gridded population distribution in our study area. Based on this preliminary study, it indicates the five indexes are coordinated with each other and reasonable and effective to assess grid size suitability. We also suggest choosing these five indexes in three perspectives of expressed level to carry out the research on grid size suitability of gridded population distribution.

Research on Grid Size Suitability of Gridded Population Distribution in Urban Area: A Case Study in Urban Area of Xuanzhou District, China

PubMed Central

Dong, Nan; Yang, Xiaohuan; Cai, Hongyan; Xu, Fengjiao

2017-01-01

The research on the grid size suitability is important to provide improvement in accuracies of gridded population distribution. It contributes to reveal the actual spatial distribution of population. However, currently little research has been done in this area. Many well-modeled gridded population dataset are basically built at a single grid scale. If the grid cell size is not appropriate, it will result in spatial information loss or data redundancy. Therefore, in order to capture the desired spatial variation of population within the area of interest, it is necessary to conduct research on grid size suitability. This study summarized three expressed levels to analyze grid size suitability, which include location expressed level, numeric information expressed level, and spatial relationship expressed level. This study elaborated the reasons for choosing the five indexes to explore expression suitability. These five indexes are consistency measure, shape index rate, standard deviation of population density, patches diversity index, and the average local variance. The suitable grid size was determined by constructing grid size-indicator value curves and suitable grid size scheme. Results revealed that the three expressed levels on 10m grid scale are satisfying. And the population distribution raster data with 10m grid size provide excellent accuracy without loss. The 10m grid size is recommended as the appropriate scale for generating a high-quality gridded population distribution in our study area. Based on this preliminary study, it indicates the five indexes are coordinated with each other and reasonable and effective to assess grid size suitability. We also suggest choosing these five indexes in three perspectives of expressed level to carry out the research on grid size suitability of gridded population distribution. PMID:28122050

Functional CAR models for large spatially correlated functional datasets.

PubMed

Zhang, Lin; Baladandayuthapani, Veerabhadran; Zhu, Hongxiao; Baggerly, Keith A; Majewski, Tadeusz; Czerniak, Bogdan A; Morris, Jeffrey S

2016-01-01

We develop a functional conditional autoregressive (CAR) model for spatially correlated data for which functions are collected on areal units of a lattice. Our model performs functional response regression while accounting for spatial correlations with potentially nonseparable and nonstationary covariance structure, in both the space and functional domains. We show theoretically that our construction leads to a CAR model at each functional location, with spatial covariance parameters varying and borrowing strength across the functional domain. Using basis transformation strategies, the nonseparable spatial-functional model is computationally scalable to enormous functional datasets, generalizable to different basis functions, and can be used on functions defined on higher dimensional domains such as images. Through simulation studies, we demonstrate that accounting for the spatial correlation in our modeling leads to improved functional regression performance. Applied to a high-throughput spatially correlated copy number dataset, the model identifies genetic markers not identified by comparable methods that ignore spatial correlations.

Investigating Bacterial-Animal Symbioses with Light Sheet Microscopy

PubMed Central

Taormina, Michael J.; Jemielita, Matthew; Stephens, W. Zac; Burns, Adam R.; Troll, Joshua V.; Parthasarathy, Raghuveer; Guillemin, Karen

2014-01-01

SUMMARY Microbial colonization of the digestive tract is a crucial event in vertebrate development, required for maturation of host immunity and establishment of normal digestive physiology. Advances in genomic, proteomic, and metabolomic technologies are providing a more detailed picture of the constituents of the intestinal habitat, but these approaches lack the spatial and temporal resolution needed to characterize the assembly and dynamics of microbial communities in this complex environment. We report the use of light sheet microscopy to provide high resolution imaging of bacterial colonization of the zebrafish intestine. The methodology allows us to characterize bacterial population dynamics across the entire organ and the behaviors of individual bacterial and host cells throughout the colonization process. The large four-dimensional datasets generated by these imaging approaches require new strategies for image analysis. When integrated with other “omics” datasets, information about the spatial and temporal dynamics of microbial cells within the vertebrate intestine will provide new mechanistic insights into how microbial communities assemble and function within hosts. PMID:22983029

Geospatial data to support analysis of water-quality conditions in basin-fill aquifers in the southwestern United States

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2009-01-01

The Southwest Principal Aquifers study area consists of most of California and Nevada and parts of Utah, Arizona, New Mexico, and Colorado; it is about 409,000 square miles. The Basin-fill aquifers extend through about 201,000 square miles of the study area and are the primary source of water for cities and agricultural communities in basins in the arid and semiarid southwestern United States (Southwest). The demand on limited ground-water resources in areas in the southwestern United States has increased significantly. This increased demand underscores the importance of understanding factors that affect the water quality in basin-fill aquifers in the region, which are being studied through the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) program. As a part of this study, spatial datasets of natural and anthropogenic factors that may affect ground-water quality of the basin-fill aquifers in the southwestern United States were developed. These data include physical characteristics of the region, such as geology, elevation, and precipitation, as well as anthropogenic factors, including population, land use, and water use. Spatial statistics for the alluvial basins in the Southwest have been calculated using the datasets. This information provides a foundation for the development of conceptual and statistical models that relate natural and anthropogenic factors to ground-water quality across the Southwest. A geographic information system (GIS) was used to determine and illustrate the spatial distribution of these basin-fill variables across the region. One hundred-meter resolution raster data layers that represent the spatial characteristics of the basins' boundaries, drainage areas, population densities, land use, and water use were developed for the entire Southwest.

Spatial variations and determinants of infant and under-five mortality in Bangladesh.

PubMed

Gruebner, Oliver; Khan, Mmh; Burkart, Katrin; Lautenbach, Sven; Lakes, Tobia; Krämer, Alexander; Subramanian, S V; Galea, Sandro

2017-09-01

Reducing child mortality is a Sustainable Development Goal yet to be achieved by many low-income countries. We applied a subnational and spatial approach based on publicly available datasets and identified permanent insolvency, urbanicity, and malaria endemicity as factors associated with child mortality. We further detected spatial clusters in the east of Bangladesh and noted Sylhet and Jamalpur as those districts that need immediate attention to reduce child mortality. Our approach is transferable to other regions in comparable settings worldwide and may guide future studies to identify subnational regions in need for public health attention. Our study adds to our understanding where we may intervene to more effectively improve health, particularly among disadvantaged populations. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687

Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data

USGS Publications Warehouse

Dorazio, Robert M.

2013-01-01

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar – and often identical – inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

Bayes and empirical Bayes estimators of abundance and density from spatial capture-recapture data.

PubMed

Dorazio, Robert M

2013-01-01

In capture-recapture and mark-resight surveys, movements of individuals both within and between sampling periods can alter the susceptibility of individuals to detection over the region of sampling. In these circumstances spatially explicit capture-recapture (SECR) models, which incorporate the observed locations of individuals, allow population density and abundance to be estimated while accounting for differences in detectability of individuals. In this paper I propose two Bayesian SECR models, one for the analysis of recaptures observed in trapping arrays and another for the analysis of recaptures observed in area searches. In formulating these models I used distinct submodels to specify the distribution of individual home-range centers and the observable recaptures associated with these individuals. This separation of ecological and observational processes allowed me to derive a formal connection between Bayes and empirical Bayes estimators of population abundance that has not been established previously. I showed that this connection applies to every Poisson point-process model of SECR data and provides theoretical support for a previously proposed estimator of abundance based on recaptures in trapping arrays. To illustrate results of both classical and Bayesian methods of analysis, I compared Bayes and empirical Bayes esimates of abundance and density using recaptures from simulated and real populations of animals. Real populations included two iconic datasets: recaptures of tigers detected in camera-trap surveys and recaptures of lizards detected in area-search surveys. In the datasets I analyzed, classical and Bayesian methods provided similar - and often identical - inferences, which is not surprising given the sample sizes and the noninformative priors used in the analyses.

Leveling data in geochemical mapping: scope of application, pros and cons of existing methods

NASA Astrophysics Data System (ADS)

Pereira, Benoît; Vandeuren, Aubry; Sonnet, Philippe

2017-04-01

Geochemical mapping successfully met a range of needs from mineral exploration to environmental management. In Europe and around the world numerous geochemical datasets already exist. These datasets may originate from geochemical mapping projects or from the collection of sample analyses requested by environmental protection regulatory bodies. Combining datasets can be highly beneficial for establishing geochemical maps with increased resolution and/or coverage area. However this practice requires assessing the equivalence between datasets and, if needed, applying data leveling to remove possible biases between datasets. In the literature, several procedures for assessing dataset equivalence and leveling data are proposed. Daneshfar & Cameron (1998) proposed a method for the leveling of two adjacent datasets while Pereira et al. (2016) proposed two methods for the leveling of datasets that contain records located within the same geographical area. Each discussed method requires its own set of assumptions (underlying populations of data, spatial distribution of data, etc.). Here we propose to discuss the scope of application, pros, cons and practical recommendations for each method. This work is illustrated with several case studies in Wallonia (Southern Belgium) and in Europe involving trace element geochemical datasets. References: Daneshfar, B. & Cameron, E. (1998), Leveling geochemical data between map sheets, Journal of Geochemical Exploration 63(3), 189-201. Pereira, B.; Vandeuren, A.; Govaerts, B. B. & Sonnet, P. (2016), Assessing dataset equivalence and leveling data in geochemical mapping, Journal of Geochemical Exploration 168, 36-48.

Zebra Crossing Spotter: Automatic Population of Spatial Databases for Increased Safety of Blind Travelers

PubMed Central

Ahmetovic, Dragan; Manduchi, Roberto; Coughlan, James M.; Mascetti, Sergio

2016-01-01

In this paper we propose a computer vision-based technique that mines existing spatial image databases for discovery of zebra crosswalks in urban settings. Knowing the location of crosswalks is critical for a blind person planning a trip that includes street crossing. By augmenting existing spatial databases (such as Google Maps or OpenStreetMap) with this information, a blind traveler may make more informed routing decisions, resulting in greater safety during independent travel. Our algorithm first searches for zebra crosswalks in satellite images; all candidates thus found are validated against spatially registered Google Street View images. This cascaded approach enables fast and reliable discovery and localization of zebra crosswalks in large image datasets. While fully automatic, our algorithm could also be complemented by a final crowdsourcing validation stage for increased accuracy. PMID:26824080

Consolidating Data of Global Urban Populations: a Comparative Approach

NASA Astrophysics Data System (ADS)

Blankespoor, B.; Khan, A.; Selod, H.

2017-12-01

Global data on city populations are essential for the study of urbanization, city growth and the spatial distribution of human settlements. Such data are either gathered by combining official estimates of urban populations from across countries or extracted from gridded population models that combine these estimates with geospatial data. These data sources provide varying estimates of urban populations and each approach has its advantages and limitations. In particular, official figures suffer from a lack of consistency in defining urban units (across both space and time) and often provide data for jurisdictions rather than the functionally meaningful urban area. On the other hand, gridded population models require a user-imposed definition to identify urban areas and are constrained by the modelling techniques and input data employed. To address these drawbacks, we combine these approaches by consolidating information from three established sources: (i) the Citypopulation.de (Brinkhoff, 2016); (ii) the World Urban Prospects data (United Nations, 2014); and (iii) the Global Human Settlements population grid (GHS-POP) (EC - JRC, 2015). We create urban footprints with GHS-POP and spatially merge georeferenced city points from both UN WUP and Citypopulation.de with these urban footprints to identify city points that belong to a single agglomeration. We create a consolidated dataset by combining population data from the UN WUP and Citypopulation.de. The flexible framework outlined can incorporate information from alternative inputs to identify urban clusters e.g. by using night-time lights, built-up area or alternative gridded population models (e.g WorldPop or Landscan) and the parameters employed (e.g. density thresholds for urban footprints) may also be adjusted, e.g., as a function of city-specific characteristics. Our consolidated dataset provides a wider and more accurate coverage of city populations to support studies of urbanization. We apply the data to re-examine Zipf's Law. Brinkhoff, Thomas. 2016. City Population.EC - JRC; Columbia University, CIESIN. 2015. GHS population grid, derived from GPW4, multi-temporal (1975, 1990, 2000, 2015).United Nations, Department of Economic and Social Affairs, Population Division. 2014. World Urbanization Prospects: 2014 Revision.

a Comparative Analysis of Five Cropland Datasets in Africa

NASA Astrophysics Data System (ADS)

Wei, Y.; Lu, M.; Wu, W.

2018-04-01

The food security, particularly in Africa, is a challenge to be resolved. The cropland area and spatial distribution obtained from remote sensing imagery are vital information. In this paper, according to cropland area and spatial location, we compare five global cropland datasets including CCI Land Cover, GlobCover, MODIS Collection 5, GlobeLand30 and Unified Cropland in circa 2010 of Africa in terms of cropland area and spatial location. The accuracy of cropland area calculated from five datasets was analyzed compared with statistic data. Based on validation samples, the accuracies of spatial location for the five cropland products were assessed by error matrix. The results show that GlobeLand30 has the best fitness with the statistics, followed by MODIS Collection 5 and Unified Cropland, GlobCover and CCI Land Cover have the lower accuracies. For the accuracy of spatial location of cropland, GlobeLand30 reaches the highest accuracy, followed by Unified Cropland, MODIS Collection 5 and GlobCover, CCI Land Cover has the lowest accuracy. The spatial location accuracy of five datasets in the Csa with suitable farming condition is generally higher than in the Bsk.

Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

NASA Astrophysics Data System (ADS)

Geng, Guannan; Zhang, Qiang; Martin, Randall V.; Lin, Jintai; Huo, Hong; Zheng, Bo; Wang, Siwen; He, Kebin

2017-03-01

Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope = 1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Granier, Claire; Liousse, Catherine; De Graaf, Erica; Enriquez, Edgar; Boulanger, Damien; Brissebrat, Guillaume

2017-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Access to Emissions Distributions and Related Ancillary Data through the ECCAD database

NASA Astrophysics Data System (ADS)

Darras, Sabine; Enriquez, Edgar; Granier, Claire; Liousse, Catherine; Boulanger, Damien; Fontaine, Alain

2016-04-01

The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Indigenous migration dynamics in the Ecuadorian Amazon: a longitudinal and hierarchical analysis.

PubMed

Davis, Jason; Sellers, Samuel; Gray, Clark; Bilsborrow, Richard

2017-01-01

Amazonian indigenous populations are approaching a critical stage in their history in which increasing education and market integration, rapid population growth and degradation of natural resources threaten the survival of their traditions and livelihoods. A topic that has hardly been touched upon in this context is migration and population mobility. We address this by analysing a unique longitudinal dataset from the Ecuadorian Amazon on the spatial mobility of five indigenous groups and mestizo co-residents. Analyses reveal traditional and new forms of population mobility and migrant selectivity, including gendered forms of marriage migration and rural-urban moves driven by education. These results illustrate a dynamic present and an uncertain future for indigenous populations in which rural, natural-resource-based lifeways may well be sustained but with increasing links to urban areas.

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

PubMed

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Dynamics of land change in India: a fine-scale spatial analysis

NASA Astrophysics Data System (ADS)

Meiyappan, P.; Roy, P. S.; Sharma, Y.; Jain, A. K.; Ramachandran, R.; Joshi, P. K.

2015-12-01

Land is scarce in India: India occupies 2.4% of worlds land area, but supports over 1/6th of worlds human and livestock population. This high population to land ratio, combined with socioeconomic development and increasing consumption has placed tremendous pressure on India's land resources for food, feed, and fuel. In this talk, we present contemporary (1985 to 2005) spatial estimates of land change in India using national-level analysis of Landsat imageries. Further, we investigate the causes of the spatial patterns of change using two complementary lines of evidence. First, we use statistical models estimated at macro-scale to understand the spatial relationships between land change patterns and their concomitant drivers. This analysis using our newly compiled extensive socioeconomic database at village level (~630,000 units), is 100x higher in spatial resolution compared to existing datasets, and covers over 200 variables. The detailed socioeconomic data enabled the fine-scale spatial analysis with Landsat data. Second, we synthesized information from over 130 survey based case studies on land use drivers in India to complement our macro-scale analysis. The case studies are especially useful to identify unobserved variables (e.g. farmer's attitude towards risk). Ours is the most detailed analysis of contemporary land change in India, both in terms of national extent, and the use of detailed spatial information on land change, socioeconomic factors, and synthesis of case studies.

Analysing and correcting the differences between multi-source and multi-scale spatial remote sensing observations.

PubMed

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

PubMed Central

Dong, Yingying; Luo, Ruisen; Feng, Haikuan; Wang, Jihua; Zhao, Jinling; Zhu, Yining; Yang, Guijun

2014-01-01

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation. PMID:25405760

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Maximizing Accessibility to Spatially Referenced Digital Data.

ERIC Educational Resources Information Center

Hunt, Li; Joselyn, Mark

1995-01-01

Discusses some widely available spatially referenced datasets, including raster and vector datasets. Strategies for improving accessibility include: acquisition of data in a software-dependent format; reorganization of data into logical geographic units; acquisition of intelligent retrieval software; improving computer hardware; and intelligent…

Global assessment of human losses due to earthquakes

USGS Publications Warehouse

Silva, Vitor; Jaiswal, Kishor; Weatherill, Graeme; Crowley, Helen

2014-01-01

Current studies have demonstrated a sharp increase in human losses due to earthquakes. These alarming levels of casualties suggest the need for large-scale investment in seismic risk mitigation, which, in turn, requires an adequate understanding of the extent of the losses, and location of the most affected regions. Recent developments in global and uniform datasets such as instrumental and historical earthquake catalogues, population spatial distribution and country-based vulnerability functions, have opened an unprecedented possibility for a reliable assessment of earthquake consequences at a global scale. In this study, a uniform probabilistic seismic hazard assessment (PSHA) model was employed to derive a set of global seismic hazard curves, using the open-source software OpenQuake for seismic hazard and risk analysis. These results were combined with a collection of empirical fatality vulnerability functions and a population dataset to calculate average annual human losses at the country level. The results from this study highlight the regions/countries in the world with a higher seismic risk, and thus where risk reduction measures should be prioritized.

Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Treesearch

John T. Abatzoglou; Solomon Z. Dobrowski; Sean A. Parks; Katherine C. Hegewisch

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from...

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

PubMed

Heino, Jani; Melo, Adriano S; Bini, Luis Mauricio; Altermatt, Florian; Al-Shami, Salman A; Angeler, David G; Bonada, Núria; Brand, Cecilia; Callisto, Marcos; Cottenie, Karl; Dangles, Olivier; Dudgeon, David; Encalada, Andrea; Göthe, Emma; Grönroos, Mira; Hamada, Neusa; Jacobsen, Dean; Landeiro, Victor L; Ligeiro, Raphael; Martins, Renato T; Miserendino, María Laura; Md Rawi, Che Salmah; Rodrigues, Marciel E; Roque, Fabio de Oliveira; Sandin, Leonard; Schmera, Denes; Sgarbi, Luciano F; Simaika, John P; Siqueira, Tadeu; Thompson, Ross M; Townsend, Colin R

2015-03-01

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.

Using mixture tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada

NASA Astrophysics Data System (ADS)

Brelsford, Christa; Shepherd, Doug

2013-09-01

In desert cities, securing sufficient water supply to meet the needs of both existing population and future growth is a complex problem with few easy solutions. Grass lawns are a major driver of water consumption and accurate measurements of vegetation area are necessary to understand drivers of changes in household water consumption. Measuring vegetation change in a heterogeneous urban environment requires sub-pixel estimation of vegetation area. Mixture Tuned Match Filtering has been successfully applied to target detection for materials that only cover small portions of a satellite image pixel. There have been few successful applications of MTMF to fractional area estimation, despite theory that suggests feasibility. We use a ground truth dataset over ten times larger than that available for any previous MTMF application to estimate the bias between ground truth data and matched filter results. We find that the MTMF algorithm underestimates the fractional area of vegetation by 5-10%, and calculate that averaging over 20 to 30 pixels is necessary to correct this bias. We conclude that with a large ground truth dataset, using MTMF for fractional area estimation is possible when results can be estimated at a lower spatial resolution than the base image. When this method is applied to estimating vegetation area in Las Vegas, NV spatial and temporal trends are consistent with expectations from known population growth and policy goals.

Bat trait, genetic and pathogen data from large-scale investigations of African fruit bats, Eidolon helvum.

PubMed

Peel, Alison J; Baker, Kate S; Hayman, David T S; Suu-Ire, Richard; Breed, Andrew C; Gembu, Guy-Crispin; Lembo, Tiziana; Fernández-Loras, Andrés; Sargan, David R; Fooks, Anthony R; Cunningham, Andrew A; Wood, James L N

2016-08-01

Bats, including African straw-coloured fruit bats (Eidolon helvum), have been highlighted as reservoirs of many recently emerged zoonotic viruses. This common, widespread and ecologically important species was the focus of longitudinal and continent-wide studies of the epidemiological and ecology of Lagos bat virus, henipaviruses and Achimota viruses. Here we present a spatial, morphological, demographic, genetic and serological dataset encompassing 2827 bats from nine countries over an 8-year period. Genetic data comprises cytochrome b mitochondrial sequences (n=608) and microsatellite genotypes from 18 loci (n=544). Tooth-cementum analyses (n=316) allowed derivation of rare age-specific serologic data for a lyssavirus, a henipavirus and two rubulaviruses. This dataset contributes a substantial volume of data on the ecology of E. helvum and its viruses and will be valuable for a wide range of studies, including viral transmission dynamic modelling in age-structured populations, investigation of seasonal reproductive asynchrony in wide-ranging species, ecological niche modelling, inference of island colonisation history, exploration of relationships between island and body size, and various spatial analyses of demographic, morphometric or serological data.

Estimating Regions of Oceanographic Importance for Seabirds Using A-Spatial Data.

PubMed

Humphries, Grant Richard Woodrow

2015-01-01

Advances in GPS tracking technologies have allowed for rapid assessment of important oceanographic regions for seabirds. This allows us to understand seabird distributions, and the characteristics which determine the success of populations. In many cases, quality GPS tracking data may not be available; however, long term population monitoring data may exist. In this study, a method to infer important oceanographic regions for seabirds will be presented using breeding sooty shearwaters as a case study. This method combines a popular machine learning algorithm (generalized boosted regression modeling), geographic information systems, long-term ecological data and open access oceanographic datasets. Time series of chick size and harvest index data derived from a long term dataset of Maori 'muttonbirder' diaries were obtained and used as response variables in a gridded spatial model. It was found that areas of the sub-Antarctic water region best capture the variation in the chick size data. Oceanographic features including wind speed and charnock (a derived variable representing ocean surface roughness) came out as top predictor variables in these models. Previously collected GPS data demonstrates that these regions are used as "flyways" by sooty shearwaters during the breeding season. It is therefore likely that wind speeds in these flyways affect the ability of sooty shearwaters to provision for their chicks due to changes in flight dynamics. This approach was designed to utilize machine learning methodology but can also be implemented with other statistical algorithms. Furthermore, these methods can be applied to any long term time series of population data to identify important regions for a species of interest.

Indigenous migration dynamics in the Ecuadorian Amazon: a longitudinal and hierarchical analysis

PubMed Central

Davis, Jason; Sellers, Samuel; Gray, Clark; Bilsborrow, Richard

2017-01-01

Amazonian indigenous populations are approaching a critical stage in their history in which increasing education and market integration, rapid population growth and degradation of natural resources threaten the survival of their traditions and livelihoods. A topic that has hardly been touched upon in this context is migration and population mobility. We address this by analysing a unique longitudinal dataset from the Ecuadorian Amazon on the spatial mobility of five indigenous groups and mestizo co-residents. Analyses reveal traditional and new forms of population mobility and migrant selectivity, including gendered forms of marriage migration and rural-urban moves driven by education. These results illustrate a dynamic present and an uncertain future for indigenous populations in which rural, natural-resource-based lifeways may well be sustained but with increasing links to urban areas. PMID:29129939

Multi-scale temporal and spatial variation in genotypic composition of Cladophora-borne Escherichia coli populations in Lake Michigan.

PubMed

Badgley, Brian D; Ferguson, John; Vanden Heuvel, Amy; Kleinheinz, Gregory T; McDermott, Colleen M; Sandrin, Todd R; Kinzelman, Julie; Junion, Emily A; Byappanahalli, Muruleedhara N; Whitman, Richard L; Sadowsky, Michael J

2011-01-01

High concentrations of Escherichia coli in mats of Cladophora in the Great Lakes have raised concern over the continued use of this bacterium as an indicator of microbial water quality. Determining the impacts of these environmentally abundant E. coli, however, necessitates a better understanding of their ecology. In this study, the population structure of 4285 Cladophora-borne E. coli isolates, obtained over multiple three day periods from Lake Michigan Cladophora mats in 2007-2009, was examined by using DNA fingerprint analyses. In contrast to previous studies that have been done using isolates from attached Cladophora obtained over large time scales and distances, the extensive sampling done here on free-floating mats over successive days at multiple sites provided a large dataset that allowed for a detailed examination of changes in population structure over a wide range of spatial and temporal scales. While Cladophora-borne E. coli populations were highly diverse and consisted of many unique isolates, multiple clonal groups were also present and accounted for approximately 33% of all isolates examined. Patterns in population structure were also evident. At the broadest scales, E. coli populations showed some temporal clustering when examined by year, but did not show good spatial distinction among sites. E. coli population structure also showed significant patterns at much finer temporal scales. Populations were distinct on an individual mat basis at a given site, and on individual days within a single mat. Results of these studies indicate that Cladophora-borne E. coli populations consist of a mixture of stable, and possibly naturalized, strains that persist during the life of the mat, and more unique, transient strains that can change over rapid time scales. It is clear that further study of microbial processes at fine spatial and temporal scales is needed, and that caution must be taken when interpolating short term microbial dynamics from results obtained from weekly or monthly samples. Copyright © 2010 Elsevier Ltd. All rights reserved.

Multi-scale temporal and spatial variation in genotypic composition of Cladophora-borne Escherichia coli populations in Lake Michigan

USGS Publications Warehouse

Badgley, B.D.; Ferguson, J.; Heuvel, A.V.; Kleinheinz, G.T.; McDermott, C.M.; Sandrin, T.R.; Kinzelman, J.; Junion, E.A.; Byappanahalli, M.N.; Whitman, R.L.; Sadowsky, M.J.

2011-01-01

High concentrations of Escherichia coli in mats of Cladophora in the Great Lakes have raised concern over the continued use of this bacterium as an indicator of microbial water quality. Determining the impacts of these environmentally abundant E. coli, however, necessitates a better understanding of their ecology. In this study, the population structure of 4285 Cladophora-borne E. coli isolates, obtained over multiple three day periods from Lake Michigan Cladophora mats in 2007-2009, was examined by using DNA fingerprint analyses. In contrast to previous studies that have been done using isolates from attached Cladophora obtained over large time scales and distances, the extensive sampling done here on free-floating mats over successive days at multiple sites provided a large dataset that allowed for a detailed examination of changes in population structure over a wide range of spatial and temporal scales. While Cladophora-borne E. coli populations were highly diverse and consisted of many unique isolates, multiple clonal groups were also present and accounted for approximately 33% of all isolates examined. Patterns in population structure were also evident. At the broadest scales, E. coli populations showed some temporal clustering when examined by year, but did not show good spatial distinction among sites. E. coli population structure also showed significant patterns at much finer temporal scales. Populations were distinct on an individual mat basis at a given site, and on individual days within a single mat. Results of these studies indicate that Cladophora-borne E. coli populations consist of a mixture of stable, and possibly naturalized, strains that persist during the life of the mat, and more unique, transient strains that can change over rapid time scales. It is clear that further study of microbial processes at fine spatial and temporal scales is needed, and that caution must be taken when interpolating short term microbial dynamics from results obtained from weekly or monthly samples.

A reanalysis dataset of the South China Sea.

PubMed

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992-2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability.

A reanalysis dataset of the South China Sea

PubMed Central

Zeng, Xuezhi; Peng, Shiqiu; Li, Zhijin; Qi, Yiquan; Chen, Rongyu

2014-01-01

Ocean reanalysis provides a temporally continuous and spatially gridded four-dimensional estimate of the ocean state for a better understanding of the ocean dynamics and its spatial/temporal variability. Here we present a 19-year (1992–2010) high-resolution ocean reanalysis dataset of the upper ocean in the South China Sea (SCS) produced from an ocean data assimilation system. A wide variety of observations, including in-situ temperature/salinity profiles, ship-measured and satellite-derived sea surface temperatures, and sea surface height anomalies from satellite altimetry, are assimilated into the outputs of an ocean general circulation model using a multi-scale incremental three-dimensional variational data assimilation scheme, yielding a daily high-resolution reanalysis dataset of the SCS. Comparisons between the reanalysis and independent observations support the reliability of the dataset. The presented dataset provides the research community of the SCS an important data source for studying the thermodynamic processes of the ocean circulation and meso-scale features in the SCS, including their spatial and temporal variability. PMID:25977803

ASSESSING THE ACCURACY OF NATIONAL LAND COVER DATASET AREA ESTIMATES AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Site specific accuracy assessments provide fine-scale evaluation of the thematic accuracy of land use/land cover (LULC) datasets; however, they provide little insight into LULC accuracy across varying spatial extents. Additionally, LULC data are typically used to describe lands...

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

PubMed

Rueckl, Martin; Lenzi, Stephen C; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca 2+ -imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca 2+ imaging datasets, particularly when these have been acquired at different spatial scales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

PubMed Central

Rueckl, Martin; Lenzi, Stephen C.; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W.

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales. PMID:28706482

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

PubMed

Aragri, Adriana; Scarabino, Tommaso; Seifritz, Erich; Comani, Silvia; Cirillo, Sossio; Tedeschi, Gioacchino; Esposito, Fabrizio; Di Salle, Francesco

2006-09-01

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity. (c) 2006 Wiley-Liss, Inc.

Cadastral Database Positional Accuracy Improvement

NASA Astrophysics Data System (ADS)

Hashim, N. M.; Omar, A. H.; Ramli, S. N. M.; Omar, K. M.; Din, N.

2017-10-01

Positional Accuracy Improvement (PAI) is the refining process of the geometry feature in a geospatial dataset to improve its actual position. This actual position relates to the absolute position in specific coordinate system and the relation to the neighborhood features. With the growth of spatial based technology especially Geographical Information System (GIS) and Global Navigation Satellite System (GNSS), the PAI campaign is inevitable especially to the legacy cadastral database. Integration of legacy dataset and higher accuracy dataset like GNSS observation is a potential solution for improving the legacy dataset. However, by merely integrating both datasets will lead to a distortion of the relative geometry. The improved dataset should be further treated to minimize inherent errors and fitting to the new accurate dataset. The main focus of this study is to describe a method of angular based Least Square Adjustment (LSA) for PAI process of legacy dataset. The existing high accuracy dataset known as National Digital Cadastral Database (NDCDB) is then used as bench mark to validate the results. It was found that the propose technique is highly possible for positional accuracy improvement of legacy spatial datasets.

Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States.

PubMed

Eggo, Rosalind M; Cauchemez, Simon; Ferguson, Neil M

2011-02-06

There is still limited understanding of key determinants of spatial spread of influenza. The 1918 pandemic provides an opportunity to elucidate spatial determinants of spread on a large scale. To better characterize the spread of the 1918 major wave, we fitted a range of city-to-city transmission models to mortality data collected for 246 population centres in England and Wales and 47 cities in the US. Using a gravity model for city-to-city contacts, we explored the effect of population size and distance on the spread of disease and tested assumptions regarding density dependence in connectivity between cities. We employed Bayesian Markov Chain Monte Carlo methods to estimate parameters of the model for population, infectivity, distance and density dependence. We inferred the most likely transmission trees for both countries. For England and Wales, a model that estimated the degree of density dependence in connectivity between cities was preferable by deviance information criterion comparison. Early in the major wave, long distance infective interactions predominated, with local infection events more likely as the epidemic became widespread. For the US, with fewer more widely dispersed cities, statistical power was lacking to estimate population size dependence or the degree of density dependence, with the preferred model depending on distance only. We find that parameters estimated from the England and Wales dataset can be applied to the US data with no likelihood penalty.

Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States

PubMed Central

Eggo, Rosalind M.; Cauchemez, Simon; Ferguson, Neil M.

2011-01-01

There is still limited understanding of key determinants of spatial spread of influenza. The 1918 pandemic provides an opportunity to elucidate spatial determinants of spread on a large scale. To better characterize the spread of the 1918 major wave, we fitted a range of city-to-city transmission models to mortality data collected for 246 population centres in England and Wales and 47 cities in the US. Using a gravity model for city-to-city contacts, we explored the effect of population size and distance on the spread of disease and tested assumptions regarding density dependence in connectivity between cities. We employed Bayesian Markov Chain Monte Carlo methods to estimate parameters of the model for population, infectivity, distance and density dependence. We inferred the most likely transmission trees for both countries. For England and Wales, a model that estimated the degree of density dependence in connectivity between cities was preferable by deviance information criterion comparison. Early in the major wave, long distance infective interactions predominated, with local infection events more likely as the epidemic became widespread. For the US, with fewer more widely dispersed cities, statistical power was lacking to estimate population size dependence or the degree of density dependence, with the preferred model depending on distance only. We find that parameters estimated from the England and Wales dataset can be applied to the US data with no likelihood penalty. PMID:20573630

Discrimination of fish populations using parasites: Random Forests on a 'predictable' host-parasite system.

PubMed

Pérez-Del-Olmo, A; Montero, F E; Fernández, M; Barrett, J; Raga, J A; Kostadinova, A

2010-10-01

We address the effect of spatial scale and temporal variation on model generality when forming predictive models for fish assignment using a new data mining approach, Random Forests (RF), to variable biological markers (parasite community data). Models were implemented for a fish host-parasite system sampled along the Mediterranean and Atlantic coasts of Spain and were validated using independent datasets. We considered 2 basic classification problems in evaluating the importance of variations in parasite infracommunities for assignment of individual fish to their populations of origin: multiclass (2-5 population models, using 2 seasonal replicates from each of the populations) and 2-class task (using 4 seasonal replicates from 1 Atlantic and 1 Mediterranean population each). The main results are that (i) RF are well suited for multiclass population assignment using parasite communities in non-migratory fish; (ii) RF provide an efficient means for model cross-validation on the baseline data and this allows sample size limitations in parasite tag studies to be tackled effectively; (iii) the performance of RF is dependent on the complexity and spatial extent/configuration of the problem; and (iv) the development of predictive models is strongly influenced by seasonal change and this stresses the importance of both temporal replication and model validation in parasite tagging studies.

Influence of spatial and temporal scales in identifying temperature extremes

NASA Astrophysics Data System (ADS)

van Eck, Christel M.; Friedlingstein, Pierre; Mulder, Vera L.; Regnier, Pierre A. G.

2016-04-01

Extreme heat events are becoming more frequent. Notable are severe heatwaves such as the European heatwave of 2003, the Russian heat wave of 2010 and the Australian heatwave of 2013. Surface temperature is attaining new maxima not only during the summer but also during the winter. The year of 2015 is reported to be a temperature record breaking year for both summer and winter. These extreme temperatures are taking their human and environmental toll, emphasizing the need for an accurate method to define a heat extreme in order to fully understand the spatial and temporal spread of an extreme and its impact. This research aims to explore how the use of different spatial and temporal scales influences the identification of a heat extreme. For this purpose, two near-surface temperature datasets of different temporal scale and spatial scale are being used. First, the daily ERA-Interim dataset of 0.25 degree and a time span of 32 years (1979-2010). Second, the daily Princeton Meteorological Forcing Dataset of 0.5 degree and a time span of 63 years (1948-2010). A temperature is considered extreme anomalous when it is surpassing the 90th, 95th, or the 99th percentile threshold based on the aforementioned pre-processed datasets. The analysis is conducted on a global scale, dividing the world in IPCC's so-called SREX regions developed for the analysis of extreme climate events. Pre-processing is done by detrending and/or subtracting the monthly climatology based on 32 years of data for both datasets and on 63 years of data for only the Princeton Meteorological Forcing Dataset. This results in 6 datasets of temperature anomalies from which the location in time and space of the anomalous warm days are identified. Comparison of the differences between these 6 datasets in terms of absolute threshold temperatures for extremes and the temporal and spatial spread of the extreme anomalous warm days show a dependence of the results on the datasets and methodology used. This stresses the need for a careful selection of data and methodology when identifying heat extremes.

Human movement data for malaria control and elimination strategic planning.

PubMed

Pindolia, Deepa K; Garcia, Andres J; Wesolowski, Amy; Smith, David L; Buckee, Caroline O; Noor, Abdisalan M; Snow, Robert W; Tatem, Andrew J

2012-06-18

Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.

Human movement data for malaria control and elimination strategic planning

PubMed Central

2012-01-01

Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements. PMID:22703541

VEMAP Phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

USGS Publications Warehouse

Kittel, T.G.F.; Rosenbloom, N.A.; Royle, J. Andrew; Daly, Christopher; Gibson, W.P.; Fisher, H.H.; Thornton, P.; Yates, D.N.; Aulenbach, S.; Kaufman, C.; McKeown, R.; Bachelet, D.; Schimel, D.S.; Neilson, R.; Lenihan, J.; Drapek, R.; Ojima, D.S.; Parton, W.J.; Melillo, J.M.; Kicklighter, D.W.; Tian, H.; McGuire, A.D.; Sykes, M.T.; Smith, B.; Cowling, S.; Hickler, T.; Prentice, I.C.; Running, S.; Hibbard, K.A.; Post, W.M.; King, A.W.; Smith, T.; Rizzo, B.; Woodward, F.I.

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP), a biogeochemical and dynamic vegetation model intercomparison. The dataset covers the period 1895-1993 on a 0.5?? latitude/longitude grid. Climate is represented at both monthly and daily timesteps. Variables are: precipitation, mininimum and maximum temperature, total incident solar radiation, daylight-period irradiance, vapor pressure, and daylight-period relative humidity. The dataset was derived from US Historical Climate Network (HCN), cooperative network, and snowpack telemetry (SNOTEL) monthly precipitation and mean minimum and maximum temperature station data. We employed techniques that rely on geostatistical and physical relationships to create the temporally and spatially complete dataset. We developed a local kriging prediction model to infill discontinuous and limited-length station records based on spatial autocorrelation structure of climate anomalies. A spatial interpolation model (PRISM) that accounts for physiographic controls was used to grid the infilled monthly station data. We implemented a stochastic weather generator (modified WGEN) to disaggregate the gridded monthly series to dailies. Radiation and humidity variables were estimated from the dailies using a physically-based empirical surface climate model (MTCLIM3). Derived datasets include a 100 yr model spin-up climate and a historical Palmer Drought Severity Index (PDSI) dataset. The VEMAP dataset exhibits statistically significant trends in temperature, precipitation, solar radiation, vapor pressure, and PDSI for US National Assessment regions. The historical climate and companion datasets are available online at data archive centers. ?? Inter-Research 2004.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

NASA Astrophysics Data System (ADS)

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya; Tang, Qiuhong; Vernon, Chris; Leng, Guoyong; Liu, Yaling; Döll, Petra; Eisner, Stephanie; Gerten, Dieter; Hanasaki, Naota; Wada, Yoshihide

2018-04-01

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971-2010, which distinguishes six water use sectors, i.e., irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971-2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. The reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.

Human-caused mortality influences spatial population dynamics: pumas in landscapes with varying mortality risks

USGS Publications Warehouse

Newby, Jesse R.; Mills, L. Scott; Ruth, Toni K.; Pletscher, Daniel H.; Mitchell, Michael S.; Quigley, Howard B.; Murphy, Kerry M.; DeSimone, Rich

2013-01-01

An understanding of how stressors affect dispersal attributes and the contribution of local populations to multi-population dynamics are of immediate value to basic and applied ecology. Puma (Puma concolor) populations are expected to be influenced by inter-population movements and susceptible to human-induced source–sink dynamics. Using long-term datasets we quantified the contribution of two puma populations to operationally define them as sources or sinks. The puma population in the Northern Greater Yellowstone Ecosystem (NGYE) was largely insulated from human-induced mortality by Yellowstone National Park. Pumas in the western Montana Garnet Mountain system were exposed to greater human-induced mortality, which changed over the study due to the closure of a 915 km2 area to hunting. The NGYE’s population growth depended on inter-population movements, as did its ability to act as a source to the larger region. The heavily hunted Garnet area was a sink with a declining population until the hunting closure, after which it became a source with positive intrinsic growth and a 16× increase in emigration. We also examined the spatial and temporal characteristics of individual dispersal attributes (emigration, dispersal distance, establishment success) of subadult pumas (N = 126). Human-caused mortality was found to negatively impact all three dispersal components. Our results demonstrate the influence of human-induced mortality on not only within population vital rates, but also inter-population vital rates, affecting the magnitude and mechanisms of local population’s contribution to the larger metapopulation.

Bayesian Spatial Design of Optimal Deep Tubewell Locations in Matlab, Bangladesh.

PubMed

Warren, Joshua L; Perez-Heydrich, Carolina; Yunus, Mohammad

2013-09-01

We introduce a method for statistically identifying the optimal locations of deep tubewells (dtws) to be installed in Matlab, Bangladesh. Dtw installations serve to mitigate exposure to naturally occurring arsenic found at groundwater depths less than 200 meters, a serious environmental health threat for the population of Bangladesh. We introduce an objective function, which incorporates both arsenic level and nearest town population size, to identify optimal locations for dtw placement. Assuming complete knowledge of the arsenic surface, we then demonstrate how minimizing the objective function over a domain favors dtws placed in areas with high arsenic values and close to largely populated regions. Given only a partial realization of the arsenic surface over a domain, we use a Bayesian spatial statistical model to predict the full arsenic surface and estimate the optimal dtw locations. The uncertainty associated with these estimated locations is correctly characterized as well. The new method is applied to a dataset from a village in Matlab and the estimated optimal locations are analyzed along with their respective 95% credible regions.

Does Land Degradation Increase Poverty in Developing Countries?

PubMed Central

2016-01-01

Land degradation is a global problem that particularly impacts the poor rural inhabitants of low and middle-income countries. We improve upon existing literature by estimating the extent of rural populations in 2000 and 2010 globally on degrading and improving agricultural land, taking into account the role of market access, and analyzing the resulting impacts on poverty. Using a variety of spatially referenced datasets, we estimate that 1.33 billion people worldwide in 2000 were located on degrading agricultural land (DAL), of which 1.26 billion were in developing countries. Almost all the world’s 200 million people on remote DAL were in developing countries, which is about 6% of their rural population. There were also 1.54 billion rural people on improving agricultural land (IAL), with 1.34 billion in developing countries. We find that a lower share of people in 2000 on DAL, or a higher share on IAL, lowers significantly how much overall economic growth reduces poverty from 2000 to 2012 across 83 developing countries. As the population on DAL and IAL in developing countries grew by 13% and 15% respectively from 2000 to 2010, these changing spatial distributions of rural populations could impact significantly future poverty in developing countries. PMID:27167738

An Evaluation of Database Solutions to Spatial Object Association

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, V S; Kurc, T; Saltz, J

2008-06-24

Object association is a common problem encountered in many applications. Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two datasets based on their positions in a common spatial coordinate system--one of the datasets may correspond to a catalog of objects observed over time in a multi-dimensional domain; the other dataset may consist of objects observed in a snapshot of the domain at a time point. The use of database management systems to the solve the object association problem provides portability across different platforms and also greater flexibility. Increasingmore » dataset sizes in today's applications, however, have made object association a data/compute-intensive problem that requires targeted optimizations for efficient execution. In this work, we investigate how database-based crossmatch algorithms can be deployed on different database system architectures and evaluate the deployments to understand the impact of architectural choices on crossmatch performance and associated trade-offs. We investigate the execution of two crossmatch algorithms on (1) a parallel database system with active disk style processing capabilities, (2) a high-throughput network database (MySQL Cluster), and (3) shared-nothing databases with replication. We have conducted our study in the context of a large-scale astronomy application with real use-case scenarios.« less

A Big Spatial Data Processing Framework Applying to National Geographic Conditions Monitoring

NASA Astrophysics Data System (ADS)

Xiao, F.

2018-04-01

In this paper, a novel framework for spatial data processing is proposed, which apply to National Geographic Conditions Monitoring project of China. It includes 4 layers: spatial data storage, spatial RDDs, spatial operations, and spatial query language. The spatial data storage layer uses HDFS to store large size of spatial vector/raster data in the distributed cluster. The spatial RDDs are the abstract logical dataset of spatial data types, and can be transferred to the spark cluster to conduct spark transformations and actions. The spatial operations layer is a series of processing on spatial RDDs, such as range query, k nearest neighbor and spatial join. The spatial query language is a user-friendly interface which provide people not familiar with Spark with a comfortable way to operation the spatial operation. Compared with other spatial frameworks, it is highlighted that comprehensive technologies are referred for big spatial data processing. Extensive experiments on real datasets show that the framework achieves better performance than traditional process methods.

Application of an imputation method for geospatial inventory of forest structural attributes across multiple spatial scales in the Lake States, U.S.A

NASA Astrophysics Data System (ADS)

Deo, Ram K.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

GoMRC Website ‘Meta-analysis Report: Land-use and submerged aquatic vegetation change in the Gulf of Mexico’

DOE Office of Scientific and Technical Information (OSTI.GOV)

Judd, Chaeli; Stefansson, Emily S.; Brushnahan, Heather

2007-12-06

Over the past century, health and spatial extent of seagrasses has decreased dramatically in the Gulf of Mexico. While some of the changes can be explained by direct impacts to the seagrass beds, we hypothesize that changes in the land use in the watersheds can also be correlated with the decline of seagrasses. Through this meta-analysis, we researched historical and compared trends in seagrass populations and land use in five bays and their watersheds within the Gulf of Mexico: Mobile Bay, Perdido Bay, Tampa Bay, Charlotte Harbor, and Galveston Bay. Using both historical records and spatial datasets, we examined landmore » use and seagrass trends in these five areas.« less

Spatial and temporal trends in the mortality burden of air pollution in China: 2004–2012

PubMed Central

Liu, Miaomiao; Huang, Yining; Ma, Zongwei; Jin, Zhou; Liu, Xingyu; Wang, Haikun; Liu, Yang; Wang, Jinnan; Jantunen, Matti; BiDr, Jun; KinneyDr, Patrick L.

2017-01-01

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10 × 10 km satellite-based PM2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing–Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha–Zhuzhou–Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in–migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions. PMID:27745948

Spatial and temporal trends in the mortality burden of air pollution in China: 2004-2012.

PubMed

Liu, Miaomiao; Huang, Yining; Ma, Zongwei; Jin, Zhou; Liu, Xingyu; Wang, Haikun; Liu, Yang; Wang, Jinnan; Jantunen, Matti; Bi, Jun; Kinney, Patrick L

2017-01-01

While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10×10km satellite-based PM 2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM 2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha-Zhuzhou-Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in-migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions. Copyright © 2016. Published by Elsevier Ltd.

How Much Can Remotely-Sensed Natural Resource Inventories Benefit from Finer Spatial Resolutions?

NASA Astrophysics Data System (ADS)

Hou, Z.; Xu, Q.; McRoberts, R. E.; Ståhl, G.; Greenberg, J. A.

2017-12-01

For remote sensing facilitated natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the variable of interest was the stem volume (m3/ha) convertible to the woodland aboveground biomass. A sample consisting of 160 field plots was selected and measured from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, mu, and the variance of the estimator of the population mean, Var(mu.hat), were estimated in two inferential frameworks, model-based and model-assisted, and compared; for each framework, Var(mu.hat) was estimated both analytically and empirically. Empirical variances were estimated with bootstrapping that for resampling takes clustering effects into account. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributes to greater precision for estimators of population parameter, but this increase is slight at a maximum rate of 20% considering that RapidEye data are 36 times finer resolution than Landsat 8 data; (2) subpixel information on texture is marginally beneficial when it comes to making inference for population of large areas; (3) cost-effectiveness is more favorable for the free of charge Landsat 8 imagery than RapidEye imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) model-based variance estimates are consistent with each other and about half as large as stabilized model-assisted estimates, suggesting superior effectiveness of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

ESSG-based global spatial reference frame for datasets interrelation

NASA Astrophysics Data System (ADS)

Yu, J. Q.; Wu, L. X.; Jia, Y. J.

2013-10-01

To know well about the highly complex earth system, a large volume of, as well as a large variety of, datasets on the planet Earth are being obtained, distributed, and shared worldwide everyday. However, seldom of existing systems concentrates on the distribution and interrelation of different datasets in a common Global Spatial Reference Frame (GSRF), which holds an invisble obstacle to the data sharing and scientific collaboration. Group on Earth Obeservation (GEO) has recently established a new GSRF, named Earth System Spatial Grid (ESSG), for global datasets distribution, sharing and interrelation in its 2012-2015 WORKING PLAN.The ESSG may bridge the gap among different spatial datasets and hence overcome the obstacles. This paper is to present the implementation of the ESSG-based GSRF. A reference spheroid, a grid subdvision scheme, and a suitable encoding system are required to implement it. The radius of ESSG reference spheroid was set to the double of approximated Earth radius to make datasets from different areas of earth system science being covered. The same paramerters of positioning and orienting as Earth Centred Earth Fixed (ECEF) was adopted for the ESSG reference spheroid to make any other GSRFs being freely transformed into the ESSG-based GSRF. Spheroid degenerated octree grid with radius refiment (SDOG-R) and its encoding method were taken as the grid subdvision and encoding scheme for its good performance in many aspects. A triple (C, T, A) model is introduced to represent and link different datasets based on the ESSG-based GSRF. Finally, the methods of coordinate transformation between the ESSGbased GSRF and other GSRFs were presented to make ESSG-based GSRF operable and propagable.

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA

PubMed Central

Jiang, Yueyang; Kim, John B.; Still, Christopher J.; Kerns, Becky K.; Kline, Jeffrey D.; Cunningham, Patrick G.

2018-01-01

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies. PMID:29461513

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

PubMed

Jiang, Yueyang; Kim, John B; Still, Christopher J; Kerns, Becky K; Kline, Jeffrey D; Cunningham, Patrick G

2018-02-20

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Advancing the integration of spatial data to map human and natural drivers on coral reefs

PubMed Central

Gove, Jamison M.; Walecka, Hilary R.; Donovan, Mary K.; Williams, Gareth J.; Jouffray, Jean-Baptiste; Crowder, Larry B.; Erickson, Ashley; Falinski, Kim; Friedlander, Alan M.; Kappel, Carrie V.; Kittinger, John N.; McCoy, Kaylyn; Norström, Albert; Nyström, Magnus; Oleson, Kirsten L. L.; Stamoulis, Kostantinos A.; White, Crow; Selkoe, Kimberly A.

2018-01-01

A major challenge for coral reef conservation and management is understanding how a wide range of interacting human and natural drivers cumulatively impact and shape these ecosystems. Despite the importance of understanding these interactions, a methodological framework to synthesize spatially explicit data of such drivers is lacking. To fill this gap, we established a transferable data synthesis methodology to integrate spatial data on environmental and anthropogenic drivers of coral reefs, and applied this methodology to a case study location–the Main Hawaiian Islands (MHI). Environmental drivers were derived from time series (2002–2013) of climatological ranges and anomalies of remotely sensed sea surface temperature, chlorophyll-a, irradiance, and wave power. Anthropogenic drivers were characterized using empirically derived and modeled datasets of spatial fisheries catch, sedimentation, nutrient input, new development, habitat modification, and invasive species. Within our case study system, resulting driver maps showed high spatial heterogeneity across the MHI, with anthropogenic drivers generally greatest and most widespread on O‘ahu, where 70% of the state’s population resides, while sedimentation and nutrients were dominant in less populated islands. Together, the spatial integration of environmental and anthropogenic driver data described here provides a first-ever synthetic approach to visualize how the drivers of coral reef state vary in space and demonstrates a methodological framework for implementation of this approach in other regions of the world. By quantifying and synthesizing spatial drivers of change on coral reefs, we provide an avenue for further research to understand how drivers determine reef diversity and resilience, which can ultimately inform policies to protect coral reefs. PMID:29494613

Internal Consistency of the NVAP Water Vapor Dataset

NASA Technical Reports Server (NTRS)

Suggs, Ronnie J.; Jedlovec, Gary J.; Arnold, James E. (Technical Monitor)

2001-01-01

The NVAP (NASA Water Vapor Project) dataset is a global dataset at 1 x 1 degree spatial resolution consisting of daily, pentad, and monthly atmospheric precipitable water (PW) products. The analysis blends measurements from the Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), the Special Sensor Microwave/Imager (SSM/I), and radiosonde observations into a daily collage of PW. The original dataset consisted of five years of data from 1988 to 1992. Recent updates have added three additional years (1993-1995) and incorporated procedural and algorithm changes from the original methodology. Since each of the PW sources (TOVS, SSM/I, and radiosonde) do not provide global coverage, each of these sources compliment one another by providing spatial coverage over regions and during times where the other is not available. For this type of spatial and temporal blending to be successful, each of the source components should have similar or compatible accuracies. If this is not the case, regional and time varying biases may be manifested in the NVAP dataset. This study examines the consistency of the NVAP source data by comparing daily collocated TOVS and SSM/I PW retrievals with collocated radiosonde PW observations. The daily PW intercomparisons are performed over the time period of the dataset and for various regions.

Rule-based topology system for spatial databases to validate complex geographic datasets

NASA Astrophysics Data System (ADS)

Martinez-Llario, J.; Coll, E.; Núñez-Andrés, M.; Femenia-Ribera, C.

2017-06-01

A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented. This improved topology rule system is built over the spatial extension Jaspa. Both projects are open source, freely available software developed by the corresponding author of this paper. Currently, there is no spatial DBMS that implements a rule-based topology engine (considering that the topology rules are designed and performed in the spatial backend). If the topology rules are applied in the frontend (as in many GIS desktop programs), ArcGIS is the most advanced solution. The system presented in this paper has several major advantages over the ArcGIS approach: it can be extended with new topology rules, it has a much wider set of rules, and it can mix feature attributes with topology rules as filters. In addition, the topology rule system can work with various DBMSs, including PostgreSQL, H2 or Oracle, and the logic is performed in the spatial backend. The proposed topology system allows users to check the complex spatial relationships among features (from one or several spatial layers) that require some complex cartographic datasets, such as the data specifications proposed by INSPIRE in Europe and the Land Administration Domain Model (LADM) for Cadastral data.

Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...

A new global 1-km dataset of percentage tree cover derived from remote sensing

USGS Publications Warehouse

DeFries, R.S.; Hansen, M.C.; Townshend, J.R.G.; Janetos, A.C.; Loveland, Thomas R.

2000-01-01

Accurate assessment of the spatial extent of forest cover is a crucial requirement for quantifying the sources and sinks of carbon from the terrestrial biosphere. In the more immediate context of the United Nations Framework Convention on Climate Change, implementation of the Kyoto Protocol calls for estimates of carbon stocks for a baseline year as well as for subsequent years. Data sources from country level statistics and other ground-based information are based on varying definitions of 'forest' and are consequently problematic for obtaining spatially and temporally consistent carbon stock estimates. By combining two datasets previously derived from the Advanced Very High Resolution Radiometer (AVHRR) at 1 km spatial resolution, we have generated a prototype global map depicting percentage tree cover and associated proportions of trees with different leaf longevity (evergreen and deciduous) and leaf type (broadleaf and needleleaf). The product is intended for use in terrestrial carbon cycle models, in conjunction with other spatial datasets such as climate and soil type, to obtain more consistent and reliable estimates of carbon stocks. The percentage tree cover dataset is available through the Global Land Cover Facility at the University of Maryland at http://glcf.umiacs.umd.edu.

Dynamic population mapping using mobile phone data.

PubMed

Deville, Pierre; Linard, Catherine; Martin, Samuel; Gilbert, Marius; Stevens, Forrest R; Gaughan, Andrea E; Blondel, Vincent D; Tatem, Andrew J

2014-11-11

During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.

Dynamic population mapping using mobile phone data

PubMed Central

Deville, Pierre; Martin, Samuel; Gilbert, Marius; Stevens, Forrest R.; Gaughan, Andrea E.; Blondel, Vincent D.; Tatem, Andrew J.

2014-01-01

During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography. PMID:25349388

Large Scale Flood Risk Analysis using a New Hyper-resolution Population Dataset

NASA Astrophysics Data System (ADS)

Smith, A.; Neal, J. C.; Bates, P. D.; Quinn, N.; Wing, O.

2017-12-01

Here we present the first national scale flood risk analyses, using high resolution Facebook Connectivity Lab population data and data from a hyper resolution flood hazard model. In recent years the field of large scale hydraulic modelling has been transformed by new remotely sensed datasets, improved process representation, highly efficient flow algorithms and increases in computational power. These developments have allowed flood risk analysis to be undertaken in previously unmodeled territories and from continental to global scales. Flood risk analyses are typically conducted via the integration of modelled water depths with an exposure dataset. Over large scales and in data poor areas, these exposure data typically take the form of a gridded population dataset, estimating population density using remotely sensed data and/or locally available census data. The local nature of flooding dictates that for robust flood risk analysis to be undertaken both hazard and exposure data should sufficiently resolve local scale features. Global flood frameworks are enabling flood hazard data to produced at 90m resolution, resulting in a mis-match with available population datasets which are typically more coarsely resolved. Moreover, these exposure data are typically focused on urban areas and struggle to represent rural populations. In this study we integrate a new population dataset with a global flood hazard model. The population dataset was produced by the Connectivity Lab at Facebook, providing gridded population data at 5m resolution, representing a resolution increase over previous countrywide data sets of multiple orders of magnitude. Flood risk analysis undertaken over a number of developing countries are presented, along with a comparison of flood risk analyses undertaken using pre-existing population datasets.

Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets.

PubMed

Tanase, Mihai A; Ismail, Ismail; Lowell, Kim; Karyanto, Oka; Santoro, Maurizio

2015-01-01

This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height.

Ground and satellite based assessment of meteorological droughts: The Coello river basin case study

NASA Astrophysics Data System (ADS)

Cruz-Roa, A. F.; Olaya-Marín, E. J.; Barrios, M. I.

2017-10-01

The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998-2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

PubMed Central

Nan, Zhixiong; Wei, Ping; Xu, Linhai; Zheng, Nanning

2016-01-01

Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle. PMID:27529248

An assessment of differences in gridded precipitation datasets in complex terrain

NASA Astrophysics Data System (ADS)

Henn, Brian; Newman, Andrew J.; Livneh, Ben; Daly, Christopher; Lundquist, Jessica D.

2018-01-01

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the datasets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas exceed 200 mm yr-1 on average, and relative differences range from 5 to 60% across the Western United States. In areas of high topographic relief, true uncertainties and biases are likely higher than the differences among the datasets; we present evidence of this based on streamflow observations. Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled.

Embedded sparse representation of fMRI data via group-wise dictionary optimization

NASA Astrophysics Data System (ADS)

Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

2016-03-01

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

Spatial Data Services for Interdisciplinary Applications from the NASA Socioeconomic Data and Applications Center

NASA Astrophysics Data System (ADS)

Chen, R. S.; MacManus, K.; Vinay, S.; Yetman, G.

2016-12-01

The Socioeconomic Data and Applications Center (SEDAC), one of 12 Distributed Active Archive Centers (DAACs) in the NASA Earth Observing System Data and Information System (EOSDIS), has developed a variety of operational spatial data services aimed at providing online access, visualization, and analytic functions for geospatial socioeconomic and environmental data. These services include: open web services that implement Open Geospatial Consortium (OGC) specifications such as Web Map Service (WMS), Web Feature Service (WFS), and Web Coverage Service (WCS); spatial query services that support Web Processing Service (WPS) and Representation State Transfer (REST); and web map clients and a mobile app that utilize SEDAC and other open web services. These services may be accessed from a variety of external map clients and visualization tools such as NASA's WorldView, NOAA's Climate Explorer, and ArcGIS Online. More than 200 data layers related to population, settlements, infrastructure, agriculture, environmental pollution, land use, health, hazards, climate change and other aspects of sustainable development are available through WMS, WFS, and/or WCS. Version 2 of the SEDAC Population Estimation Service (PES) supports spatial queries through WPS and REST in the form of a user-defined polygon or circle. The PES returns an estimate of the population residing in the defined area for a specific year (2000, 2005, 2010, 2015, or 2020) based on SEDAC's Gridded Population of the World version 4 (GPWv4) dataset, together with measures of accuracy. The SEDAC Hazards Mapper and the recently released HazPop iOS mobile app enable users to easily submit spatial queries to the PES and see the results. SEDAC has developed an operational virtualized backend infrastructure to manage these services and support their continual improvement as standards change, new data and services become available, and user needs evolve. An ongoing challenge is to improve the reliability and performance of the infrastructure, in conjunction with external services, to meet both research and operational needs.

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products

PubMed Central

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-01-01

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. PMID:28587266

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products.

PubMed

Guo, Xiaoyi; Zhang, Hongyan; Wu, Zhengfang; Zhao, Jianjun; Zhang, Zhengxiang

2017-06-06

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

Spatial and temporal dynamics of commercial reef-fish fisheries on the West Florida Shelf: Understanding drivers of fleet behavior and the implications for future management

NASA Astrophysics Data System (ADS)

Cockrell, M.; Murawski, S. A.; Sanchirico, J. N.; O'Farrell, S.; Strelcheck, A.

2016-02-01

Spatial and temporal patterns of fishing activity have historically been described over relatively coarse scales or with limited datasets. However, new and innovative approaches for fisheries management will require an understanding of both species population dynamics and fleet behavior at finer spatial and temporal resolution. In this study we describe the spatial and temporal patterns of commercial reef-fish fisheries on the West Florida Shelf (WFS) from 2006-14, using a combination of on-board observer, catch logbook, and vessel satellite tracking data. The satellite tracking data is both high resolution (ie, records from each vessel at least once every hour for the duration of a trip), and required of all federally-permitted reef fish vessels in the Gulf of Mexico, making this a uniquely rich and powerful dataset. Along with spatial and temporal fishery dynamics, we quantified concomitant patterns in fishery economics and catch metrics, such as total landings and catch composition. Fishery patterns were correlated to a number of variables across the vessel, trip, and whole fleet scales, including vessel size, distance from home port, number of days at sea, and days available to fish. Notably, changes in management structure during the years examined (eg, establishment of a seasonal closed area in 2009 and implementation of an individual fishing quota system for Grouper-Tilefish in 2010), as well as emergency spatial closures during the Deepwater Horizon oil spill in 2010, enabled us to examine the impacts of specific management frameworks on the WFS reef-fish fishery. This research highlights the need to better understand the biological, economic, and social impacts within fisheries when managing for conservation and fisheries sustainability. We discuss our results in the context of a changing policy and management landscape for marine and coastal resources in the Gulf of Mexico.

Local Hotspots In The Gulf Of Maine: Spatial Overlap Between Dynamic Aggregations Of Primary Productivity And Fish Abundance

NASA Astrophysics Data System (ADS)

Ribera, M.

2016-02-01

Identification of biological hotspots may be a necessary step toward ecosystem-based management goals, as these often signal underlying processes that aggregate or stimulate resources in a particular location. However, previously used metrics to locate these hotspots are not easily adapted to local marine datasets, in part due to the high spatial and temporal variability of phytoplankton populations. While most fish species in temperate regions are well adapted to the seasonal variability of phytoplankton abundance, it is the variability beyond this predictable pattern (i.e. anomalies) that may heavily impact the abundance and spatial distribution of organisms higher up the food chain. The objective of this study was to identify local-scale biological hotspots in a region in the western side of the Gulf of Maine using remote sensing chlorophyll-a data (from MERIS sensor), and to study the spatial overlap between these hotspots and high concentrations of fish abundance (derived from VTR dataset). For this reason, we defined a new hotspot metric that identified as a hotspot any area that consistently exhibited high-magnitude anomalies through time, a sign of highly dynamic communities. We improved on previous indices by minimizing the effect that different means and variances across space may have on the results, a situation that often occurs when comparing coastal and offshore systems. Results show a significant spatial correlation between pelagic fish abundance and aggregations of primary productivity. Spatial correlations were also significant between benthic fish abundance and primary productivity hotspots, but only during spring months. We argue that this new hotspot index compliments existing global measures as it helps managers understand the dynamic characteristics of a complex marine system. It also provides a unique metric that is easily compared across space and between different trophic levels, which may facilitate future ecosystem-wide studies.

Local Hotspots In The Gulf Of Maine: Spatial Overlap Between Dynamic Aggregations Of Primary Productivity And Fish Abundance

NASA Astrophysics Data System (ADS)

Ribera, M.

2016-12-01

Identification of biological hotspots may be a necessary step toward ecosystem-based management goals, as these often signal underlying processes that aggregate or stimulate resources in a particular location. However, previously used metrics to locate these hotspots are not easily adapted to local marine datasets, in part due to the high spatial and temporal variability of phytoplankton populations. While most fish species in temperate regions are well adapted to the seasonal variability of phytoplankton abundance, it is the variability beyond this predictable pattern (i.e. anomalies) that may heavily impact the abundance and spatial distribution of organisms higher up the food chain. The objective of this study was to identify local-scale biological hotspots in a region in the western side of the Gulf of Maine using remote sensing chlorophyll-a data (from MERIS sensor), and to study the spatial overlap between these hotspots and high concentrations of fish abundance (derived from VTR dataset). For this reason, we defined a new hotspot metric that identified as a hotspot any area that consistently exhibited high-magnitude anomalies through time, a sign of highly dynamic communities. We improved on previous indices by minimizing the effect that different means and variances across space may have on the results, a situation that often occurs when comparing coastal and offshore systems. Results show a significant spatial correlation between pelagic fish abundance and aggregations of primary productivity. Spatial correlations were also significant between benthic fish abundance and primary productivity hotspots, but only during spring months. We argue that this new hotspot index compliments existing global measures as it helps managers understand the dynamic characteristics of a complex marine system. It also provides a unique metric that is easily compared across space and between different trophic levels, which may facilitate future ecosystem-wide studies.

A new macroseismic intensity prediction equation and magnitude estimates of the 1811-1812 New Madrid and 1886 Charleston, South Carolina, earthquakes

NASA Astrophysics Data System (ADS)

Boyd, O. S.; Cramer, C. H.

2013-12-01

We develop an intensity prediction equation (IPE) for the Central and Eastern United States, explore differences between modified Mercalli intensities (MMI) and community internet intensities (CII) and the propensity for reporting, and estimate the moment magnitudes of the 1811-1812 New Madrid, MO, and 1886 Charleston, SC, earthquakes. We constrain the study with North American census data, the National Oceanic and Atmospheric Administration MMI dataset (responses between 1924 and 1985), and the USGS ';Did You Feel It?' CII dataset (responses between June, 2000 and August, 2012). The combined intensity dataset has more than 500,000 felt reports for 517 earthquakes with magnitudes between 2.5 and 7.2. The IPE has the basic form, MMI=c1+c2M+c3exp(λ)+c4λ. where M is moment magnitude and λ is mean log hypocentral distance. Previous IPEs use a limited dataset of MMI, do not differentiate between MMI and CII data in the CEUS, nor account for spatial variations in population. These factors can have an impact at all magnitudes, especially the last factor at large magnitudes and small intensities where the population drops to zero in the Atlantic Ocean and Gulf of Mexico. We assume that the number of reports of a given intensity have hypocentral distances that are log-normally distributed, the distribution of which is modulated by population and the propensity for individuals to report their experience. We do not account for variations in stress drop, regional variations in Q, or distance-dependent geometrical spreading. We simulate the distribution of reports of a given intensity accounting for population and use a grid search method to solve for the fraction of population to report the intensity, the standard deviation of the log-normal distribution and the mean log hypocentral distance, which appears in the above equation. We find that lower intensities, both CII and MMI, are less likely to be reported than greater intensities. Further, there are strong spatial variations in the level of CII reporting. For example, large metropolitan areas appear to have a lower level of reporting relative to rural areas. In general, we find that intensities decrease with increasing distance and decreasing magnitude, as expected. Coefficients for the IPE are c1=1.98×0.13 c2=1.76×0.02 c3=-0.0027×0.0004, and c4=-1.26×0.03. We find significant differences in mean log hypocentral distance between MMI- and CII-based reporting, particularly at smaller mean log distance and higher intensity. Values of mean log distance for CII at high intensity tend to be smaller than for MMI at the same value of intensity. The new IPE leads to magnitude estimates for the 1811-1812 New Madrid earthquakes that are within the broad range of those determined previously. Using three MMI datasets for the New Madrid mainshocks, the new relation results in estimates for the moment magnitudes of the December 16th, 1811, January 23rd, 1812, and February 7th, 1812 mainshocks and December 16th dawn aftershock of 7.1¬¬-7.4, 7.2, 7.5-7.7, and 6.7-7.2, respectively, with a magnitude uncertainty of about ×0.4 units. We estimate a magnitude of 7.0×0.3 for the 1886 Charleston, SC earthquake.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets.

PubMed

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O; Gelfand, Alan E

2016-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online.

Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets

PubMed Central

Datta, Abhirup; Banerjee, Sudipto; Finley, Andrew O.; Gelfand, Alan E.

2018-01-01

Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations become large. This article develops a class of highly scalable nearest-neighbor Gaussian process (NNGP) models to provide fully model-based inference for large geostatistical datasets. We establish that the NNGP is a well-defined spatial process providing legitimate finite-dimensional Gaussian densities with sparse precision matrices. We embed the NNGP as a sparsity-inducing prior within a rich hierarchical modeling framework and outline how computationally efficient Markov chain Monte Carlo (MCMC) algorithms can be executed without storing or decomposing large matrices. The floating point operations (flops) per iteration of this algorithm is linear in the number of spatial locations, thereby rendering substantial scalability. We illustrate the computational and inferential benefits of the NNGP over competing methods using simulation studies and also analyze forest biomass from a massive U.S. Forest Inventory dataset at a scale that precludes alternative dimension-reducing methods. Supplementary materials for this article are available online. PMID:29720777

SAGEMAP: A web-based spatial dataset for sage grouse and sagebrush steppe management in the Intermountain West

USGS Publications Warehouse

Knick, Steven T.; Schueck, Linda

2002-01-01

The Snake River Field Station of the Forest and Rangeland Ecosystem Science Center has developed and now maintains a database of the spatial information needed to address management of sage grouse and sagebrush steppe habitats in the western United States. The SAGEMAP project identifies and collects infor-mation for the region encompassing the historical extent of sage grouse distribution. State and federal agencies, the primary entities responsible for managing sage grouse and their habitats, need the information to develop an objective assessment of the current status of sage grouse populations and their habitats, or to provide responses and recommendations for recovery if sage grouse are listed as a Threatened or Endangered Species. The spatial data on the SAGEMAP website (http://SAGEMAP.wr.usgs.gov) are an important component in documenting current habitat and other environmental conditions. In addition, the data can be used to identify areas that have undergone significant changes in land cover and to determine underlying causes. As such, the database permits an analysis for large-scale and range-wide factors that may be causing declines of sage grouse populations. The spatial data contained on this site also will be a critical component guiding the decision processes for restoration of habitats in the Great Basin. Therefore, development of this database and the capability to disseminate the information carries multiple benefits for land and wildlife management.

What are we ‘tweeting’ about obesity? Mapping tweets with Topic Modeling and Geographic Information System

PubMed Central

Ghosh, Debarchana (Debs); Guha, Rajarshi

2014-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are ‘food deserts’, ‘fast food’, and ‘childhood obesity’. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as ‘childhood obesity and schools’, ‘obesity prevention’, and ‘obesity and food habits’ are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets. PMID:25126022

What are we 'tweeting' about obesity? Mapping tweets with Topic Modeling and Geographic Information System.

PubMed

Ghosh, Debarchana Debs; Guha, Rajarshi

2013-01-01

Public health related tweets are difficult to identify in large conversational datasets like Twitter.com. Even more challenging is the visualization and analyses of the spatial patterns encoded in tweets. This study has the following objectives: How can topic modeling be used to identify relevant public health topics such as obesity on Twitter.com? What are the common obesity related themes? What is the spatial pattern of the themes? What are the research challenges of using large conversational datasets from social networking sites? Obesity is chosen as a test theme to demonstrate the effectiveness of topic modeling using Latent Dirichlet Allocation (LDA) and spatial analysis using Geographic Information System (GIS). The dataset is constructed from tweets (originating from the United States) extracted from Twitter.com on obesity-related queries. Examples of such queries are 'food deserts', 'fast food', and 'childhood obesity'. The tweets are also georeferenced and time stamped. Three cohesive and meaningful themes such as 'childhood obesity and schools', 'obesity prevention', and 'obesity and food habits' are extracted from the LDA model. The GIS analysis of the extracted themes show distinct spatial pattern between rural and urban areas, northern and southern states, and between coasts and inland states. Further, relating the themes with ancillary datasets such as US census and locations of fast food restaurants based upon the location of the tweets in a GIS environment opened new avenues for spatial analyses and mapping. Therefore the techniques used in this study provide a possible toolset for computational social scientists in general and health researchers in specific to better understand health problems from large conversational datasets.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

NASA Astrophysics Data System (ADS)

Kishore, P.; Jyothi, S.; Basha, Ghouse; Rao, S. V. B.; Rajeevan, M.; Velicogna, Isabella; Sutterley, Tyler C.

2016-01-01

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial-temporal analyses of rainfall have been studied by using 107 (1901-2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

Geostatistical Characterization of Cereal Leaf Beetle (Coleoptera: Chrysomelidae) Distributions in Wheat.

PubMed

Reay-Jones, Francis P F

2017-08-01

A 3-yr study was conducted in wheat, Triticum aestivum L., in South Carolina to characterize the spatial distribution of Oulema melanopus (L.) adults, eggs, and larvae using semivariograms, which provides a measure of spatial dependence among sampling data. Moran's I coefficients for peak densities of each life stage indicated significant positive autocorrelation for seven (two for eggs, one for larvae, and four for adults) of the 16 datasets. Aggregation was detected in 13 of these 16 datasets when analyzed by semivariogram modeling, with spherical, Gaussian, and exponential models best fitting for eight, four, and one dataset, respectively, and with models for two datasets having only one parameter (nugget) significantly different from zero. The nugget-to-sill ratios ranged from 0.043 to 0.774, and indicated strong spatial dependence in six models (three for adults, two for eggs, and one for larvae), moderate spatial dependence in six models (three for adults and six for eggs), and weak spatial dependence in one model (adults). Range values varied from 39.1 m to 234.1 m, with an average of 120.1 ± 14.0 m. Average range values were 104.9, 135.2, and 161.2 m for adults, eggs, and larvae, respectively. Because the majority of semivariogram models in our study indicated aggregated distributions, spatial sampling will provide more information than nonspatial random sampling. Developing our understanding of spatial dependence of crop pests is needed to optimize sampling plans and can provide a basis for exploring site-specific management tactics. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

NASA Technical Reports Server (NTRS)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

Spatially-explicit estimation of geographical representation in large-scale species distribution datasets.

PubMed

Kalwij, Jesse M; Robertson, Mark P; Ronk, Argo; Zobel, Martin; Pärtel, Meelis

2014-01-01

Much ecological research relies on existing multispecies distribution datasets. Such datasets, however, can vary considerably in quality, extent, resolution or taxonomic coverage. We provide a framework for a spatially-explicit evaluation of geographical representation within large-scale species distribution datasets, using the comparison of an occurrence atlas with a range atlas dataset as a working example. Specifically, we compared occurrence maps for 3773 taxa from the widely-used Atlas Florae Europaeae (AFE) with digitised range maps for 2049 taxa of the lesser-known Atlas of North European Vascular Plants. We calculated the level of agreement at a 50-km spatial resolution using average latitudinal and longitudinal species range, and area of occupancy. Agreement in species distribution was calculated and mapped using Jaccard similarity index and a reduced major axis (RMA) regression analysis of species richness between the entire atlases (5221 taxa in total) and between co-occurring species (601 taxa). We found no difference in distribution ranges or in the area of occupancy frequency distribution, indicating that atlases were sufficiently overlapping for a valid comparison. The similarity index map showed high levels of agreement for central, western, and northern Europe. The RMA regression confirmed that geographical representation of AFE was low in areas with a sparse data recording history (e.g., Russia, Belarus and the Ukraine). For co-occurring species in south-eastern Europe, however, the Atlas of North European Vascular Plants showed remarkably higher richness estimations. Geographical representation of atlas data can be much more heterogeneous than often assumed. Level of agreement between datasets can be used to evaluate geographical representation within datasets. Merging atlases into a single dataset is worthwhile in spite of methodological differences, and helps to fill gaps in our knowledge of species distribution ranges. Species distribution dataset mergers, such as the one exemplified here, can serve as a baseline towards comprehensive species distribution datasets.

Datasets, Technologies and Products from the NASA/NOAA Electronic Theater 2002

NASA Technical Reports Server (NTRS)

Hasler, A. Fritz; Starr, David (Technical Monitor)

2001-01-01

An in depth look at the Earth Science datasets used in the Etheater Visualizations will be presented. This will include the satellite orbits, platforms, scan patterns, the size, temporal and spatial resolution, and compositing techniques used to obtain the datasets as well as the spectral bands utilized.

Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area

USGS Publications Warehouse

McKinney, Tim S.; Anning, David W.

2012-01-01

This product "Digital spatial data for observed, predicted, and misclassification errors for observations in the training dataset for nitrate and arsenic concentrations in basin-fill aquifers in the Southwest Principal Aquifers study area" is a 1:250,000-scale point spatial dataset developed as part of a regional Southwest Principal Aquifers (SWPA) study (Anning and others, 2012). The study examined the vulnerability of basin-fill aquifers in the southwestern United States to nitrate contamination and arsenic enrichment. Statistical models were developed by using the random forest classifier algorithm to predict concentrations of nitrate and arsenic across a model grid that represents local- and basin-scale measures of source, aquifer susceptibility, and geochemical conditions.

Data Basin Aquatic Center: expanding access to aquatic conservation data, analysis tools, people and practical answers

NASA Astrophysics Data System (ADS)

Osborne-Gowey, J.; Strittholt, J.; Bergquist, J.; Ward, B. C.; Sheehan, T.; Comendant, T.; Bachelet, D. M.

2009-12-01

The world’s aquatic resources are experiencing anthropogenic pressures on an unprecedented scale and aquatic organisms are experiencing widespread population changes and ecosystem-scale habitat alterations. Climate change is likely to exacerbate these threats, in some cases reducing the range of native North American fishes by 20-100% (depending on the location of the population and the model assumptions). Scientists around the globe are generating large volumes of data that vary in quality, format, supporting documentation, and accessibility. Moreover, diverse models are being run at various temporal and spatial scales as scientists attempt to understand previous (and project future) human impacts to aquatic species and their habitats. Conservation scientists often struggle to synthesize this wealth of information for developing practical on-the-ground management strategies. As a result, the best available science is often not utilized in the decision-making and adaptive management processes. As aquatic conservation problems around the globe become more serious and the demand to solve them grows more urgent, scientists and land-use managers need a new way to bring strategic, science-based, and action-oriented approaches to aquatic conservation. The Conservation Biology Institute (CBI), with partners such as ESRI, is developing an Aquatic Center as part of a dynamic, web-based resource (Data Basin; http: databasin.org) that centralizes usable aquatic datasets and provides analytical tools to visualize, analyze, and communicate findings for practical applications. To illustrate its utility, we present example datasets of varying spatial scales and synthesize multiple studies to arrive at novel solutions to aquatic threats.

Assessing the quality of open spatial data for mobile location-based services research and applications

NASA Astrophysics Data System (ADS)

Ciepłuch, C.; Mooney, P.; Jacob, R.; Zheng, J.; Winstanely, A. C.

2011-12-01

New trends in GIS such as Volunteered Geographical Information (VGI), Citizen Science, and Urban Sensing, have changed the shape of the geoinformatics landscape. The OpenStreetMap (OSM) project provided us with an exciting, evolving, free and open solution as a base dataset for our geoserver and spatial data provider for our research. OSM is probably the best known and best supported example of VGI and user generated spatial content on the Internet. In this paper we will describe current results from the development of quality indicators for measures for OSM data. Initially we have analysed the Ireland OSM data in grid cells (5km) to gather statistical data about the completeness, accuracy, and fitness for purpose of the underlying spatial data. This analysis included: density of user contributions, spatial density of points and polygons, types of tags and metadata used, dominant contributors in a particular area or for a particular geographic feature type, etc. There greatest OSM activity and spatial data density is highly correlated with centres of large population. The ability to quantify and assess if VGI, such as OSM, is of sufficient quality for mobile mapping applications and Location-based services is critical to the future success of VGI as a spatial data source for these technologies.

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

DOE PAGES

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya; ...

2018-04-06

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. Here, the reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. Here, the reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

Reconstruction of global gridded monthly sectoral water withdrawals for 1971–2010 and analysis of their spatiotemporal patterns

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Zhongwei; Hejazi, Mohamad; Li, Xinya

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scales, due to a lack of observations at seasonal and local scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5°) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e., irrigation,more » domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that total global water withdrawal has increased significantly during 1971–2010, mainly driven by the increase in irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of total annual irrigation water withdrawal in mid- and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual timescales. The reconstructed gridded water withdrawal dataset is open access, and can be used for examining issues related to water withdrawals at fine spatial, temporal, and sectoral scales.« less

High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020

PubMed Central

Sorichetta, Alessandro; Hornby, Graeme M.; Stevens, Forrest R.; Gaughan, Andrea E.; Linard, Catherine; Tatem, Andrew J.

2015-01-01

The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100 m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository. PMID:26347245

High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020.

PubMed

Sorichetta, Alessandro; Hornby, Graeme M; Stevens, Forrest R; Gaughan, Andrea E; Linard, Catherine; Tatem, Andrew J

2015-01-01

The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100 m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository.

Applications of spatial statistical network models to stream data

USGS Publications Warehouse

Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal

2014-01-01

Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

IPUMS: Detailed global data on population characteristics

NASA Astrophysics Data System (ADS)

Kugler, T.

2017-12-01

Many new and exciting sources of data on human population distributions based on remote sensing, mobile technology, and other mechanisms are becoming available. These new data sources often provide fine scale spatial and/or temporal resolution. However, they typically focus on the location of population, with little or no information on population characteristics. The large and growing collection of data available through the IPUMS family of products complements datasets that provide spatial and temporal detail but little attribute detail by providing the full depth of characteristics covered by population censuses, including demographic, household structure, economic, employment, education, and housing characteristics. IPUMS International provides census microdata for 85 countries. Microdata provide the responses to every census question for each individual in a sample of households. Microdata identify the sub-national geographic unit in which a household is located, but for confidentiality reasons, identified units must include a minimum population, typically 20,000 people. Small-area aggregate data often describe much smaller geographic units, enabling study of detailed spatial patterns of population characteristics. However the structure of aggregate data tables is highly heterogeneous across countries, census years, and even topics within a given census, making these data difficult to work with in any systematic way. A recently funded project will assemble small-area aggregate population and agricultural census data published by national statistical offices. Through preliminary work collecting and cataloging over 10,000 tables, we have identified a small number of structural families that can be used to organize the many different structures. These structural families will form the basis for software tools to document and standardize the tables for ingest into a common database. Both the microdata and aggregate data are made available through IPUMS Terra, facilitating integration with land use, land cover, climate, and other environmental data. These data can be used to address pressing global challenges, such as food and water security, development and deforestation, and environmentally-influenced migration.

Effect of Variable Spatial Scales on USLE-GIS Computations

NASA Astrophysics Data System (ADS)

Patil, R. J.; Sharma, S. K.

2017-12-01

Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

Recent vegetation phenology variability and wild reindeer migration in Hardangervidda plateau (Norway)

NASA Astrophysics Data System (ADS)

Courault, Romain; Franclet, Alexiane; Bourrand, Kévin; Bilodeau, Clélia; Saïd, Sonia; Cohen, Marianne

2018-05-01

More than others, arctic ecosystems are affected by consequences of global climate changes. The herbivorous plays numerous roles both in Scandinavian natural and cultural landscapes (Forbes et al., 2007). Wild reindeer (Rangifer tarandus L.) herds in Hardangervidda plateau (Norway) constitute one of the isolated populations along Fennoscandia mountain range. The study aims to understand temporal and spatial variability of intra- and inter-annual home ranges extent and geophysical properties. We then characterize phenological variability with Corine Land Cover ecological habitat assessment and bi-monthly NDVI index (MODIS 13Q1, 250 m). Thirdly, we test relationships between reindeer's estimated densities and geophysical factors. All along the study, a Python toolbox ("GRiD") has been mounted and refined to fit with biogeographical expectancies. The toolbox let user's choice of inputs and facilitate then the gathering of raster datasets with given spatial extent of clipping and resolution. The grid generation and cells extraction gives one tabular output, allowing then to easily compute complex geostatistical analysis with regular spreadsheets. Results are based on reindeer's home ranges, associated extent (MODIS tile) and spatial resolution (250 m). Spatial mismatch of 0.6 % has been found between ecological habitat when comparing raw (100 m2) and new dataset (250 m2). Inter-annual home ranges analysis describes differences between inter-seasonal migrations (early spring, end of the summer) and calving or capitalizing times. For intra-annual home ranges, significant correlations have been found between reindeer's estimated densities and both altitudes and phenology. GRiD performance and biogeographical results suggests 1) to enhance geometric accuracy 2) better examine links between estimated densities and NDVI.

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Yan; Piao, Shilong; Huang, Mengtian

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Global patterns and climate drivers of water-use efficiency in terrestrial ecosystems deduced from satellite-based datasets and carbon cycle models

DOE PAGES

Sun, Yan; Piao, Shilong; Huang, Mengtian; ...

2015-12-23

Our aim is to investigate how ecosystem water-use efficiency (WUE) varies spatially under different climate conditions, and how spatial variations in WUE differ from those of transpiration-based water-use efficiency (WUE t) and transpiration-based inherent water-use efficiency (IWUE t). LocationGlobal terrestrial ecosystems. We investigated spatial patterns of WUE using two datasets of gross primary productivity (GPP) and evapotranspiration (ET) and four biosphere model estimates of GPP and ET. Spatial relationships between WUE and climate variables were further explored through regression analyses. Global WUE estimated by two satellite-based datasets is 1.9 ± 0.1 and 1.8 ± 0.6g C m -2mm -1 lowermore » than the simulations from four process-based models (2.0 ± 0.3g C m -2mm -1) but comparable within the uncertainty of both approaches. In both satellite-based datasets and process models, precipitation is more strongly associated with spatial gradients of WUE for temperate and tropical regions, but temperature dominates north of 50 degrees N. WUE also increases with increasing solar radiation at high latitudes. The values of WUE from datasets and process-based models are systematically higher in wet regions (with higher GPP) than in dry regions. WUE t shows a lower precipitation sensitivity than WUE, which is contrary to leaf- and plant-level observations. IWUE t, the product of WUE t and water vapour deficit, is found to be rather conservative with spatially increasing precipitation, in agreement with leaf- and plant-level measurements. In conclusion, WUE, WUE t and IWUE t produce different spatial relationships with climate variables. In dry ecosystems, water losses from evaporation from bare soil, uncorrelated with productivity, tend to make WUE lower than in wetter regions. Yet canopy conductance is intrinsically efficient in those ecosystems and maintains a higher IWUEt. This suggests that the responses of each component flux of evapotranspiration should be analysed separately when investigating regional gradients in WUE, its temporal variability and its trends.« less

Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects.

PubMed

Dong, Ni; Huang, Helai; Zheng, Liang

2015-09-01

In zone-level crash prediction, accounting for spatial dependence has become an extensively studied topic. This study proposes Support Vector Machine (SVM) model to address complex, large and multi-dimensional spatial data in crash prediction. Correlation-based Feature Selector (CFS) was applied to evaluate candidate factors possibly related to zonal crash frequency in handling high-dimension spatial data. To demonstrate the proposed approaches and to compare them with the Bayesian spatial model with conditional autoregressive prior (i.e., CAR), a dataset in Hillsborough county of Florida was employed. The results showed that SVM models accounting for spatial proximity outperform the non-spatial model in terms of model fitting and predictive performance, which indicates the reasonableness of considering cross-zonal spatial correlations. The best model predictive capability, relatively, is associated with the model considering proximity of the centroid distance by choosing the RBF kernel and setting the 10% of the whole dataset as the testing data, which further exhibits SVM models' capacity for addressing comparatively complex spatial data in regional crash prediction modeling. Moreover, SVM models exhibit the better goodness-of-fit compared with CAR models when utilizing the whole dataset as the samples. A sensitivity analysis of the centroid-distance-based spatial SVM models was conducted to capture the impacts of explanatory variables on the mean predicted probabilities for crash occurrence. While the results conform to the coefficient estimation in the CAR models, which supports the employment of the SVM model as an alternative in regional safety modeling. Copyright © 2015 Elsevier Ltd. All rights reserved.

Social dataset analysis and mapping tools for Risk Perception: resilience, people preparation and communication tools

NASA Astrophysics Data System (ADS)

Peters-Guarin, Graciela; Garcia, Carolina; Frigerio, Simone

2010-05-01

Perception has been identified as resource and part of the resilience of a community to disasters. Risk perception, if present, may determine the potential damage a household or community experience. Different levels of risk perception and preparedness can influence directly people's susceptibility and the way they might react in case of an emergency caused by natural hazards. In spite of the profuse literature about risk perception, works to spatially portray this feature are really scarce. The spatial relationship to danger or hazard is being recognised as an important factor of the risk equation; it can be used as a powerful tool either for better knowledge or for operational reasons (e.g. management of preventive information). Risk perception and people's awareness when displayed in a spatial format can be useful for several actors in the risk management arena. Local authorities and civil protection can better address educational activities to increase the preparation of particularly vulnerable groups of clusters of households within a community. It can also be useful for the emergency personal in order to optimally direct the actions in case of an emergency. In the framework of the Marie Curie Research Project, a Community Based Early Warning System (CBEWS) it's been developed in the Mountain Community Valtellina of Tirano, northern Italy. This community has been continuously exposed to different mass movements and floods, in particular, a large event in 1987 which affected a large portion of the valley and left 58 dead. The actual emergency plan for the study area is composed by a real time, highly detailed, decision support system. This emergency plan contains detailed instructions for the rapid deployment of civil protection and other emergency personal in case of emergency, for risk scenarios previously defined. Especially in case of a large event, where timely reaction is crucial for reducing casualties, it is important for those in charge of emergency management, to know in advance the different levels of risk perception and preparedness existing among several sectors of the population. Knowing where the most vulnerable population is located may optimize the use of resources, better direct the initial efforts and organize the evacuation and attention procedures. As part of the CBEWS, a comprehensive survey was applied in the study area to measure, among others features, the levels of risk perception, preparation and information received about natural hazards. After a statistical and direct analysis on a complete social dataset recorded, a spatial information distribution is actually in progress. Based on boundaries features (municipalities and sub-districts) of Italian Institute of Statistics (ISTAT), a local scale background has been granted (a private address level is not accessible for privacy rules so the local districts-ID inside municipality has been the detail level performed) and a spatial location of the surveyed population has been completed. The geometric component has been defined and actually it is possible to create a local distribution of social parameters derived from perception questionnaries results. A lot of raw information and social-statistical analysis offer different mirror and "visual concept" of risk perception. For this reason a concrete complete GeoDB is under working for the complete organization of the dataset. By a technical point of view the environment for data sharing is based on a complete open source web-service environment, to offer manually-made and user-friendly interface to this kind of information. Final aim is to offer different switches of dataset, using the same scale prototype and data hierarchical structure, to provide and compare social location of risk perception in the most detailed level.

Space-for-Time Substitution Works in Everglades Ecological Forecasting Models

PubMed Central

Banet, Amanda I.; Trexler, Joel C.

2013-01-01

Space-for-time substitution is often used in predictive models because long-term time-series data are not available. Critics of this method suggest factors other than the target driver may affect ecosystem response and could vary spatially, producing misleading results. Monitoring data from the Florida Everglades were used to test whether spatial data can be substituted for temporal data in forecasting models. Spatial models that predicted bluefin killifish (Lucania goodei) population response to a drying event performed comparably and sometimes better than temporal models. Models worked best when results were not extrapolated beyond the range of variation encompassed by the original dataset. These results were compared to other studies to determine whether ecosystem features influence whether space-for-time substitution is feasible. Taken in the context of other studies, these results suggest space-for-time substitution may work best in ecosystems with low beta-diversity, high connectivity between sites, and small lag in organismal response to the driver variable. PMID:24278368

Visualizing Time-Varying Distribution Data in EOS Application

NASA Technical Reports Server (NTRS)

Shen, Han-Wei

2004-01-01

In this research, we have developed several novel visualization methods for spatial probability density function data. Our focus has been on 2D spatial datasets, where each pixel is a random variable, and has multiple samples which are the results of experiments on that random variable. We developed novel clustering algorithms as a means to reduce the information contained in these datasets; and investigated different ways of interpreting and clustering the data.

Architecture of the local spatial data infrastructure for regional climate change research

NASA Astrophysics Data System (ADS)

Titov, Alexander; Gordov, Evgeny

2013-04-01

Georeferenced datasets (meteorological databases, modeling and reanalysis results, etc.) are actively used in modeling and analysis of climate change for various spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their size which might constitute up to tens terabytes for a single dataset studies in the area of climate and environmental change require a special software support based on SDI approach. A dedicated architecture of the local spatial data infrastructure aiming at regional climate change analysis using modern web mapping technologies is presented. Geoportal is a key element of any SDI, allowing searching of geoinformation resources (datasets and services) using metadata catalogs, producing geospatial data selections by their parameters (data access functionality) as well as managing services and applications of cartographical visualization. It should be noted that due to objective reasons such as big dataset volume, complexity of data models used, syntactic and semantic differences of various datasets, the development of environmental geodata access, processing and visualization services turns out to be quite a complex task. Those circumstances were taken into account while developing architecture of the local spatial data infrastructure as a universal framework providing geodata services. So that, the architecture presented includes: 1. Effective in terms of search, access, retrieval and subsequent statistical processing, model of storing big sets of regional georeferenced data, allowing in particular to store frequently used values (like monthly and annual climate change indices, etc.), thus providing different temporal views of the datasets 2. General architecture of the corresponding software components handling geospatial datasets within the storage model 3. Metadata catalog describing in detail using ISO 19115 and CF-convention standards datasets used in climate researches as a basic element of the spatial data infrastructure as well as its publication according to OGC CSW (Catalog Service Web) specification 4. Computational and mapping web services to work with geospatial datasets based on OWS (OGC Web Services) standards: WMS, WFS, WPS 5. Geoportal as a key element of thematic regional spatial data infrastructure providing also software framework for dedicated web applications development To realize web mapping services Geoserver software is used since it provides natural WPS implementation as a separate software module. To provide geospatial metadata services GeoNetwork Opensource (http://geonetwork-opensource.org) product is planned to be used for it supports ISO 19115/ISO 19119/ISO 19139 metadata standards as well as ISO CSW 2.0 profile for both client and server. To implement thematic applications based on geospatial web services within the framework of local SDI geoportal the following open source software have been selected: 1. OpenLayers JavaScript library, providing basic web mapping functionality for the thin client such as web browser 2. GeoExt/ExtJS JavaScript libraries for building client-side web applications working with geodata services. The web interface developed will be similar to the interface of such popular desktop GIS applications, as uDIG, QuantumGIS etc. The work is partially supported by RF Ministry of Education and Science grant 8345, SB RAS Program VIII.80.2.1 and IP 131.

An empirical understanding of triple collocation evaluation measure

NASA Astrophysics Data System (ADS)

Scipal, Klaus; Doubkova, Marcela; Hegyova, Alena; Dorigo, Wouter; Wagner, Wolfgang

2013-04-01

Triple collocation method is an advanced evaluation method that has been used in the soil moisture field for only about half a decade. The method requires three datasets with an independent error structure that represent an identical phenomenon. The main advantages of the method are that it a) doesn't require a reference dataset that has to be considered to represent the truth, b) limits the effect of random and systematic errors of other two datasets, and c) simultaneously assesses the error of three datasets. The objective of this presentation is to assess the triple collocation error (Tc) of the ASAR Global Mode Surface Soil Moisture (GM SSM 1) km dataset and highlight problems of the method related to its ability to cancel the effect of error of ancillary datasets. In particular, the goal is to a) investigate trends in Tc related to the change in spatial resolution from 5 to 25 km, b) to investigate trends in Tc related to the choice of a hydrological model, and c) to study the relationship between Tc and other absolute evaluation methods (namely RMSE and Error Propagation EP). The triple collocation method is implemented using ASAR GM, AMSR-E, and a model (either AWRA-L, GLDAS-NOAH, or ERA-Interim). First, the significance of the relationship between the three soil moisture datasets was tested that is a prerequisite for the triple collocation method. Second, the trends in Tc related to the choice of the third reference dataset and scale were assessed. For this purpose the triple collocation is repeated replacing AWRA-L with two different globally available model reanalysis dataset operating at different spatial resolution (ERA-Interim and GLDAS-NOAH). Finally, the retrieved results were compared to the results of the RMSE and EP evaluation measures. Our results demonstrate that the Tc method does not eliminate the random and time-variant systematic errors of the second and the third dataset used in the Tc. The possible reasons include the fact a) that the TC method could not fully function with datasets acting at very different spatial resolutions, or b) that the errors were not fully independent as initially assumed.

High spatial resolution mapping of folds and fractures using Unmanned Aerial Vehicle (UAV) photogrammetry

NASA Astrophysics Data System (ADS)

Cruden, A. R.; Vollgger, S.

2016-12-01

The emerging capability of UAV photogrammetry combines a simple and cost-effective method to acquire digital aerial images with advanced computer vision algorithms that compute spatial datasets from a sequence of overlapping digital photographs from various viewpoints. Depending on flight altitude and camera setup, sub-centimeter spatial resolution orthophotographs and textured dense point clouds can be achieved. Orientation data can be collected for detailed structural analysis by digitally mapping such high-resolution spatial datasets in a fraction of time and with higher fidelity compared to traditional mapping techniques. Here we describe a photogrammetric workflow applied to a structural study of folds and fractures within alternating layers of sandstone and mudstone at a coastal outcrop in SE Australia. We surveyed this location using a downward looking digital camera mounted on commercially available multi-rotor UAV that autonomously followed waypoints at a set altitude and speed to ensure sufficient image overlap, minimum motion blur and an appropriate resolution. The use of surveyed ground control points allowed us to produce a geo-referenced 3D point cloud and an orthophotograph from hundreds of digital images at a spatial resolution < 10 mm per pixel, and cm-scale location accuracy. Orientation data of brittle and ductile structures were semi-automatically extracted from these high-resolution datasets using open-source software. This resulted in an extensive and statistically relevant orientation dataset that was used to 1) interpret the progressive development of folds and faults in the region, and 2) to generate a 3D structural model that underlines the complex internal structure of the outcrop and quantifies spatial variations in fold geometries. Overall, our work highlights how UAV photogrammetry can contribute to new insights in structural analysis.

Spatial-Temporal Distribution of Hantavirus Rodent-Borne Infection by Oligoryzomys fulvescens in the Agua Buena Region - Panama

PubMed Central

Gonzalez, Publio; Cumbrera, Alberto; Rivero, Alina; Avila, Mario; Armién, Aníbal G.; Koster, Frederick; Glass, Gregory

2016-01-01

Background Hotspot detection and characterization has played an increasing role in understanding the maintenance and transmission of zoonotic pathogens. Identifying the specific environmental factors (or their correlates) that influence reservoir host abundance help increase understanding of how pathogens are maintained in natural systems and are crucial to identifying disease risk. However, most recent studies are performed at macro-scale and describe broad temporal patterns of population abundances. Few have been conducted at a microscale over short time periods that better capture the dynamical patterns of key populations. These finer resolution studies may better define the likelihood of local pathogen persistence. This study characterizes the landscape distribution and spatio-temporal dynamics of Oligoryzomys fulvescens (O. fulvescens), an important mammalian reservoir in Central America. Methods Information collected in a longitudinal study of rodent populations in the community of Agua Buena in Tonosí, Panama, between April 2006 and December 2009 was analyzed using non-spatial analyses (box plots) and explicit spatial statistical tests (correlograms, SADIE and LISA). A 90 node grid was built (raster format) to design a base map. The area between the nodes was 0.09 km2 and the total study area was 6.43 km2 (2.39 x 2.69 km). The temporal assessment dataset was divided into four periods for each year studied: the dry season, rainy season, and two months-long transitions between seasons (the months of April and December). Results There were heterogeneous patterns in the population densities and degrees of dispersion of O. fulvescens that varied across seasons and among years. The species typically was locally absent during the late transitional months of the season, and re-established locally in subsequent years. These populations re-occurred in the same area during the first three years but subsequently re-established further south in the final year of the study. Spatial autocorrelation analyses indicated local populations encompassed approximately 300–600 m. The borders between suitable and unsuitable habitats were sharply demarcated over short distances. Conclusion Oligoryzomys fulvescens showed a well-defined spatial pattern that evolved over time, and led to a pattern of changing aggregation. Thus, hot spots of abundance showed a general shifting pattern that helps explain the intermittent risk from pathogens transmitted by this species. This variation was associated with seasonality, as well as anthropogenic pressures that occurred with agricultural activities. These factors help define the characteristics of the occurrence, timing, intensity and duration of synanthropic populations affected by human populations and, consequently, possible exposure that local human populations experience. PMID:26894436

Spatial-Temporal Distribution of Hantavirus Rodent-Borne Infection by Oligoryzomys fulvescens in the Agua Buena Region--Panama.

PubMed

Armién, Blas; Ortiz, Paulo Lazaro; Gonzalez, Publio; Cumbrera, Alberto; Rivero, Alina; Avila, Mario; Armién, Aníbal G; Koster, Frederick; Glass, Gregory

2016-02-01

Hotspot detection and characterization has played an increasing role in understanding the maintenance and transmission of zoonotic pathogens. Identifying the specific environmental factors (or their correlates) that influence reservoir host abundance help increase understanding of how pathogens are maintained in natural systems and are crucial to identifying disease risk. However, most recent studies are performed at macro-scale and describe broad temporal patterns of population abundances. Few have been conducted at a microscale over short time periods that better capture the dynamical patterns of key populations. These finer resolution studies may better define the likelihood of local pathogen persistence. This study characterizes the landscape distribution and spatio-temporal dynamics of Oligoryzomys fulvescens (O. fulvescens), an important mammalian reservoir in Central America. Information collected in a longitudinal study of rodent populations in the community of Agua Buena in Tonosí, Panama, between April 2006 and December 2009 was analyzed using non-spatial analyses (box plots) and explicit spatial statistical tests (correlograms, SADIE and LISA). A 90 node grid was built (raster format) to design a base map. The area between the nodes was 0.09 km(2) and the total study area was 6.43 km(2) (2.39 x 2.69 km). The temporal assessment dataset was divided into four periods for each year studied: the dry season, rainy season, and two months-long transitions between seasons (the months of April and December). There were heterogeneous patterns in the population densities and degrees of dispersion of O. fulvescens that varied across seasons and among years. The species typically was locally absent during the late transitional months of the season, and re-established locally in subsequent years. These populations re-occurred in the same area during the first three years but subsequently re-established further south in the final year of the study. Spatial autocorrelation analyses indicated local populations encompassed approximately 300-600 m. The borders between suitable and unsuitable habitats were sharply demarcated over short distances. Oligoryzomys fulvescens showed a well-defined spatial pattern that evolved over time, and led to a pattern of changing aggregation. Thus, hot spots of abundance showed a general shifting pattern that helps explain the intermittent risk from pathogens transmitted by this species. This variation was associated with seasonality, as well as anthropogenic pressures that occurred with agricultural activities. These factors help define the characteristics of the occurrence, timing, intensity and duration of synanthropic populations affected by human populations and, consequently, possible exposure that local human populations experience.

Providing Geographic Datasets as Linked Data in Sdi

NASA Astrophysics Data System (ADS)

Hietanen, E.; Lehto, L.; Latvala, P.

2016-06-01

In this study, a prototype service to provide data from Web Feature Service (WFS) as linked data is implemented. At first, persistent and unique Uniform Resource Identifiers (URI) are created to all spatial objects in the dataset. The objects are available from those URIs in Resource Description Framework (RDF) data format. Next, a Web Ontology Language (OWL) ontology is created to describe the dataset information content using the Open Geospatial Consortium's (OGC) GeoSPARQL vocabulary. The existing data model is modified in order to take into account the linked data principles. The implemented service produces an HTTP response dynamically. The data for the response is first fetched from existing WFS. Then the Geographic Markup Language (GML) format output of the WFS is transformed on-the-fly to the RDF format. Content Negotiation is used to serve the data in different RDF serialization formats. This solution facilitates the use of a dataset in different applications without replicating the whole dataset. In addition, individual spatial objects in the dataset can be referred with URIs. Furthermore, the needed information content of the objects can be easily extracted from the RDF serializations available from those URIs. A solution for linking data objects to the dataset URI is also introduced by using the Vocabulary of Interlinked Datasets (VoID). The dataset is divided to the subsets and each subset is given its persistent and unique URI. This enables the whole dataset to be explored with a web browser and all individual objects to be indexed by search engines.

Architectural Implications for Spatial Object Association Algorithms*

PubMed Central

Kumar, Vijay S.; Kurc, Tahsin; Saltz, Joel; Abdulla, Ghaleb; Kohn, Scott R.; Matarazzo, Celeste

2013-01-01

Spatial object association, also referred to as crossmatch of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server®, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST). PMID:25692244

NASA Cold Land Processes Experiment (CLPX 2002/03): Atmospheric analyses datasets

Treesearch

Glen E. Liston; Daniel L. Birkenheuer; Christopher A. Hiemstra; Donald W. Cline; Kelly Elder

2008-01-01

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over...

geoknife: Reproducible web-processing of large gridded datasets

USGS Publications Warehouse

Read, Jordan S.; Walker, Jordan I.; Appling, Alison P.; Blodgett, David L.; Read, Emily K.; Winslow, Luke A.

2016-01-01

Geoprocessing of large gridded data according to overlap with irregular landscape features is common to many large-scale ecological analyses. The geoknife R package was created to facilitate reproducible analyses of gridded datasets found on the U.S. Geological Survey Geo Data Portal web application or elsewhere, using a web-enabled workflow that eliminates the need to download and store large datasets that are reliably hosted on the Internet. The package provides access to several data subset and summarization algorithms that are available on remote web processing servers. Outputs from geoknife include spatial and temporal data subsets, spatially-averaged time series values filtered by user-specified areas of interest, and categorical coverage fractions for various land-use types.

Survey-based socio-economic data from slums in Bangalore, India

NASA Astrophysics Data System (ADS)

Roy, Debraj; Palavalli, Bharath; Menon, Niveditha; King, Robin; Pfeffer, Karin; Lees, Michael; Sloot, Peter M. A.

2018-01-01

In 2010, an estimated 860 million people were living in slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 slums across the city. The slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries.

A dataset of multiresolution functional brain parcellations in an elderly population with no or mild cognitive impairment.

PubMed

Tam, Angela; Dansereau, Christian; Badhwar, AmanPreet; Orban, Pierre; Belleville, Sylvie; Chertkow, Howard; Dagher, Alain; Hanganu, Alexandru; Monchi, Oury; Rosa-Neto, Pedro; Shmuel, Amir; Breitner, John; Bellec, Pierre

2016-12-01

We present group eight resolutions of brain parcellations for clusters generated from resting-state functional magnetic resonance images for 99 cognitively normal elderly persons and 129 patients with mild cognitive impairment, pooled from four independent datasets. This dataset was generated as part of the following study: Common Effects of Amnestic Mild Cognitive Impairment on Resting-State Connectivity Across Four Independent Studies (Tam et al., 2015) [1]. The brain parcellations have been registered to both symmetric and asymmetric MNI brain templates and generated using a method called bootstrap analysis of stable clusters (BASC) (Bellec et al., 2010) [2]. We present two variants of these parcellations. One variant contains bihemisphereic parcels (4, 6, 12, 22, 33, 65, 111, and 208 total parcels across eight resolutions). The second variant contains spatially connected regions of interest (ROIs) that span only one hemisphere (10, 17, 30, 51, 77, 199, and 322 total ROIs across eight resolutions). We also present maps illustrating functional connectivity differences between patients and controls for four regions of interest (striatum, dorsal prefrontal cortex, middle temporal lobe, and medial frontal cortex). The brain parcels and associated statistical maps have been publicly released as 3D volumes, available in .mnc and .nii file formats on figshare and on Neurovault. Finally, the code used to generate this dataset is available on Github.

The impact of the resolution of meteorological datasets on catchment-scale drought studies

NASA Astrophysics Data System (ADS)

Hellwig, Jost; Stahl, Kerstin

2017-04-01

Gridded meteorological datasets provide the basis to study drought at a range of scales, including catchment scale drought studies in hydrology. They are readily available to study past weather conditions and often serve real time monitoring as well. As these datasets differ in spatial/temporal coverage and spatial/temporal resolution, for most studies there is a tradeoff between these features. Our investigation examines whether biases occur when studying drought on catchment scale with low resolution input data. For that, a comparison among the datasets HYRAS (covering Central Europe, 1x1 km grid, daily data, 1951 - 2005), E-OBS (Europe, 0.25° grid, daily data, 1950-2015) and GPCC (whole world, 0.5° grid, monthly data, 1901 - 2013) is carried out. Generally, biases in precipitation increase with decreasing resolution. Most important variations are found during summer. In low mountain range of Central Europe the datasets of sparse resolution (E-OBS, GPCC) overestimate dry days and underestimate total precipitation since they are not able to describe high spatial variability. However, relative measures like the correlation coefficient reveal good consistencies of dry and wet periods, both for absolute precipitation values and standardized indices like the Standardized Precipitation Index (SPI) or Standardized Precipitation Evaporation Index (SPEI). Particularly the most severe droughts derived from the different datasets match very well. These results indicate that absolute values of sparse resolution datasets applied to catchment scale might be critical to use for an assessment of the hydrological drought at catchment scale, whereas relative measures for determining periods of drought are more trustworthy. Therefore, studies on drought, that downscale meteorological data, should carefully consider their data needs and focus on relative measures for dry periods if sufficient for the task.

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

Geostatistics as a tool to improve the natural background level definition: An application in groundwater.

PubMed

Dalla Libera, Nico; Fabbri, Paolo; Mason, Leonardo; Piccinini, Leonardo; Pola, Marco

2017-11-15

The Natural Background Level (NBL), suggested by UE BRIDGE project, is suited for spatially distributed datasets providing a regional value that could be higher than the Threshold Value (TV) set by every country. In hydro-geochemically dis-homogeneous areas, the use of a unique regional NBL, higher than TV, could arise problems to distinguish between natural occurrences and anthropogenic contaminant sources. Hence, the goal of this study is to improve the NBL definition employing a geostatistical approach, which reconstructs the contaminant spatial structure accounting geochemical and hydrogeological relationships. This integrated mapping is fundamental to evaluate the contaminant's distribution impact on the NBL, giving indications to improve it. We decided to test this method on the Drainage Basin of Venice Lagoon (DBVL, NE Italy), where the existing NBL is seven times higher than the TV. This area is notoriously affected by naturally occurring arsenic contamination. An available geochemical dataset collected by 50 piezometers was used to reconstruct the spatial distribution of arsenic in the densely populated area of the DBVL. A cokriging approach was applied exploiting the geochemical relationships among As, Fe and NH4+. The obtained spatial predictions of arsenic concentrations were divided into three different zones: i) areas with an As concentration lower than the TV, ii) areas with an As concentration between the TV and the median of the values higher than the TV, and iii) areas with an As concentration higher than the median. Following the BRIDGE suggestions, where enough samples were available, the 90th percentile for each zone was calculated to obtain a local NBL (LNBL). Differently from the original NBL, this local value gives more detailed water quality information accounting the hydrogeological and geochemical setting, and contaminant spatial variation. Hence, the LNBL could give more indications about the distinction between natural occurrence and anthropogenic contamination. Copyright © 2017 Elsevier B.V. All rights reserved.

The effect of spatial resolution on water scarcity estimates in Australia

NASA Astrophysics Data System (ADS)

Gevaert, Anouk; Veldkamp, Ted; van Dijk, Albert; Ward, Philip

2017-04-01

Water scarcity is an important global issue with severe socio-economic consequences, and its occurrence is likely to increase in many regions due to population growth, economic development and climate change. This has prompted a number of global and regional studies to identify areas that are vulnerable to water scarcity and to determine how this vulnerability will change in the future. A drawback of these studies, however, is that they typically have coarse spatial resolutions. Here, we studied the effect of increasing the spatial resolution of water scarcity estimates in Australia, and the Murray-Darling Basin in particular. This was achieved by calculating the water stress index (WSI), an indicator showing the ratio of water use to water availability, at 0.5 and 0.05 degree resolution for the period 1990-2010. Monthly water availability data were based on outputs of the Australian Water Resources Assessment Landscape model (AWRA-L), which was run at both spatial resolutions and at a daily time scale. Water use information was obtained from a monthly 0.5 degree global dataset that distinguishes between water consumption for irrigation, livestock, industrial and domestic uses. The data were downscaled to 0.05 degree by dividing the sectoral water uses over the areas covered by relevant land use types using a high resolution ( 0.5km) land use dataset. The monthly WSIs at high and low resolution were then used to evaluate differences in the patterns of water scarcity frequency and intensity. In this way, we assess to what extent increasing the spatial resolution can improve the identification of vulnerable areas and thereby assist in the development of strategies to lower this vulnerability. The results of this study provide insight into the scalability of water scarcity estimates and the added value of high resolution water scarcity information in water resources management.

Development of a GIService based on spatial data mining for location choice of convenience stores in Taipei City

NASA Astrophysics Data System (ADS)

Jung, Chinte; Sun, Chih-Hong

2006-10-01

Motivated by the increasing accessibility of technology, more and more spatial data are being made digitally available. How to extract the valuable knowledge from these large (spatial) databases is becoming increasingly important to businesses, as well. It is essential to be able to analyze and utilize these large datasets, convert them into useful knowledge, and transmit them through GIS-enabled instruments and the Internet, conveying the key information to business decision-makers effectively and benefiting business entities. In this research, we combine the techniques of GIS, spatial decision support system (SDSS), spatial data mining (SDM), and ArcGIS Server to achieve the following goals: (1) integrate databases from spatial and non-spatial datasets about the locations of businesses in Taipei, Taiwan; (2) use the association rules, one of the SDM methods, to extract the knowledge from the integrated databases; and (3) develop a Web-based SDSS GIService as a location-selection tool for business by the product of ArcGIS Server.

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

Population-Area Relationship for Medieval European Cities

PubMed Central

Lobo, José; Bettencourt, Luís M. A.; Ortman, Scott G.; Smith, Michael E.

2016-01-01

Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems. Yet, many of the spatial, political and economic features of medieval European cities were particular to the Middle Ages, and subsequently changed over the Early Modern Period and Industrial Revolution. There is a long tradition of demographic studies estimating the population sizes of medieval European cities, and comparative analyses of these data have shed much light on the long-term evolution of urban systems. However, the next step—to systematically relate the population size of these cities to their spatial and socioeconomic characteristics—has seldom been taken. This raises a series of interesting questions, as both modern and ancient cities have been observed to obey area-population relationships predicted by settlement scaling theory. To address these questions, we analyze a new dataset for the settled area and population of 173 European cities from the early fourteenth century to determine the relationship between population and settled area. To interpret this data, we develop two related models that lead to differing predictions regarding the quantitative form of the population-area relationship, depending on the level of social mixing present in these cities. Our empirical estimates of model parameters show a strong densification of cities with city population size, consistent with patterns in contemporary cities. Although social life in medieval Europe was orchestrated by hierarchical institutions (e.g., guilds, church, municipal organizations), our results show no statistically significant influence of these institutions on agglomeration effects. The similarities between the empirical patterns of settlement relating area to population observed here support the hypothesis that cities throughout history share common principles of organization that self-consistently relate their socioeconomic networks to structured urban spaces. PMID:27706192

Population-Area Relationship for Medieval European Cities.

PubMed

Cesaretti, Rudolf; Lobo, José; Bettencourt, Luís M A; Ortman, Scott G; Smith, Michael E

2016-01-01

Medieval European urbanization presents a line of continuity between earlier cities and modern European urban systems. Yet, many of the spatial, political and economic features of medieval European cities were particular to the Middle Ages, and subsequently changed over the Early Modern Period and Industrial Revolution. There is a long tradition of demographic studies estimating the population sizes of medieval European cities, and comparative analyses of these data have shed much light on the long-term evolution of urban systems. However, the next step-to systematically relate the population size of these cities to their spatial and socioeconomic characteristics-has seldom been taken. This raises a series of interesting questions, as both modern and ancient cities have been observed to obey area-population relationships predicted by settlement scaling theory. To address these questions, we analyze a new dataset for the settled area and population of 173 European cities from the early fourteenth century to determine the relationship between population and settled area. To interpret this data, we develop two related models that lead to differing predictions regarding the quantitative form of the population-area relationship, depending on the level of social mixing present in these cities. Our empirical estimates of model parameters show a strong densification of cities with city population size, consistent with patterns in contemporary cities. Although social life in medieval Europe was orchestrated by hierarchical institutions (e.g., guilds, church, municipal organizations), our results show no statistically significant influence of these institutions on agglomeration effects. The similarities between the empirical patterns of settlement relating area to population observed here support the hypothesis that cities throughout history share common principles of organization that self-consistently relate their socioeconomic networks to structured urban spaces.

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime.

PubMed

Fitterer, Jessica L; Nelson, Trisalyn A

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks).

A Review of the Statistical and Quantitative Methods Used to Study Alcohol-Attributable Crime

PubMed Central

Fitterer, Jessica L.; Nelson, Trisalyn A.

2015-01-01

Modelling the relationship between alcohol consumption and crime generates new knowledge for crime prevention strategies. Advances in data, particularly data with spatial and temporal attributes, have led to a growing suite of applied methods for modelling. In support of alcohol and crime researchers we synthesized and critiqued existing methods of spatially and quantitatively modelling the effects of alcohol exposure on crime to aid method selection, and identify new opportunities for analysis strategies. We searched the alcohol-crime literature from 1950 to January 2014. Analyses that statistically evaluated or mapped the association between alcohol and crime were included. For modelling purposes, crime data were most often derived from generalized police reports, aggregated to large spatial units such as census tracts or postal codes, and standardized by residential population data. Sixty-eight of the 90 selected studies included geospatial data of which 48 used cross-sectional datasets. Regression was the prominent modelling choice (n = 78) though dependent on data many variations existed. There are opportunities to improve information for alcohol-attributable crime prevention by using alternative population data to standardize crime rates, sourcing crime information from non-traditional platforms (social media), increasing the number of panel studies, and conducting analysis at the local level (neighbourhood, block, or point). Due to the spatio-temporal advances in crime data, we expect a continued uptake of flexible Bayesian hierarchical modelling, a greater inclusion of spatial-temporal point pattern analysis, and shift toward prospective (forecast) modelling over small areas (e.g., blocks). PMID:26418016

Human neutral genetic variation and forensic STR data.

PubMed

Silva, Nuno M; Pereira, Luísa; Poloni, Estella S; Currat, Mathias

2012-01-01

The forensic genetics field is generating extensive population data on polymorphism of short tandem repeats (STR) markers in globally distributed samples. In this study we explored and quantified the informative power of these datasets to address issues related to human evolution and diversity, by using two online resources: an allele frequency dataset representing 141 populations summing up to almost 26 thousand individuals; a genotype dataset consisting of 42 populations and more than 11 thousand individuals. We show that the genetic relationships between populations based on forensic STRs are best explained by geography, as observed when analysing other worldwide datasets generated specifically to study human diversity. However, the global level of genetic differentiation between populations (as measured by a fixation index) is about half the value estimated with those other datasets, which contain a much higher number of markers but much less individuals. We suggest that the main factor explaining this difference is an ascertainment bias in forensics data resulting from the choice of markers for individual identification. We show that this choice results in average low variance of heterozygosity across world regions, and hence in low differentiation among populations. Thus, the forensic genetic markers currently produced for the purpose of individual assignment and identification allow the detection of the patterns of neutral genetic structure that characterize the human population but they do underestimate the levels of this genetic structure compared to the datasets of STRs (or other kinds of markers) generated specifically to study the diversity of human populations.

Merging Station Observations with Large-Scale Gridded Data to Improve Hydrological Predictions over Chile

NASA Astrophysics Data System (ADS)

Peng, L.; Sheffield, J.; Verbist, K. M. J.

2016-12-01

Hydrological predictions at regional-to-global scales are often hampered by the lack of meteorological forcing data. The use of large-scale gridded meteorological data is able to overcome this limitation, but these data are subject to regional biases and unrealistic values at local scale. This is especially challenging in regions such as Chile, where climate exhibits high spatial heterogeneity as a result of long latitude span and dramatic elevation changes. However, regional station-based observational datasets are not fully exploited and have the potential of constraining biases and spatial patterns. This study aims at adjusting precipitation and temperature estimates from the Princeton University global meteorological forcing (PGF) gridded dataset to improve hydrological simulations over Chile, by assimilating 982 gauges from the Dirección General de Aguas (DGA). To merge station data with the gridded dataset, we use a state-space estimation method to produce optimal gridded estimates, considering both the error of the station measurements and the gridded PGF product. The PGF daily precipitation, maximum and minimum temperature at 0.25° spatial resolution are adjusted for the period of 1979-2010. Precipitation and temperature gauges with long and continuous records (>70% temporal coverage) are selected, while the remaining stations are used for validation. The leave-one-out cross validation verifies the robustness of this data assimilation approach. The merged dataset is then used to force the Variable Infiltration Capacity (VIC) hydrological model over Chile at daily time step which are compared to the observations of streamflow. Our initial results show that the station-merged PGF precipitation effectively captures drizzle and the spatial pattern of storms. Overall the merged dataset has significant improvements compared to the original PGF with reduced biases and stronger inter-annual variability. The invariant spatial pattern of errors between the station data and the gridded product opens up the possibility of merging real-time satellite and intermittent gauge observations to produce more accurate real-time hydrological predictions.

From Parasite Encounter to Infection: Multiple-Scale Drivers of Parasite Richness in a Wild Social Primate Population

NASA Technical Reports Server (NTRS)

Benavides J. A.; Huchard, E.; Pettorelli, N.; King, A. J.; Brown, M. E.; Archer, C. E.; Appleton, C. C.; Raymond, M.; Cowlishaw, G.

2011-01-01

Host parasite diversity plays a fundamental role in ecological and evolutionary processes, yet the factors that drive it are still poorly understood. A variety of processes, operating across a range of spatial scales, are likely to influence both the probability of parasite encounter and subsequent infection. Here, we explored eight possible determinants of parasite richness, comprising rainfall and temperature at the population level, ranging behavior and home range productivity at the group level, and age, sex, body condition, and social rank at the individual level. We used a unique dataset describing gastrointestinal parasites in a terrestrial subtropical vertebrate (chacma baboons, Papio ursinus), comprising 662 faecal samples from 86 individuals representing all age-sex classes across two groups over two dry seasons in a desert population. Three mixed models were used to identify the most important factor at each of the three spatial scales (population, group, individual); these were then standardised and combined in a single, global, mixed model. Individual age had the strongest influence on parasite richness, in a convex relationship. Parasite richness was also higher in females and animals in poor condition, albeit at a lower order of magnitude than age. Finally, with a further halving of effect size, parasite richness was positively correlated to day range and temperature. These findings indicate that a range of factors influence host parasite richness through both encounter and infection probabilities, but that individual-level processes may be more important than those at the group or population level.

Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range

NASA Astrophysics Data System (ADS)

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Newman, Andrew J.; Hughes, Mimi; McGurk, Bruce; Lundquist, Jessica D.

2018-01-01

Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain may improve its representation, particularly for basins whose orientations (e.g., windward-facing) are favored for orographic precipitation enhancement.

Overweight and obesity in India: policy issues from an exploratory multi-level analysis.

PubMed

Siddiqui, Md Zakaria; Donato, Ronald

2016-06-01

This article analyses a nationally representative household dataset-the National Family Health Survey (NFHS-3) conducted in 2005 to 2006-to examine factors influencing the prevalence of overweight/obesity in India. The dataset was disaggregated into four sub-population groups-urban and rural females and males-and multi-level logit regression models were used to estimate the impact of particular covariates on the likelihood of overweight/obesity. The multi-level modelling approach aimed to identify individual and macro-level contextual factors influencing this health outcome. In contrast to most studies on low-income developing countries, the findings reveal that education for females beyond a particular level of educational attainment exhibits a negative relationship with the likelihood of overweight/obesity. This relationship was not observed for males. Muslim females and all Sikh sub-populations have a higher likelihood of overweight/obesity suggesting the importance of socio-cultural influences. The results also show that the relationship between wealth and the probability of overweight/obesity is stronger for males than females highlighting the differential impact of increasing socio-economic status on gender. Multi-level analysis reveals that states exerted an independent influence on the likelihood of overweight/obesity beyond individual-level covariates, reflecting the importance of spatially related contextual factors on overweight/obesity. While this study does not disentangle macro-level 'obesogenic' environmental factors from socio-cultural network influences, the results highlight the need to refrain from adopting a 'one size fits all' policy approach in addressing the overweight/obesity epidemic facing India. Instead, policy implementation requires a more nuanced and targeted approach to incorporate the growing recognition of socio-cultural and spatial contextual factors impacting on healthy behaviours. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Spatial analysis of factors influencing long-term stress in the grizzly bear (Ursus arctos) population of Alberta, Canada.

PubMed

Bourbonnais, Mathieu L; Nelson, Trisalyn A; Cattet, Marc R L; Darimont, Chris T; Stenhouse, Gordon B

2013-01-01

Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others, conservation and management efforts can benefit by understanding spatial- and gender-based stress responses to landscape conditions.

Spatial Analysis of Factors Influencing Long-Term Stress in the Grizzly Bear (Ursus arctos) Population of Alberta, Canada

PubMed Central

Bourbonnais, Mathieu L.; Nelson, Trisalyn A.; Cattet, Marc R. L.; Darimont, Chris T.; Stenhouse, Gordon B.

2013-01-01

Non-invasive measures for assessing long-term stress in free ranging mammals are an increasingly important approach for understanding physiological responses to landscape conditions. Using a spatially and temporally expansive dataset of hair cortisol concentrations (HCC) generated from a threatened grizzly bear (Ursus arctos) population in Alberta, Canada, we quantified how variables representing habitat conditions and anthropogenic disturbance impact long-term stress in grizzly bears. We characterized spatial variability in male and female HCC point data using kernel density estimation and quantified variable influence on spatial patterns of male and female HCC stress surfaces using random forests. Separate models were developed for regions inside and outside of parks and protected areas to account for substantial differences in anthropogenic activity and disturbance within the study area. Variance explained in the random forest models ranged from 55.34% to 74.96% for males and 58.15% to 68.46% for females. Predicted HCC levels were higher for females compared to males. Generally, high spatially continuous female HCC levels were associated with parks and protected areas while low-to-moderate levels were associated with increased anthropogenic disturbance. In contrast, male HCC levels were low in parks and protected areas and low-to-moderate in areas with increased anthropogenic disturbance. Spatial variability in gender-specific HCC levels reveal that the type and intensity of external stressors are not uniform across the landscape and that male and female grizzly bears may be exposed to, or perceive, potential stressors differently. We suggest observed spatial patterns of long-term stress may be the result of the availability and distribution of foods related to disturbance features, potential sexual segregation in available habitat selection, and may not be influenced by sources of mortality which represent acute traumas. In this wildlife system and others, conservation and management efforts can benefit by understanding spatial- and gender-based stress responses to landscape conditions. PMID:24386273

Spatial prediction of near surface soil water retention functions using hydrogeophysics

NASA Astrophysics Data System (ADS)

Gibson, J. P.; Franz, T. E.

2017-12-01

The hydrological community often turns to widely available spatial datasets such as SSURGO to characterize the spatial variability of soil across a landscape of interest. This has served as a reasonable first approximation when lacking localized soil data. However, previous work has shown that information loss within land surface models primarily stems from parameterization. Localized soil sampling is both expensive and time intense, and thus a need exists in connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with larger spatial datasets. In this work, we utilize 2 geophysical techniques; cosmic ray neutron probe and electromagnetic induction, to identify temporally stable soil moisture patterns. This is achieved by measuring numerous times over a range of wet to dry field conditions in order to apply an empirical orthogonal function. We then present measured water retention functions of shallow cores extracted within each temporally stable zone. Lastly, we use soil moisture patterns as a covariate to predict soil hydraulic properties in areas without measurement and validate using a leave-one-out cross validation analysis. Using these approaches to better constrain soil hydraulic property variability, we speculate that further research can better estimate hydrologic fluxes in areas of interest.

Development of a socio-ecological environmental justice model for watershed-based management

NASA Astrophysics Data System (ADS)

Sanchez, Georgina M.; Nejadhashemi, A. Pouyan; Zhang, Zhen; Woznicki, Sean A.; Habron, Geoffrey; Marquart-Pyatt, Sandra; Shortridge, Ashton

2014-10-01

The dynamics and relationships between society and nature are complex and difficult to predict. Anthropogenic activities affect the ecological integrity of our natural resources, specifically our streams. Further, it is well-established that the costs of these activities are born unequally by different human communities. This study considered the utility of integrating stream health metrics, based on stream health indicators, with socio-economic measures of communities, to better characterize these effects. This study used a spatial multi-factor model and bivariate mapping to produce a novel assessment for watershed management, identification of vulnerable areas, and allocation of resources. The study area is the Saginaw River watershed located in Michigan. In-stream hydrological and water quality data were used to predict fish and macroinvertebrate measures of stream health. These measures include the Index of Biological Integrity (IBI), Hilsenhoff Biotic Index (HBI), Family IBI, and total number of Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxa. Stream health indicators were then compared to spatially coincident socio-economic data, obtained from the United States Census Bureau (2010), including race, income, education, housing, and population size. Statistical analysis including spatial regression and cluster analysis were used to examine the correlation between vulnerable human populations and environmental conditions. Overall, limited correlation was observed between the socio-economic data and ecological measures of stream health, with the highest being a negative correlation of 0.18 between HBI and the social parameter household size. Clustering was observed in the datasets with urban areas representing a second order clustering effect over the watershed. Regions with the worst stream health and most vulnerable social populations were most commonly located nearby or down-stream to highly populated areas and agricultural lands.

Dataset on outdoor behavior-system and spatial-pattern in the third place in cold area-based on the perspective of new energy structure.

PubMed

Ren, Kai; Wang, Yuan; Liu, Tingxi; Wang, Guanli

2017-02-01

The data presented in this paper are related to the research article entitled "Exploration of Outdoor Behavior System and Spatial Pattern in the Third Place in Cold Area- based on the perspective of new energy structure" (Ren, 2016) [1]. The dataset was from a field sub-time extended investigation to residents of Power Home Community in Inner Mongolia of China that belongs to cold region of ID area according to Chinese design code for buildings. This filed data provided descriptive statistics about environment-behavior symbiosis system, environment loading, behavior system, spatial demanding and spatial pattern for all kinds of residents (Older, younger, children). The field data set is made publicly available to enable critical or extended analyzes.

NATIONAL HYDROGRAPHY DATASET

EPA Science Inventory

Resource Purpose:The National Hydrography Dataset (NHD) is a comprehensive set of digital spatial data that contains information about surface water features such as lakes, ponds, streams, rivers, springs and wells. Within the NHD, surface water features are combined to fo...

Exploratory spatial data analysis of global MODIS active fire data

NASA Astrophysics Data System (ADS)

Oom, D.; Pereira, J. M. C.

2013-04-01

We performed an exploratory spatial data analysis (ESDA) of autocorrelation patterns in the NASA MODIS MCD14ML Collection 5 active fire dataset, for the period 2001-2009, at the global scale. The dataset was screened, resulting in an annual rate of false alarms and non-vegetation fires ranging from a minimum of 3.1% in 2003 to a maximum of 4.4% in 2001. Hot bare soils and gas flares were the major sources of false alarms and non-vegetation fires. The data were aggregated at 0.5° resolution for the global and local spatial autocorrelation Fire counts were found to be positively correlated up to distances of around 200 km, and negatively for larger distances. A value of 0.80 (p = 0.001, α = 0.05) for Moran's I indicates strong spatial autocorrelation between fires at global scale, with 60% of all cells displaying significant positive or negative spatial correlation. Different types of spatial autocorrelation were mapped and regression diagnostics allowed for the identification of spatial outlier cells, with fire counts much higher or lower than expected, considering their spatial context.

The emergence of spatial cyberinfrastructure.

PubMed

Wright, Dawn J; Wang, Shaowen

2011-04-05

Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.

The emergence of spatial cyberinfrastructure

PubMed Central

Wright, Dawn J.; Wang, Shaowen

2011-01-01

Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge. PMID:21467227

Development and Applications of a Comprehensive Land Use Classification and Map for the US

PubMed Central

Theobald, David M.

2014-01-01

Land cover maps reasonably depict areas that are strongly converted by human activities, but typically are unable to resolve low-density but widespread development patterns. Data products specifically designed to resolve land uses complement land cover datasets and likely improve our ability to understand the extent and complexity of human modification. Methods for developing a comprehensive land use classification system are described, and a map of land use for the conterminous United States is presented to reveal what we are doing on the land. The comprehensive, detailed and high-resolution dataset was developed through spatial analysis of nearly two-dozen publicly-available, national spatial datasets – predominately based on census housing, employment, and infrastructure, as well as land cover from satellite imagery. This effort resulted in 79 land use classes that fit within five main land use groups: built-up, production, recreation, conservation, and water. Key findings from this study are that built-up areas occupy 13.6% of mainland US, but that the majority of this occurs as low-density exurban/rural residential (9.1% of the US), while more intensive built-up land uses occupy 4.5%. For every acre of urban and suburban residential land, there are 0.13 commercial, 0.07 industrial, 0.48 institutional, and 0.29 acres of interstates/highways. This database can be used to address a variety of natural resource applications, and I provide three examples here: an entropy index of the diversity of land uses for smart-growth planning, a power-law scaling of metropolitan area population to developed footprint, and identifying potential conflict areas by delineating the urban interface. PMID:24728210

Models of Eucalypt phenology predict bat population flux.

PubMed

Giles, John R; Plowright, Raina K; Eby, Peggy; Peel, Alison J; McCallum, Hamish

2016-10-01

Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt-focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86-93% accuracy. Negative binomial models explained 43-53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt-based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar-based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape-scale population movement and spatiotemporal disease dynamics.

Architectural Implications for Spatial Object Association Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, V S; Kurc, T; Saltz, J

2009-01-29

Spatial object association, also referred to as cross-match of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server R, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation providesmore » insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).« less

Differential adult survival at close seabird colonies: The importance of spatial foraging segregation and bycatch risk during the breeding season.

PubMed

Genovart, Meritxell; Bécares, Juan; Igual, José-Manuel; Martínez-Abraín, Alejandro; Escandell, Raul; Sánchez, Antonio; Rodríguez, Beneharo; Arcos, José M; Oro, Daniel

2018-03-01

Marine megafauna, including seabirds, are critically affected by fisheries bycatch. However, bycatch risk may differ on temporal and spatial scales due to the uneven distribution and effort of fleets operating different fishing gear, and to focal species distribution and foraging behavior. Scopoli's shearwater Calonectris diomedea is a long-lived seabird that experiences high bycatch rates in longline fisheries and strong population-level impacts due to this type of anthropogenic mortality. Analyzing a long-term dataset on individual monitoring, we compared adult survival (by means of multi-event capture-recapture models) among three close predator-free Mediterranean colonies of the species. Unexpectedly for a long-lived organism, adult survival varied among colonies. We explored potential causes of this differential survival by (1) measuring egg volume as a proxy of food availability and parental condition; (2) building a specific longline bycatch risk map for the species; and (3) assessing the distribution patterns of breeding birds from the three study colonies via GPS tracking. Egg volume was very similar between colonies over time, suggesting that environmental variability related to habitat foraging suitability was not the main cause of differential survival. On the other hand, differences in foraging movements among individuals from the three colonies expose them to differential mortality risk, which likely influenced the observed differences in adult survival. The overlap of information obtained by the generation of specific bycatch risk maps, the quantification of population demographic parameters, and the foraging spatial analysis should inform managers about differential sensitivity to the anthropogenic impact at mesoscale level and guide decisions depending on the spatial configuration of local populations. The approach would apply and should be considered in any species where foraging distribution is colony-specific and mortality risk varies spatially. © 2017 John Wiley & Sons Ltd.

Leveraging Geographic Information Systems in an Integrated Health Care Delivery Organization

PubMed Central

Clift, Kathryn; Scott, Luther; Johnson, Michael; Gonzalez, Carlos

2014-01-01

A handful of the many changes resulting from the Affordable Care Act underscore the need for a geographic understanding of existing and prospective member communities. Health exchanges require that health provider networks are geographically accessible to underserved populations, and nonprofit hospitals nationwide are required to conduct community health needs assessments every three years. Beyond these requirements, health care providers are using maps and spatial analysis to better address health outcomes that are related in complex ways to social and economic factors. Kaiser Permanente is applying geographic information systems, with spatial analytics and map-based visualizations, to data sourced from its electronic medical records and from publicly and commercially available datasets. The results are helping to shape an understanding of the health needs of Kaiser Permanente members in the context of their communities. This understanding is part of a strategy to inform partnerships and interventions in and beyond traditional care delivery settings. PMID:24694317

Modeling urbanization patterns at a global scale with generative adversarial networks

NASA Astrophysics Data System (ADS)

Albert, A. T.; Strano, E.; Gonzalez, M.

2017-12-01

Current demographic projections show that, in the next 30 years, global population growth will mostly take place in developing countries. Coupled with a decrease in density, such population growth could potentially double the land occupied by settlements by 2050. The lack of reliable and globally consistent socio-demographic data, coupled with the limited predictive performance underlying traditional urban spatial explicit models, call for developing better predictive methods, calibrated using a globally-consistent dataset. Thus, richer models of the spatial interplay between the urban built-up land, population distribution and energy use are central to the discussion around the expansion and development of cities, and their impact on the environment in the context of a changing climate. In this talk we discuss methods for, and present an analysis of, urban form, defined as the spatial distribution of macroeconomic quantities that characterize a city, using modern machine learning methods and best-available remote-sensing data for the world's largest 25,000 cities. We first show that these cities may be described by a small set of patterns in radial building density, nighttime luminosity, and population density, which highlight, to first order, differences in development and land use across the world. We observe significant, spatially-dependent variance around these typical patterns, which would be difficult to model using traditional statistical methods. We take a first step in addressing this challenge by developing CityGAN, a conditional generative adversarial network model for simulating realistic urban forms. To guide learning and measure the quality of the simulated synthetic cities, we develop a specialized loss function for GAN optimization that incorporates standard spatial statistics used by urban analysis experts. Our framework is a stark departure from both the standard physics-based approaches in the literature (that view urban forms as fractals with a scale-free behavior), and the traditional statistical learning approaches (whereby values of individual pixels are modeled as functions of locally-defined, hand-engineered features). This is a first-of-its-kind analysis of urban forms using data at a planetary scale.

A geospatial database model for the management of remote sensing datasets at multiple spectral, spatial, and temporal scales

NASA Astrophysics Data System (ADS)

Ifimov, Gabriela; Pigeau, Grace; Arroyo-Mora, J. Pablo; Soffer, Raymond; Leblanc, George

2017-10-01

In this study the development and implementation of a geospatial database model for the management of multiscale datasets encompassing airborne imagery and associated metadata is presented. To develop the multi-source geospatial database we have used a Relational Database Management System (RDBMS) on a Structure Query Language (SQL) server which was then integrated into ArcGIS and implemented as a geodatabase. The acquired datasets were compiled, standardized, and integrated into the RDBMS, where logical associations between different types of information were linked (e.g. location, date, and instrument). Airborne data, at different processing levels (digital numbers through geocorrected reflectance), were implemented in the geospatial database where the datasets are linked spatially and temporally. An example dataset consisting of airborne hyperspectral imagery, collected for inter and intra-annual vegetation characterization and detection of potential hydrocarbon seepage events over pipeline areas, is presented. Our work provides a model for the management of airborne imagery, which is a challenging aspect of data management in remote sensing, especially when large volumes of data are collected.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

The French-Canadian data set of Demirjian for dental age estimation: a systematic review and meta-analysis.

PubMed

Jayaraman, Jayakumar; Wong, Hai Ming; King, Nigel M; Roberts, Graham J

2013-07-01

Estimation of age of an individual can be performed by evaluating the pattern of dental development. A dataset for age estimation based on the dental maturity of a French-Canadian population was published over 35 years ago and has become the most widely accepted dataset. The applicability of this dataset has been tested on different population groups. To estimate the observed differences between Chronological age (CA) and Dental age (DA) when the French Canadian dataset was used to estimate the age of different population groups. A systematic search of literature for papers utilizing the French Canadian dataset for age estimation was performed. All language articles from PubMed, Embase and Cochrane databases were electronically searched for terms 'Demirjian' and 'Dental age' published between January 1973 and December 2011. A hand search of articles was also conducted. A total of 274 studies were identified from which 34 studies were included for qualitative analysis and 12 studies were included for quantitative assessment and meta-analysis. When synthesizing the estimation results from different population groups, on average, the Demirjian dataset overestimated the age of females by 0.65 years (-0.10 years to +2.82 years) and males by 0.60 years (-0.23 years to +3.04 years). The French Canadian dataset overestimates the age of the subjects by more than six months and hence this dataset should be used only with considerable caution when estimating age of group of subjects of any global population. Copyright © 2013 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

Updated population metadata for United States historical climatology network stations

USGS Publications Warehouse

Owen, T.W.; Gallo, K.P.

2000-01-01

The United States Historical Climatology Network (HCN) serial temperature dataset is comprised of 1221 high-quality, long-term climate observing stations. The HCN dataset is available in several versions, one of which includes population-based temperature modifications to adjust urban temperatures for the "heat-island" effect. Unfortunately, the decennial population metadata file is not complete as missing values are present for 17.6% of the 12 210 population values associated with the 1221 individual stations during the 1900-90 interval. Retrospective grid-based populations. Within a fixed distance of an HCN station, were estimated through the use of a gridded population density dataset and historically available U.S. Census county data. The grid-based populations for the HCN stations provide values derived from a consistent methodology compared to the current HCN populations that can vary as definitions of the area associated with a city change over time. The use of grid-based populations may minimally be appropriate to augment populations for HCN climate stations that lack any population data, and are recommended when consistent and complete population data are required. The recommended urban temperature adjustments based on the HCN and grid-based methods of estimating station population can be significantly different for individual stations within the HCN dataset.

A multidimensional representation model of geographic features

USGS Publications Warehouse

Usery, E. Lynn; Timson, George; Coletti, Mark

2016-01-28

A multidimensional model of geographic features has been developed and implemented with data from The National Map of the U.S. Geological Survey. The model, programmed in C++ and implemented as a feature library, was tested with data from the National Hydrography Dataset demonstrating the capability to handle changes in feature attributes, such as increases in chlorine concentration in a stream, and feature geometry, such as the changing shoreline of barrier islands over time. Data can be entered directly, from a comma separated file, or features with attributes and relationships can be automatically populated in the model from data in the Spatial Data Transfer Standard format.

Risk and resilience in the late glacial: A case study from the western Mediterranean

NASA Astrophysics Data System (ADS)

Barton, C. Michael; Aura Tortosa, J. Emili; Garcia-Puchol, Oreto; Riel-Salvatore, Julien G.; Gauthier, Nicolas; Vadillo Conesa, Margarita; Pothier Bouchard, Geneviève

2018-03-01

The period spanning the Last Glacial Maximum through early Holocene encompasses dramatic and rapid environmental changes that offered both increased risk and new opportunities to human populations of the Mediterranean zone. The regional effects of global climate change varied spatially with latitude, topography, and distance from a shifting coastline; and human adaptations to these changes played out at these regional scales. To better understand the spatial and temporal dynamics of climate change and human social-ecological-technological systems (or SETS) during the transition from full glacial to interglacial, we carried out a meta-analysis of archaeological and paleoenvironmental datasets across the western Mediterranean region. We compiled information on prehistoric technology, land-use, and hunting strategies from 291 archaeological assemblages, recovered from 122 sites extending from southern Spain, through Mediterranean France, to northern and peninsular Italy, as well as 2,386 radiocarbon dates from across this region. We combine these data on human ecological dynamics with paleoenvironmental information derived from global climate models, proxy data, and estimates of coastlines modeled from sea level estimates and digital terrain. The LGM represents an ecologically predictable period for over much of the western Mediterranean, while the remainder of the Pleistocene was increasingly unpredictable, making it a period of increased ecological risk for hunter-gatherers. In response to increasing spatial and temporal uncertainty, hunter-gatherers reorganized different constituents of their SETS, allowing regional populations to adapt to these conditions up to a point. Beyond this threshold, rapid environmental change resulted in significant demographic change in Mediterranean hunter-gatherer populations.

Mapping and spatiotemporal analysis tool for hydrological data: Spellmap

USDA-ARS?s Scientific Manuscript database

Lack of data management and analyses tools is one of the major limitations to effectively evaluate and use large datasets of high-resolution atmospheric, surface, and subsurface observations. High spatial and temporal resolution datasets better represent the spatiotemporal variability of hydrologica...

Long-term vegetation activity trends in the Iberian Peninsula and The Balearic Islands using high spatial resolution NOAA-AVHRR data (1981 - 2015).

NASA Astrophysics Data System (ADS)

Martin-Hernandez, Natalia; Vicente-Serrano, Sergio; Azorin-Molina, Cesar; Begueria-Portugues, Santiago; Reig-Gracia, Fergus; Zabalza-Martínez, Javier

2017-04-01

We have analysed trends in the Normalized Difference Vegetation Index (NDVI) in the Iberian Peninsula and The Balearic Islands over the period 1981 - 2015 using a new high resolution data set from the entire available NOAA - AVHRR images (IBERIAN NDVI dataset). After a complete processing including geocoding, calibration, cloud removal, topographic correction and temporal filtering, we obtained bi-weekly time series. To assess the accuracy of the new IBERIAN NDVI time-series, we have compared temporal variability and trends of NDVI series with those results reported by GIMMS 3g and MODIS (MOD13A3) NDVI datasets. In general, the IBERIAN NDVI showed high reliability with these two products but showing higher spatial resolution than the GIMMS dataset and covering two more decades than the MODIS dataset. Using the IBERIAN NDVI dataset, we analysed NDVI trends by means of the non-parametric Mann-Kendall test and Theil-Sen slope estimator. In average, vegetation trends in the study area show an increase over the last decades. However, there are local spatial differences: the main increase has been recorded in humid regions of the north of the Iberian Peninsula. The statistical techniques allow finding abrupt and gradual changes in different land cover types during the analysed period. These changes are related with human activity due to land transformations (from dry to irrigated land), land abandonment and forest recovery.

Improved Statistical Method For Hydrographic Climatic Records Quality Control

NASA Astrophysics Data System (ADS)

Gourrion, J.; Szekely, T.

2016-02-01

Climate research benefits from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of a quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to early 2014, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has been implemented in the latest version of the CORA dataset and will benefit to the next version of the Copernicus CMEMS dataset.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

2018-05-01

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

OpenMSI Arrayed Analysis Tools v2.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

BOWEN, BENJAMIN; RUEBEL, OLIVER; DE ROND, TRISTAN

2017-02-07

Mass spectrometry imaging (MSI) enables high-resolution spatial mapping of biomolecules in samples and is a valuable tool for the analysis of tissues from plants and animals, microbial interactions, high-throughput screening, drug metabolism, and a host of other applications. This is accomplished by desorbing molecules from the surface on spatially defined locations, using a laser or ion beam. These ions are analyzed by a mass spectrometry and collected into a MSI 'image', a dataset containing unique mass spectra from the sampled spatial locations. MSI is used in a diverse and increasing number of biological applications. The OpenMSI Arrayed Analysis Tool (OMAAT)more » is a new software method that addresses the challenges of analyzing spatially defined samples in large MSI datasets, by providing support for automatic sample position optimization and ion selection.« less

Resolution testing and limitations of geodetic and tsunami datasets for finite fault inversions along subduction zones

NASA Astrophysics Data System (ADS)

Williamson, A.; Newman, A. V.

2017-12-01

Finite fault inversions utilizing multiple datasets have become commonplace for large earthquakes pending data availability. The mixture of geodetic datasets such as Global Navigational Satellite Systems (GNSS) and InSAR, seismic waveforms, and when applicable, tsunami waveforms from Deep-Ocean Assessment and Reporting of Tsunami (DART) gauges, provide slightly different observations that when incorporated together lead to a more robust model of fault slip distribution. The merging of different datasets is of particular importance along subduction zones where direct observations of seafloor deformation over the rupture area are extremely limited. Instead, instrumentation measures related ground motion from tens to hundreds of kilometers away. The distance from the event and dataset type can lead to a variable degree of resolution, affecting the ability to accurately model the spatial distribution of slip. This study analyzes the spatial resolution attained individually from geodetic and tsunami datasets as well as in a combined dataset. We constrain the importance of distance between estimated parameters and observed data and how that varies between land-based and open ocean datasets. Analysis focuses on accurately scaled subduction zone synthetic models as well as analysis of the relationship between slip and data in recent large subduction zone earthquakes. This study shows that seafloor deformation sensitive datasets, like open-ocean tsunami waveforms or seafloor geodetic instrumentation, can provide unique offshore resolution for understanding most large and particularly tsunamigenic megathrust earthquake activity. In most environments, we simply lack the capability to resolve static displacements using land-based geodetic observations.

An Atlas of ShakeMaps and population exposure catalog for earthquake loss modeling

USGS Publications Warehouse

Allen, T.I.; Wald, D.J.; Earle, P.S.; Marano, K.D.; Hotovec, A.J.; Lin, K.; Hearne, M.G.

2009-01-01

We present an Atlas of ShakeMaps and a catalog of human population exposures to moderate-to-strong ground shaking (EXPO-CAT) for recent historical earthquakes (1973-2007). The common purpose of the Atlas and exposure catalog is to calibrate earthquake loss models to be used in the US Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER). The full ShakeMap Atlas currently comprises over 5,600 earthquakes from January 1973 through December 2007, with almost 500 of these maps constrained-to varying degrees-by instrumental ground motions, macroseismic intensity data, community internet intensity observations, and published earthquake rupture models. The catalog of human exposures is derived using current PAGER methodologies. Exposure to discrete levels of shaking intensity is obtained by correlating Atlas ShakeMaps with a global population database. Combining this population exposure dataset with historical earthquake loss data, such as PAGER-CAT, provides a useful resource for calibrating loss methodologies against a systematically-derived set of ShakeMap hazard outputs. We illustrate two example uses for EXPO-CAT; (1) simple objective ranking of country vulnerability to earthquakes, and; (2) the influence of time-of-day on earthquake mortality. In general, we observe that countries in similar geographic regions with similar construction practices tend to cluster spatially in terms of relative vulnerability. We also find little quantitative evidence to suggest that time-of-day is a significant factor in earthquake mortality. Moreover, earthquake mortality appears to be more systematically linked to the population exposed to severe ground shaking (Modified Mercalli Intensity VIII+). Finally, equipped with the full Atlas of ShakeMaps, we merge each of these maps and find the maximum estimated peak ground acceleration at any grid point in the world for the past 35 years. We subsequently compare this "composite ShakeMap" with existing global hazard models, calculating the spatial area of the existing hazard maps exceeded by the combined ShakeMap ground motions. In general, these analyses suggest that existing global, and regional, hazard maps tend to overestimate hazard. Both the Atlas of ShakeMaps and EXPO-CAT have many potential uses for examining earthquake risk and epidemiology. All of the datasets discussed herein are available for download on the PAGER Web page ( http://earthquake.usgs.gov/ eqcenter/pager/prodandref/ ). ?? 2009 Springer Science+Business Media B.V.

Assessment of Observational Uncertainty in Extreme Precipitation Events over the Continental United States

NASA Astrophysics Data System (ADS)

Slinskey, E. A.; Loikith, P. C.; Waliser, D. E.; Goodman, A.

2017-12-01

Extreme precipitation events are associated with numerous societal and environmental impacts. Furthermore, anthropogenic climate change is projected to alter precipitation intensity across portions of the Continental United States (CONUS). Therefore, a spatial understanding and intuitive means of monitoring extreme precipitation over time is critical. Towards this end, we apply an event-based indicator, developed as a part of NASA's support of the ongoing efforts of the US National Climate Assessment, which assigns categories to extreme precipitation events based on 3-day storm totals as a basis for dataset intercomparison. To assess observational uncertainty across a wide range of historical precipitation measurement approaches, we intercompare in situ station data from the Global Historical Climatology Network (GHCN), satellite-derived precipitation data from NASA's Tropical Rainfall Measuring Mission (TRMM), gridded in situ station data from the Parameter-elevation Regressions on Independent Slopes Model (PRISM), global reanalysis from NASA's Modern Era Retrospective-Analysis version 2 (MERRA 2), and regional reanalysis with gauge data assimilation from NCEP's North American Regional Reanalysis (NARR). Results suggest considerable variability across the five-dataset suite in the frequency, spatial extent, and magnitude of extreme precipitation events. Consistent with expectations, higher resolution datasets were found to resemble station data best and capture a greater frequency of high-end extreme events relative to lower spatial resolution datasets. The degree of dataset agreement varies regionally, however all datasets successfully capture the seasonal cycle of precipitation extremes across the CONUS. These intercomparison results provide additional insight about observational uncertainty and the ability of a range of precipitation measurement and analysis products to capture extreme precipitation event climatology. While the event category threshold is fixed in this analysis, preliminary results from the development of a flexible categorization scheme, that scales with grid resolution, are presented.

Application of Alignment Methodologies to Spatial Ontologies in the Hydro Domain

NASA Astrophysics Data System (ADS)

Lieberman, J. E.; Cheatham, M.; Varanka, D.

2015-12-01

Ontologies are playing an increasing role in facilitating mediation and translation between datasets representing diverse schemas, vocabularies, or knowledge communities. This role is relatively straightforward when there is one ontology comprising all relevant common concepts that can be mapped to entities in each dataset. Frequently, one common ontology has not been agreed to. Either each dataset is represented by a distinct ontology, or there are multiple candidates for commonality. Either the one most appropriate (expressive, relevant, correct) ontology must be chosen, or else concepts and relationships matched across multiple ontologies through an alignment process so that they may be used in concert to carry out mediation or other semantic operations. A resulting alignment can be effective to the extent that entities in in the ontologies represent differing terminology for comparable conceptual knowledge. In cases such as spatial ontologies, though, ontological entities may also represent disparate conceptualizations of space according to the discernment methods and application domains on which they are based. One ontology's wetland concept may overlap in space with another ontology's recharge zone or wildlife range or water feature. In order to evaluate alignment with respect to spatial ontologies, alignment has been applied to a series of ontologies pertaining to surface water that are used variously in hydrography (characterization of water features), hydrology (study of water cycling), and water quality (nutrient and contaminant transport) application domains. There is frequently a need to mediate between datasets in each domain in order to develop broader understanding of surface water systems, so there is a practical as well theoretical value in the alignment. From a domain expertise standpoint, the ontologies under consideration clearly contain some concepts that are spatially as well as conceptually identical and then others with less clear similarities in either sense. Our study serves both to determine the limits of standard methods for aligning spatial ontologies and to suggest new methods of calculating similarity axioms that take into account semantic, spatial, and cognitive criteria relevant to fitness for relevant usage scenarios.

Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian Tiger Mosquito, Aedes albopictus.

PubMed

Schmidt, Thomas L; Rašić, Gordana; Zhang, Dongjing; Zheng, Xiaoying; Xi, Zhiyong; Hoffmann, Ary A

2017-10-01

Aedes albopictus is a highly invasive disease vector with an expanding worldwide distribution. Genetic assays using low to medium resolution markers have found little evidence of spatial genetic structure even at broad geographic scales, suggesting frequent passive movement along human transportation networks. Here we analysed genetic structure of Aedes albopictus collected from 12 sample sites in Guangzhou, China, using thousands of genome-wide single nucleotide polymorphisms (SNPs). We found evidence for passive gene flow, with distance from shipping terminals being the strongest predictor of genetic distance among mosquitoes. As further evidence of passive dispersal, we found multiple pairs of full-siblings distributed between two sample sites 3.7 km apart. After accounting for geographical variability, we also found evidence for isolation by distance, previously undetectable in Ae. albopictus. These findings demonstrate how large SNP datasets and spatially-explicit hypothesis testing can be used to decipher processes at finer geographic scales than formerly possible. Our approach can be used to help predict new invasion pathways of Ae. albopictus and to refine strategies for vector control that involve the transformation or suppression of mosquito populations.

Spatial-explicit modeling of social vulnerability to malaria in East Africa

PubMed Central

2014-01-01

Background Despite efforts in eradication and control, malaria remains a global challenge, particularly affecting vulnerable groups. Despite the recession in malaria cases, previously malaria free areas are increasingly confronted with epidemics as a result of changing environmental and socioeconomic conditions. Next to modeling transmission intensities and probabilities, integrated spatial methods targeting the complex interplay of factors that contribute to social vulnerability are required to effectively reduce malaria burden. We propose an integrative method for mapping relative levels of social vulnerability in a spatially explicit manner to support the identification of intervention measures. Methods Based on a literature review, a holistic risk and vulnerability framework has been developed to guide the assessment of social vulnerability to water-related vector-borne diseases (VBDs) in the context of changing environmental and societal conditions. Building on the framework, this paper applies spatially explicit modeling for delineating homogeneous regions of social vulnerability to malaria in eastern Africa, while taking into account expert knowledge for weighting the single vulnerability indicators. To assess the influence of the selected indicators on the final index a local sensitivity analysis is carried out. Results Results indicate that high levels of malaria vulnerability are concentrated in the highlands, where immunity within the population is currently low. Additionally, regions with a lack of access to education and health services aggravate vulnerability. Lower values can be found in regions with relatively low poverty, low population pressure, low conflict density and reduced contributions from the biological susceptibility domain. Overall, the factors characterizing vulnerability vary spatially in the region. The vulnerability index reveals a high level of robustness in regard to the final choice of input datasets, with the exception of the immunity indicator which has a marked impact on the composite vulnerability index. Conclusions We introduce a conceptual framework for modeling risk and vulnerability to VBDs. Drawing on the framework we modeled social vulnerability to malaria in the context of global change using a spatially explicit approach. The results provide decision makers with place-specific options for targeting interventions that aim at reducing the burden of the disease amongst the different vulnerable population groups. PMID:25127688

SEER Data & Software

Cancer.gov

Options for accessing datasets for incidence, mortality, county populations, standard populations, expected survival, and SEER-linked and specialized data. Plus variable definitions, documentation for reporting and using datasets, statistical software (SEER*Stat), and observational research resources.

Survey-based socio-economic data from slums in Bangalore, India

PubMed Central

Roy, Debraj; Palavalli, Bharath; Menon, Niveditha; King, Robin; Pfeffer, Karin; Lees, Michael; Sloot, Peter M. A.

2018-01-01

In 2010, an estimated 860 million people were living in slums worldwide, with around 60 million added to the slum population between 2000 and 2010. In 2011, 200 million people in urban Indian households were considered to live in slums. In order to address and create slum development programmes and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual slums. We collected the data presented in this paper in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self reported data. Our survey of Bangalore covered 36 slums across the city. The slums were chosen based on stratification criteria, which included geographical location of the slum, whether the slum was resettled or rehabilitated, notification status of the slum, the size of the slum and the religious profile. This paper describes the relational model of the slum dataset, the variables in the dataset, the variables constructed for analysis and the issues identified with the dataset. The data collected includes around 267,894 data points spread over 242 questions for 1,107 households. The dataset can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty and well-being in the context of rapid urbanization of cities in developing countries. PMID:29313840

Tularosa Basin Play Fairway Analysis Data and Models

DOE Data Explorer

Nash, Greg

2017-07-11

This submission includes raster datasets for each layer of evidence used for weights of evidence analysis as well as the deterministic play fairway analysis (PFA). Data representative of heat, permeability and groundwater comprises some of the raster datasets. Additionally, the final deterministic PFA model is provided along with a certainty model. All of these datasets are best used with an ArcGIS software package, specifically Spatial Data Modeler.

Using mixture-tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada

NASA Astrophysics Data System (ADS)

Brelsford, Christa; Shepherd, Doug

2014-01-01

In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed between MTMF estimates and ground measurements of abundance is noisy. We use a ground truth dataset over 10 times larger than that available for any previous MTMF application to estimate the bias between ground data and MTMF results. We find that MTMF underestimates the fractional area of vegetation by 5% to 10% and show that averaging over multiple pixels is necessary to reduce noise in the dataset. We conclude that MTMF is a viable technique for fractional area estimation when a large dataset is available for calibration. When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

A scoping review of spatial cluster analysis techniques for point-event data.

PubMed

Fritz, Charles E; Schuurman, Nadine; Robertson, Colin; Lear, Scott

2013-05-01

Spatial cluster analysis is a uniquely interdisciplinary endeavour, and so it is important to communicate and disseminate ideas, innovations, best practices and challenges across practitioners, applied epidemiology researchers and spatial statisticians. In this research we conducted a scoping review to systematically search peer-reviewed journal databases for research that has employed spatial cluster analysis methods on individual-level, address location, or x and y coordinate derived data. To illustrate the thematic issues raised by our results, methods were tested using a dataset where known clusters existed. Point pattern methods, spatial clustering and cluster detection tests, and a locally weighted spatial regression model were most commonly used for individual-level, address location data (n = 29). The spatial scan statistic was the most popular method for address location data (n = 19). Six themes were identified relating to the application of spatial cluster analysis methods and subsequent analyses, which we recommend researchers to consider; exploratory analysis, visualization, spatial resolution, aetiology, scale and spatial weights. It is our intention that researchers seeking direction for using spatial cluster analysis methods, consider the caveats and strengths of each approach, but also explore the numerous other methods available for this type of analysis. Applied spatial epidemiology researchers and practitioners should give special consideration to applying multiple tests to a dataset. Future research should focus on developing frameworks for selecting appropriate methods and the corresponding spatial weighting schemes.

Dissecting the space-time structure of tree-ring datasets using the partial triadic analysis.

PubMed

Rossi, Jean-Pierre; Nardin, Maxime; Godefroid, Martin; Ruiz-Diaz, Manuela; Sergent, Anne-Sophie; Martinez-Meier, Alejandro; Pâques, Luc; Rozenberg, Philippe

2014-01-01

Tree-ring datasets are used in a variety of circumstances, including archeology, climatology, forest ecology, and wood technology. These data are based on microdensity profiles and consist of a set of tree-ring descriptors, such as ring width or early/latewood density, measured for a set of individual trees. Because successive rings correspond to successive years, the resulting dataset is a ring variables × trees × time datacube. Multivariate statistical analyses, such as principal component analysis, have been widely used for extracting worthwhile information from ring datasets, but they typically address two-way matrices, such as ring variables × trees or ring variables × time. Here, we explore the potential of the partial triadic analysis (PTA), a multivariate method dedicated to the analysis of three-way datasets, to apprehend the space-time structure of tree-ring datasets. We analyzed a set of 11 tree-ring descriptors measured in 149 georeferenced individuals of European larch (Larix decidua Miller) during the period of 1967-2007. The processing of densitometry profiles led to a set of ring descriptors for each tree and for each year from 1967-2007. The resulting three-way data table was subjected to two distinct analyses in order to explore i) the temporal evolution of spatial structures and ii) the spatial structure of temporal dynamics. We report the presence of a spatial structure common to the different years, highlighting the inter-individual variability of the ring descriptors at the stand scale. We found a temporal trajectory common to the trees that could be separated into a high and low frequency signal, corresponding to inter-annual variations possibly related to defoliation events and a long-term trend possibly related to climate change. We conclude that PTA is a powerful tool to unravel and hierarchize the different sources of variation within tree-ring datasets.

Improvements in the spatial representation of lakes and reservoirs in the contiguous United States for the National Water Model

NASA Astrophysics Data System (ADS)

Khan, S.; Salas, F.; Sampson, K. M.; Read, L. K.; Cosgrove, B.; Li, Z.; Gochis, D. J.

2017-12-01

The representation of inland surface water bodies in distributed hydrologic models at the continental scale is a challenge. The National Water Model (NWM) utilizes the National Hydrography Dataset Plus Version 2 (NHDPlusV2) "waterbody" dataset to represent lakes and reservoirs. The "waterbody" layer is a comprehensive dataset that represents surface water bodies using common features like lakes, ponds, reservoirs, estuaries, playas and swamps/marshes. However, a major issue that remains unresolved even in the latest revision of NHDPlus Version 2 is the inconsistency in waterbody digitization and delineation errors. Manually correcting the water body polygons becomes tedious and quickly impossible for continental-scale hydrologic models such as the NWM. In this study, we improved spatial representation of 6,802 lakes and reservoirs by analyzing 379,110 waterbodies in the contiguous United States (excluding the Laurentian Great Lakes). We performed a step-by- step process that integrates a set of geospatial analyses to identify, track, and correct the extent of lakes and reservoirs features that are larger than 0.75 km2. The following assumptions were applied while developing the new dataset: a) lakes and reservoirs cannot directly feed into each other; b) each waterbody must have one outlet; and c) a single lake or reservoir feature cannot have multiple parts. The majority of the NHDplusV2 waterbody features in the original dataset are delineated correctly. However approximately 3 % of the lake and reservoir polygons were found to be incorrect with topological errors and were corrected accordingly. It is important to fix these digitizing errors because the waterbody features are closely linked to the river topology. This new waterbody dataset will ensure that model-simulated water is directed into and through the lakes and reservoirs in a manner that supports the NWM code base and assumptions. The improved dataset will facilitate more effective integration of lakes and reservoirs with correct spatial features into the updated NWM.

U.S. Datasets

Cancer.gov

Datasets for U.S. mortality, U.S. populations, standard populations, county attributes, and expected survival. Plus SEER-linked databases (SEER-Medicare, SEER-Medicare Health Outcomes Survey [SEER-MHOS], SEER-Consumer Assessment of Healthcare Providers and Systems [SEER-CAHPS]).

Allele Frequencies Net Database: Improvements for storage of individual genotypes and analysis of existing data.

PubMed

Santos, Eduardo Jose Melos Dos; McCabe, Antony; Gonzalez-Galarza, Faviel F; Jones, Andrew R; Middleton, Derek

2016-03-01

The Allele Frequencies Net Database (AFND) is a freely accessible database which stores population frequencies for alleles or genes of the immune system in worldwide populations. Herein we introduce two new tools. We have defined new classifications of data (gold, silver and bronze) to assist users in identifying the most suitable populations for their tasks. The gold standard datasets are defined by allele frequencies summing to 1, sample sizes >50 and high resolution genotyping, while silver standard datasets do not meet gold standard genotyping resolution and/or sample size criteria. The bronze standard datasets are those that could not be classified under the silver or gold standards. The gold standard includes >500 datasets covering over 3 million individuals from >100 countries at one or more of the following loci: HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1 and -DRB1 - with all loci except DPA1 present in more than 220 datasets. Three out of 12 geographic regions have low representation (the majority of their countries having less than five datasets) and the Central Asia region has no representation. There are 18 countries that are not represented by any gold standard datasets but are represented by at least one dataset that is either silver or bronze standard. We also briefly summarize the data held by AFND for KIR genes, alleles and their ligands. Our second new component is a data submission tool to assist users in the collection of the genotypes of the individuals (raw data), facilitating submission of short population reports to Human Immunology, as well as simplifying the submission of population demographics and frequency data. Copyright © 2015 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

Temperature and population density determine reservoir regions of seasonal persistence in highland malaria.

PubMed

Siraj, Amir S; Bouma, Menno J; Santos-Vega, Mauricio; Yeshiwondim, Asnakew K; Rothman, Dale S; Yadeta, Damtew; Sutton, Paul C; Pascual, Mercedes

2015-12-07

A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these 'unstable' malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands. © 2015 The Author(s).

Temperature and population density determine reservoir regions of seasonal persistence in highland malaria

PubMed Central

Siraj, Amir S.; Bouma, Menno J.; Santos-Vega, Mauricio; Yeshiwondim, Asnakew K.; Rothman, Dale S.; Yadeta, Damtew; Sutton, Paul C.; Pascual, Mercedes

2015-01-01

A better understanding of malaria persistence in highly seasonal environments such as highlands and desert fringes requires identifying the factors behind the spatial reservoir of the pathogen in the low season. In these ‘unstable’ malaria regions, such reservoirs play a critical role by allowing persistence during the low transmission season and therefore, between seasonal outbreaks. In the highlands of East Africa, the most populated epidemic regions in Africa, temperature is expected to be intimately connected to where in space the disease is able to persist because of pronounced altitudinal gradients. Here, we explore other environmental and demographic factors that may contribute to malaria's highland reservoir. We use an extensive spatio-temporal dataset of confirmed monthly Plasmodium falciparum cases from 1995 to 2005 that finely resolves space in an Ethiopian highland. With a Bayesian approach for parameter estimation and a generalized linear mixed model that includes a spatially structured random effect, we demonstrate that population density is important to disease persistence during the low transmission season. This population effect is not accounted for in typical models for the transmission dynamics of the disease, but is consistent in part with a more complex functional form of the force of infection proposed by theory for vector-borne infections, only during the low season as we discuss. As malaria risk usually decreases in more urban environments with increased human densities, the opposite counterintuitive finding identifies novel control targets during the low transmission season in African highlands. PMID:26631558

Effects of spatial resolution and landscape structure on land cover characterization

NASA Astrophysics Data System (ADS)

Yang, Wenli

This dissertation addressed problems in scaling, problems that are among the main challenges in remote sensing. The principal objective of the research was to investigate the effects of changing spatial scale on the representation of land cover. A second objective was to determine the relationship between such effects, characteristics of landscape structure and scaling procedures. Four research issues related to spatial scaling were examined. They included: (1) the upscaling of Normalized Difference Vegetation Index (NDVI); (2) the effects of spatial scale on indices of landscape structure; (3) the representation of land cover databases at different spatial scales; and (4) the relationships between landscape indices and land cover area estimations. The overall bias resulting from non-linearity of NDVI in relation to spatial resolution is generally insignificant as compared to other factors such as influences of aerosols and water vapor. The bias is, however, related to land surface characteristics. Significant errors may be introduced in heterogeneous areas where different land cover types exhibit strong spectral contrast. Spatially upscaled SPOT and TM NDVIs have information content comparable with the AVHRR-derived NDVI. Indices of landscape structure and spatial resolution are generally related, but the exact forms of the relationships are subject to changes in other factors including the basic patch unit constituting a landscape and the proportional area of foreground land cover under consideration. The extent of agreement between spatially aggregated coarse resolution land cover datasets and full resolution datasets changes with the properties of the original datasets, including the pixel size and class definition. There are close relationships between landscape structure and class areas estimated from spatially aggregated land cover databases. The relationships, however, do not permit extension from one area to another. Inversion calibration across different geographic/ecological areas is, therefore, not feasible. Different rules govern the land cover area changes across resolutions when different upscaling methods are used. Special attention should be given to comparison between land cover maps derived using different methods.

Annual global mean temperature explains reproductive success in a marine vertebrate from 1955 to 2010.

PubMed

Mauck, Robert A; Dearborn, Donald C; Huntington, Charles E

2018-04-01

The salient feature of anthropogenic climate change over the last century has been the rise in global mean temperature. However, global mean temperature is not used as an explanatory variable in studies of population-level response to climate change, perhaps because the signal-to-noise ratio of this gross measure makes its effect difficult to detect in any but the longest of datasets. Using a population of Leach's storm-petrels breeding in the Bay of Fundy, we tested whether local, regional, or global temperature measures are the best index of reproductive success in the face of climate change in species that travel widely between and within seasons. With a 56-year dataset, we found that annual global mean temperature (AGMT) was the single most important predictor of hatching success, more so than regional sea surface temperatures (breeding season or winter) and local air temperatures at the nesting colony. Storm-petrel reproductive success showed a quadratic response to rising temperatures, in that hatching success increased up to some critical temperature, and then declined when AGMT exceeded that temperature. The year at which AGMT began to consistently exceed that critical temperature was 1988. Importantly, in this population of known-age individuals, the impact of changing climate was greatest on inexperienced breeders: reproductive success of inexperienced birds increased more rapidly as temperatures rose and declined more rapidly after the tipping point than did reproductive success of experienced individuals. The generality of our finding that AGMT is the best predictor of reproductive success in this system may hinge on two things. First, an integrative global measure may be best for species in which individuals move across an enormous spatial range, especially within seasons. Second, the length of our dataset and our capacity to account for individual- and age-based variation in reproductive success increase our ability to detect a noisy signal. © 2017 John Wiley & Sons Ltd.

Mapping regional soil water erosion risk in the Brittany-Loire basin for water management agency

NASA Astrophysics Data System (ADS)

Degan, Francesca; Cerdan, Olivier; Salvador-Blanes, Sébastien; Gautier, Jean-Noël

2014-05-01

Soil water erosion is one of the main degradation processes that affect soils through the removal of soil particles from the surface. The impacts for environment and agricultural areas are diverse, such as water pollution, crop yield depression, organic matter loss and reduction in water storage capacity. There is therefore a strong need to produce maps at the regional scale to help environmental policy makers and soil and water management bodies to mitigate the effect of water and soil pollution. Our approach aims to model and map soil erosion risk at regional scale (155 000 km²) and high spatial resolution (50 m) in the Brittany - Loire basin. The factors responsible for soil erosion are different according to the spatial and time scales considered. The regional scale entails challenges about homogeneous data sets availability, spatial resolution of results, various erosion processes and agricultural practices. We chose to improve the MESALES model (Le Bissonnais et al., 2002) to map soil erosion risk, because it was developed specifically for water erosion in agricultural fields in temperate areas. The MESALES model consists in a decision tree which gives for each combination of factors the corresponding class of soil erosion risk. Four factors that determine soil erosion risk are considered: soils, land cover, climate and topography. The first main improvement of the model consists in using newly available datasets that are more accurate than the initial ones. The datasets used cover all the study area homogeneously. Soil dataset has a 1/1 000 000 scale and attributes such as texture, soil type, rock fragment and parent material are used. The climate dataset has a spatial resolution of 8 km and a temporal resolution of mm/day for 12 years. Elevation dataset has a spatial resolution of 50 m. Three different land cover datasets are used where the finest spatial resolution is 50 m over three years. Using these datasets, four erosion factors are characterized and quantified: the soil factors (soil sealing, erodibility and runoff), the rate of land cover over three years for each season and for 77 land use classes, the topographic factor (slope and drainage area) and the climate hazard (seasonal amount and rainfall erosivity). These modifications of the original MESALES model allow to better represent erosion risk for arable and bare land. We validated model results by stakeholder consultations and meetings over all the study area. The model has finally been modified taking into account validation results. Results are provided with a spatial resolution of 1 km, and then integrated into 2121 catchments. An erosion risk map for each season and an annual erosion risk map are produced. These new maps allow to organize in hierarchy 2121 catchments into three erosion risk classes. In the annual erosion risk map, 347 catchments have the highest erosion risk, which corresponds to 16 % of total Brittany-Loire basin area. Water management agency now uses these maps to identify priority areas and to plan specific preservation practices.

Developing a regional retrospective ensemble precipitation dataset for watershed hydrology modeling, Idaho, USA

NASA Astrophysics Data System (ADS)

Flores, A. N.; Smith, K.; LaPorte, P.

2011-12-01

Applications like flood forecasting, military trafficability assessment, and slope stability analysis necessitate the use of models capable of resolving hydrologic states and fluxes at spatial scales of hillslopes (e.g., 10s to 100s m). These models typically require precipitation forcings at spatial scales of kilometers or better and time intervals of hours. Yet in especially rugged terrain that typifies much of the Western US and throughout much of the developing world, precipitation data at these spatiotemporal resolutions is difficult to come by. Ground-based weather radars have significant problems in high-relief settings and are sparsely located, leaving significant gaps in coverage and high uncertainties. Precipitation gages provide accurate data at points but are very sparsely located and their placement is often not representative, yielding significant coverage gaps in a spatial and physiographic sense. Numerical weather prediction efforts have made precipitation data, including critically important information on precipitation phase, available globally and in near real-time. However, these datasets present watershed modelers with two problems: (1) spatial scales of many of these datasets are tens of kilometers or coarser, (2) numerical weather models used to generate these datasets include a land surface parameterization that in some circumstances can significantly affect precipitation predictions. We report on the development of a regional precipitation dataset for Idaho that leverages: (1) a dataset derived from a numerical weather prediction model, (2) gages within Idaho that report hourly precipitation data, and (3) a long-term precipitation climatology dataset. Hourly precipitation estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA) are stochastically downscaled using a hybrid orographic and statistical model from their native resolution (1/2 x 2/3 degrees) to a resolution of approximately 1 km. Downscaled precipitation realizations are conditioned on hourly observations from reporting gages and then conditioned again on the Parameter-elevation Regressions on Independent Slopes Model (PRISM) at the monthly timescale to reflect orographic precipitation trends common to watersheds of the Western US. While this methodology potentially introduces cross-pollination of errors due to the re-use of precipitation gage data, it nevertheless achieves an ensemble-based precipitation estimate and appropriate measures of uncertainty at a spatiotemporal resolution appropriate for watershed modeling.

Accuracy assessment of seven global land cover datasets over China

NASA Astrophysics Data System (ADS)

Yang, Yongke; Xiao, Pengfeng; Feng, Xuezhi; Li, Haixing

2017-03-01

Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special attention should be paid for data users who are interested in these regions.

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

A framework for incorporating DTI Atlas Builder registration into Tract-Based Spatial Statistics and a simulated comparison to standard TBSS.

PubMed

Leming, Matthew; Steiner, Rachel; Styner, Martin

2016-02-27

Tract-based spatial statistics (TBSS) 6 is a software pipeline widely employed in comparative analysis of the white matter integrity from diffusion tensor imaging (DTI) datasets. In this study, we seek to evaluate the relationship between different methods of atlas registration for use with TBSS and different measurements of DTI (fractional anisotropy, FA, axial diffusivity, AD, radial diffusivity, RD, and medial diffusivity, MD). To do so, we have developed a novel tool that builds on existing diffusion atlas building software, integrating it into an adapted version of TBSS called DAB-TBSS (DTI Atlas Builder-Tract-Based Spatial Statistics) by using the advanced registration offered in DTI Atlas Builder 7 . To compare the effectiveness of these two versions of TBSS, we also propose a framework for simulating population differences for diffusion tensor imaging data, providing a more substantive means of empirically comparing DTI group analysis programs such as TBSS. In this study, we used 33 diffusion tensor imaging datasets and simulated group-wise changes in this data by increasing, in three different simulations, the principal eigenvalue (directly altering AD), the second and third eigenvalues (RD), and all three eigenvalues (MD) in the genu, the right uncinate fasciculus, and the left IFO. Additionally, we assessed the benefits of comparing the tensors directly using a functional analysis of diffusion tensor tract statistics (FADTTS 10 ). Our results indicate comparable levels of FA-based detection between DAB-TBSS and TBSS, with standard TBSS registration reporting a higher rate of false positives in other measurements of DTI. Within the simulated changes investigated here, this study suggests that the use of DTI Atlas Builder's registration enhances TBSS group-based studies.

Multiple Imputation of Groundwater Data to Evaluate Spatial and Temporal Anthropogenic Influences on Subsurface Water Fluxes in Los Angeles, CA

NASA Astrophysics Data System (ADS)

Manago, K. F.; Hogue, T. S.; Hering, A. S.

2014-12-01

In the City of Los Angeles, groundwater accounts for 11% of the total water supply on average, and 30% during drought years. Due to ongoing drought in California, increased reliance on local water supply highlights the need for better understanding of regional groundwater dynamics and estimating sustainable groundwater supply. However, in an urban setting, such as Los Angeles, understanding or modeling groundwater levels is extremely complicated due to various anthropogenic influences such as groundwater pumping, artificial recharge, landscape irrigation, leaking infrastructure, seawater intrusion, and extensive impervious surfaces. This study analyzes anthropogenic effects on groundwater levels using groundwater monitoring well data from the County of Los Angeles Department of Public Works. The groundwater data is irregularly sampled with large gaps between samples, resulting in a sparsely populated dataset. A multiple imputation method is used to fill the missing data, allowing for multiple ensembles and improved error estimates. The filled data is interpolated to create spatial groundwater maps utilizing information from all wells. The groundwater data is evaluated at a monthly time step over the last several decades to analyze the effect of land cover and identify other influencing factors on groundwater levels spatially and temporally. Preliminary results show irrigated parks have the largest influence on groundwater fluctuations, resulting in large seasonal changes, exceeding changes in spreading grounds. It is assumed that these fluctuations are caused by watering practices required to sustain non-native vegetation. Conversely, high intensity urbanized areas resulted in muted groundwater fluctuations and behavior decoupling from climate patterns. Results provides improved understanding of anthropogenic effects on groundwater levels in addition to providing high quality datasets for validation of regional groundwater models.

A Unified Picture of Mass Segregation in Globular Clusters

NASA Astrophysics Data System (ADS)

Watkins, Laura

2017-08-01

The sensitivity, stability and longevity of HST have opened up an exciting new parameter space: we now have velocity measurements, in the form of proper motions (PMs), for stars from the tip of the red giant branch to a few magnitudes below the main-sequence turn off for a large sample of globular clusters (GCs). For the very first time, we have the opportunity to measure both kinematic and spatial dependences on stellar mass in GCs.The formation and evolution histories of GCs are poorly understood, so too are their intermediate-mass black hole populations and binary fractions. However, the current structure and dynamical state of a GC is directly determined by its past history and its components, so by understanding the former we can gain insight into the latter. Quantifying variations in spatial structure for stars of different mass is extremely difficult with photometry alone as datasets are inhomogenous and incomplete. We require kinematic data for stars that span a range of stellar masses, combined with proper dynamical modelling. We now have the data in hand, but still lack the models needed to maximise the scientific potential of our HST datasets.Here, we propose to extend existing single-mass discrete dynamical-modelling tools to include kinematic and spatial variations with stellar mass, and verify the upgrades using mock data generated from N-body models. We will then apply the models to HST PM data and directly quantify energy equipartition and mass segregation in the GCs. The theoretical phase of the project is vital for the success of the subsequent data analysis, and will serve as a benchmark for future observational campaigns with HST, JWST and beyond.

A Global Geospatial Database of 5000+ Historic Flood Event Extents

NASA Astrophysics Data System (ADS)

Tellman, B.; Sullivan, J.; Doyle, C.; Kettner, A.; Brakenridge, G. R.; Erickson, T.; Slayback, D. A.

2017-12-01

A key dataset that is missing for global flood model validation and understanding historic spatial flood vulnerability is a global historical geo-database of flood event extents. Decades of earth observing satellites and cloud computing now make it possible to not only detect floods in near real time, but to run these water detection algorithms back in time to capture the spatial extent of large numbers of specific events. This talk will show results from the largest global historical flood database developed to date. We use the Dartmouth Flood Observatory flood catalogue to map over 5000 floods (from 1985-2017) using MODIS, Landsat, and Sentinel-1 Satellites. All events are available for public download via the Earth Engine Catalogue and via a website that allows the user to query floods by area or date, assess population exposure trends over time, and download flood extents in geospatial format.In this talk, we will highlight major trends in global flood exposure per continent, land use type, and eco-region. We will also make suggestions how to use this dataset in conjunction with other global sets to i) validate global flood models, ii) assess the potential role of climatic change in flood exposure iii) understand how urbanization and other land change processes may influence spatial flood exposure iv) assess how innovative flood interventions (e.g. wetland restoration) influence flood patterns v) control for event magnitude to assess the role of social vulnerability and damage assessment vi) aid in rapid probabilistic risk assessment to enable microinsurance markets. Authors on this paper are already using the database for the later three applications and will show examples of wetland intervention analysis in Argentina, social vulnerability analysis in the USA, and micro insurance in India.

Gridded precipitation fields at high temporal and spatial resolution for operational flood forecasting in the Rhine basin

NASA Astrophysics Data System (ADS)

van Osnabrugge, Bart; Weerts, Albrecht; Uijlenhoet, Remko

2017-04-01

Gridded areal precipitation, as one of the most important hydrometeorological input variables for initial state estimation in operational hydrological forecasting, is available in the form of raster data sets (e.g. HYRAS and EOBS) for the River Rhine basin. These datasets are compiled off-line on a daily time step using station data with the highest possible spatial density. However, such a product is not available operationally and at an hourly discretisation. Therefore, we constructed an hourly gridded precipitation dataset at 1.44 km2 resolution for the Rhine basin for the period from 1998 to present using a REGNIE-like interpolation procedure (Weerts et al., 2008) using a low and a high density rain gauge network. The datasets were validated against daily HYRAS (Rauthe, 2013) and EOBS (Haylock, 2008) data. The main goal of the operational procedure is to emulate the HYRAS dataset as good as possible, as the daily HYRAS dataset is used in the off-line calibration of the hydrological model. Our main findings are that even with low station density, the spatial patterns found in the HYRAS data set are well reproduced. With low station density (years 1999-2006) our dataset underestimates precipitation compared to HYRAS and EOBS, notably during the winter. However, interpolation based on the same set of stations overestimates precipitation compared to EOBS for the years 2006-2014. This discrepancy disappears when switching to the high station density. We also analyze the robustness of the hourly precipitation fields by comparing with stations not used during interpolation. Specific issues regarding the data when creating the gridded precipitation fields will be highlighted. Finally, the datasets are used to drive an hourly and daily gridded WFLOW_HBV model of the Rhine at the same spatial resolution. Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201 Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., Gratzki, A. 2013: A Central European precipitation climatology - Part 1: Generation and validation of a high-resolution gridded daily data set (HYRAS). Meteorologische Zeitschrift, 22(3), 235 256 Weerts, A.H., D. Meißner, and S. Rademacher, 2008. Input data rainfall-runoff model operational system FEWS-NL & FEWS-DE. Technical report, Deltares.

High Resolution Stratigraphic Mapping in Complex Terrain: A Comparison of Traditional Remote Sensing Techniques with Unmanned Aerial Vehicle - Structure from Motion Photogrammetry

NASA Astrophysics Data System (ADS)

Nesbit, P. R.; Hugenholtz, C.; Durkin, P.; Hubbard, S. M.; Kucharczyk, M.; Barchyn, T.

2016-12-01

Remote sensing and digital mapping have started to revolutionize geologic mapping in recent years as a result of their realized potential to provide high resolution 3D models of outcrops to assist with interpretation, visualization, and obtaining accurate measurements of inaccessible areas. However, in stratigraphic mapping applications in complex terrain, it is difficult to acquire information with sufficient detail at a wide spatial coverage with conventional techniques. We demonstrate the potential of a UAV and Structure from Motion (SfM) photogrammetric approach for improving 3D stratigraphic mapping applications within a complex badland topography. Our case study is performed in Dinosaur Provincial Park (Alberta, Canada), mapping late Cretaceous fluvial meander belt deposits of the Dinosaur Park formation amidst a succession of steeply sloping hills and abundant drainages - creating a challenge for stratigraphic mapping. The UAV-SfM dataset (2 cm spatial resolution) is compared directly with a combined satellite and aerial LiDAR dataset (30 cm spatial resolution) to reveal advantages and limitations of each dataset before presenting a unique workflow that utilizes the dense point cloud from the UAV-SfM dataset for analysis. The UAV-SfM dense point cloud minimizes distortion, preserves 3D structure, and records an RGB attribute - adding potential value in future studies. The proposed UAV-SfM workflow allows for high spatial resolution remote sensing of stratigraphy in complex topographic environments. This extended capability can add value to field observations and has the potential to be integrated with subsurface petroleum models.

Operational use of open satellite data for marine water quality monitoring

NASA Astrophysics Data System (ADS)

Symeonidis, Panagiotis; Vakkas, Theodoros

2017-09-01

The purpose of this study was to develop an operational platform for marine water quality monitoring using near real time satellite data. The developed platform utilizes free and open satellite data available from different data sources like COPERNICUS, the European Earth Observation Initiative, or NASA, from different satellites and instruments. The quality of the marine environment is operationally evaluated using parameters like chlorophyll-a concentration, water color and Sea Surface Temperature (SST). For each parameter, there are more than one dataset available, from different data sources or satellites, to allow users to select the most appropriate dataset for their area or time of interest. The above datasets are automatically downloaded from the data provider's services and ingested to the central, spatial engine. The spatial data platform uses the Postgresql database with the PostGIS extension for spatial data storage and Geoserver for the provision of the spatial data services. The system provides daily, 10 days and monthly maps and time series of the above parameters. The information is provided using a web client which is based on the GET SDI PORTAL, an easy to use and feature rich geospatial visualization and analysis platform. The users can examine the temporal variation of the parameters using a simple time animation tool. In addition, with just one click on the map, the system provides an interactive time series chart for any of the parameters of the available datasets. The platform can be offered as Software as a Service (SaaS) to any area in the Mediterranean region.

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

High-Resolution Digital Terrain Models of the Sacramento/San Joaquin Delta Region, California

USGS Publications Warehouse

Coons, Tom; Soulard, Christopher E.; Knowles, Noah

2008-01-01

The U.S. Geological Survey (USGS) Western Region Geographic Science Center, in conjunction with the USGS Water Resources Western Branch of Regional Research, has developed a high-resolution elevation dataset covering the Sacramento/San Joaquin Delta region of California. The elevation data were compiled photogrammically from aerial photography (May 2002) with a scale of 1:15,000. The resulting dataset has a 10-meter horizontal resolution grid of elevation values. The vertical accuracy was determined to be 1 meter. Two versions of the elevation data are available: the first dataset has all water coded as zero, whereas the second dataset has bathymetry data merged with the elevation data. The projection of both datasets is set to UTM Zone 10, NAD 1983. The elevation data are clipped into files that spatially approximate 7.5-minute USGS quadrangles, with about 100 meters of overlap to facilitate combining the files into larger regions without data gaps. The files are named after the 7.5-minute USGS quadrangles that cover the same general spatial extent. File names that include a suffix (_b) indicate that the bathymetry data are included (for example, sac_east versus sac_east_b). These files are provided in ESRI Grid format.

SPARQL Query Re-writing Using Partonomy Based Transformation Rules

NASA Astrophysics Data System (ADS)

Jain, Prateek; Yeh, Peter Z.; Verma, Kunal; Henson, Cory A.; Sheth, Amit P.

Often the information present in a spatial knowledge base is represented at a different level of granularity and abstraction than the query constraints. For querying ontology's containing spatial information, the precise relationships between spatial entities has to be specified in the basic graph pattern of SPARQL query which can result in long and complex queries. We present a novel approach to help users intuitively write SPARQL queries to query spatial data, rather than relying on knowledge of the ontology structure. Our framework re-writes queries, using transformation rules to exploit part-whole relations between geographical entities to address the mismatches between query constraints and knowledge base. Our experiments were performed on completely third party datasets and queries. Evaluations were performed on Geonames dataset using questions from National Geographic Bee serialized into SPARQL and British Administrative Geography Ontology using questions from a popular trivia website. These experiments demonstrate high precision in retrieval of results and ease in writing queries.

Spatiotemporal Permutation Entropy as a Measure for Complexity of Cardiac Arrhythmia

NASA Astrophysics Data System (ADS)

Schlemmer, Alexander; Berg, Sebastian; Lilienkamp, Thomas; Luther, Stefan; Parlitz, Ulrich

2018-05-01

Permutation entropy (PE) is a robust quantity for measuring the complexity of time series. In the cardiac community it is predominantly used in the context of electrocardiogram (ECG) signal analysis for diagnoses and predictions with a major application found in heart rate variability parameters. In this article we are combining spatial and temporal PE to form a spatiotemporal PE that captures both, complexity of spatial structures and temporal complexity at the same time. We demonstrate that the spatiotemporal PE (STPE) quantifies complexity using two datasets from simulated cardiac arrhythmia and compare it to phase singularity analysis and spatial PE (SPE). These datasets simulate ventricular fibrillation (VF) on a two-dimensional and a three-dimensional medium using the Fenton-Karma model. We show that SPE and STPE are robust against noise and demonstrate its usefulness for extracting complexity features at different spatial scales.

Demonstration of Airborne Wide Area Assessment Technologies at Pueblo Precision Bombing Ranges, Colorado. Hyperspectral Imaging, Version 2.0

DTIC Science & Technology

2007-09-27

the spatial and spectral resolution ...variety of geological and vegetation mapping efforts, the Hymap sensor offered the best available combination of spectral and spatial resolution , signal... The limitations of the technology currently relate to spatial and spectral resolution and geo- correction accuracy. Secondly, HSI datasets

Developing a new global network of river reaches from merged satellite-derived datasets

NASA Astrophysics Data System (ADS)

Lion, C.; Allen, G. H.; Beighley, E.; Pavelsky, T.

2015-12-01

In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water extent, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope must be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcsecond spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus ~2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our preliminary results for a portion of South America and demonstrate the strengths and weaknesses of the method.

Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach

NASA Astrophysics Data System (ADS)

Paul, Subir; Nagesh Kumar, D.

2018-04-01

Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

Testing the effect of the Himalayan mountains as a physical barrier to gene flow in Hippophae tibetana Schlect. (Elaeagnaceae)

PubMed Central

Qiong, La; Zhang, Wenju; Wang, Hao; Zeng, Liyan; Birks, H. John B.; Zhong, Yang

2017-01-01

Hippophae tibetana is a small, dioecious wind-pollinated shrub endemic to the Tibetan-Qinghai Plateau. It is one of the shrubs that occur at very high elevations (5250 m a.s.l.). The Himalayan mountains provides a significant geographical barrier to the Qinghai-Tibetan Plateau, dividing the Himalayan area into two regions with Nepal to the south and Tibet to the north. There is no information on how the Himalayan mountains influence gene flow and population differentiation of alpine plants. In this study, we analyzed eight nuclear microsatellite markers and cpDNA trnT-trnF regions to test the role of the Himalayan mountains as a barrier to gene flow between populations of H. tibetana. We also examined the fine-scale genetic structure within a population of H. tibetana on the north slope of Mount (Mt.) Everest. For microsatellite analyses, a total of 241 individuals were sampled from seven populations in our study area (4 from Nepal, 3 from Tibet), including 121 individuals that were spatially mapped within a 100 m × 100 m plot. To test for seed flow, the cpDNA trnT-trnF regions of 100 individuals from 6 populations (4 from Nepal, 2 from Tibet) were also sequenced. Significant genetic differentiation was detected between the two regions by both microsatellite and cpDNA data analyses. These two datasets agree about southern and northern population differentiation, indicating that the Himalayan mountains represent a barrier to H. tibetana limiting gene flow between these two areas. At a fine scale, spatial autocorrelation analysis suggests significant genetic structure within a distance of less than 45 m, which may be attributed mainly to vegetative reproduction and habitat fragmentation, as well as limited gene flow. PMID:28489850

Testing the effect of the Himalayan mountains as a physical barrier to gene flow in Hippophae tibetana Schlect. (Elaeagnaceae).

PubMed

Qiong, La; Zhang, Wenju; Wang, Hao; Zeng, Liyan; Birks, H John B; Zhong, Yang

2017-01-01

Hippophae tibetana is a small, dioecious wind-pollinated shrub endemic to the Tibetan-Qinghai Plateau. It is one of the shrubs that occur at very high elevations (5250 m a.s.l.). The Himalayan mountains provides a significant geographical barrier to the Qinghai-Tibetan Plateau, dividing the Himalayan area into two regions with Nepal to the south and Tibet to the north. There is no information on how the Himalayan mountains influence gene flow and population differentiation of alpine plants. In this study, we analyzed eight nuclear microsatellite markers and cpDNA trnT-trnF regions to test the role of the Himalayan mountains as a barrier to gene flow between populations of H. tibetana. We also examined the fine-scale genetic structure within a population of H. tibetana on the north slope of Mount (Mt.) Everest. For microsatellite analyses, a total of 241 individuals were sampled from seven populations in our study area (4 from Nepal, 3 from Tibet), including 121 individuals that were spatially mapped within a 100 m × 100 m plot. To test for seed flow, the cpDNA trnT-trnF regions of 100 individuals from 6 populations (4 from Nepal, 2 from Tibet) were also sequenced. Significant genetic differentiation was detected between the two regions by both microsatellite and cpDNA data analyses. These two datasets agree about southern and northern population differentiation, indicating that the Himalayan mountains represent a barrier to H. tibetana limiting gene flow between these two areas. At a fine scale, spatial autocorrelation analysis suggests significant genetic structure within a distance of less than 45 m, which may be attributed mainly to vegetative reproduction and habitat fragmentation, as well as limited gene flow.

Masting in ponderosa pine: comparisons of pollen and seed over space and time.

PubMed

Mooney, Kailen A; Linhart, Yan B; Snyder, Marc A

2011-03-01

Many plant species exhibit variable and synchronized reproduction, or masting, but less is known of the spatial scale of synchrony, effects of climate, or differences between patterns of pollen and seed production. We monitored pollen and seed cone production for seven Pinus ponderosa populations (607 trees) separated by up to 28 km and 1,350 m in elevation in Boulder County, Colorado, USA for periods of 4-31 years for a mean per site of 8.7 years for pollen and 12.1 for seed cone production. We also analyzed climate data and a published dataset on 21 years of seed production for an eighth population (Manitou) 100 km away. Individual trees showed high inter-annual variation in reproduction. Synchrony was high within populations, but quickly became asynchronous among populations with a combination of increasing distance and elevational difference. Inter-annual variation in temperature and precipitation had differing influences on seed production for Boulder County and Manitou. We speculate that geographically variable effects of climate on reproduction arise from environmental heterogeneity and population genetic differentiation, which in turn result in localized synchrony. Although individual pines produce pollen and seed, only one-third of the covariation within trees was shared. As compared to seed cones, pollen had lower inter-annual variation at the level of the individual tree and was more synchronous. However, pollen and seed production were similar with respect to inter-annual variation at the population level, spatial scales of synchrony and associations with climate. Our results show that strong masting can occur at a localized scale, and that reproductive patterns can differ between pollen and seed cone production in a hermaphroditic plant.

A multiscale Bayesian data integration approach for mapping air dose rates around the Fukushima Daiichi Nuclear Power Plant.

PubMed

Wainwright, Haruko M; Seki, Akiyuki; Chen, Jinsong; Saito, Kimiaki

2017-02-01

This paper presents a multiscale data integration method to estimate the spatial distribution of air dose rates in the regional scale around the Fukushima Daiichi Nuclear Power Plant. We integrate various types of datasets, such as ground-based walk and car surveys, and airborne surveys, all of which have different scales, resolutions, spatial coverage, and accuracy. This method is based on geostatistics to represent spatial heterogeneous structures, and also on Bayesian hierarchical models to integrate multiscale, multi-type datasets in a consistent manner. The Bayesian method allows us to quantify the uncertainty in the estimates, and to provide the confidence intervals that are critical for robust decision-making. Although this approach is primarily data-driven, it has great flexibility to include mechanistic models for representing radiation transport or other complex correlations. We demonstrate our approach using three types of datasets collected at the same time over Fukushima City in Japan: (1) coarse-resolution airborne surveys covering the entire area, (2) car surveys along major roads, and (3) walk surveys in multiple neighborhoods. Results show that the method can successfully integrate three types of datasets and create an integrated map (including the confidence intervals) of air dose rates over the domain in high resolution. Moreover, this study provides us with various insights into the characteristics of each dataset, as well as radiocaesium distribution. In particular, the urban areas show high heterogeneity in the contaminant distribution due to human activities as well as large discrepancy among different surveys due to such heterogeneity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Percentage of Protected Area Amounts within each Watershed Boundary for the Conterminous US

EPA Science Inventory

Abstract: This dataset uses spatial information from the Watershed Boundary Dataset (WBD, March 2011) and the Protected Areas Database of the United States (PAD-US Version 1.0). The resulting data layer, with percentages of protected areas by category, was created using the ATtI...

Leveraging freely available remote sensing and ancillary datasets for semi-automated identification of potential wetland areas using a Geographic Information System (GIS).

DOT National Transportation Integrated Search

2016-06-01

The purpose of this study was to develop a wetland identification tool that makes use of freely available geospatial : datasets to identify potential wetland locations at a spatial scale relevant for transportation corridor assessments. The tool was ...

ACCURACY OF THE 1992 NATIONAL LAND COVER DATASET AREA ESTIMATES: AN ANALYSIS AT MULTIPLE SPATIAL EXTENTS

EPA Science Inventory

Abstract for poster presentation:

Site-specific accuracy assessments evaluate fine-scale accuracy of land-use/land-cover(LULC) datasets but provide little insight into accuracy of area estimates of LULC

classes derived from sampling units of varying size. Additiona...

An Open-Access Modeled Passenger Flow Matrix for the Global Air Network in 2010

PubMed Central

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J.; Fik, Timothy J.; Tatem, Andrew J.

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data. PMID:23691194

An open-access modeled passenger flow matrix for the global air network in 2010.

PubMed

Huang, Zhuojie; Wu, Xiao; Garcia, Andres J; Fik, Timothy J; Tatem, Andrew J

2013-01-01

The expanding global air network provides rapid and wide-reaching connections accelerating both domestic and international travel. To understand human movement patterns on the network and their socioeconomic, environmental and epidemiological implications, information on passenger flow is required. However, comprehensive data on global passenger flow remain difficult and expensive to obtain, prompting researchers to rely on scheduled flight seat capacity data or simple models of flow. This study describes the construction of an open-access modeled passenger flow matrix for all airports with a host city-population of more than 100,000 and within two transfers of air travel from various publicly available air travel datasets. Data on network characteristics, city population, and local area GDP amongst others are utilized as covariates in a spatial interaction framework to predict the air transportation flows between airports. Training datasets based on information from various transportation organizations in the United States, Canada and the European Union were assembled. A log-linear model controlling the random effects on origin, destination and the airport hierarchy was then built to predict passenger flows on the network, and compared to the results produced using previously published models. Validation analyses showed that the model presented here produced improved predictive power and accuracy compared to previously published models, yielding the highest successful prediction rate at the global scale. Based on this model, passenger flows between 1,491 airports on 644,406 unique routes were estimated in the prediction dataset. The airport node characteristics and estimated passenger flows are freely available as part of the Vector-Borne Disease Airline Importation Risk (VBD-Air) project at: www.vbd-air.com/data.

Unleashing spatially distributed ecohydrology modeling using Big Data tools

NASA Astrophysics Data System (ADS)

Miles, B.; Idaszak, R.

2015-12-01

Physically based spatially distributed ecohydrology models are useful for answering science and management questions related to the hydrology and biogeochemistry of prairie, savanna, forested, as well as urbanized ecosystems. However, these models can produce hundreds of gigabytes of spatial output for a single model run over decadal time scales when run at regional spatial scales and moderate spatial resolutions (~100-km2+ at 30-m spatial resolution) or when run for small watersheds at high spatial resolutions (~1-km2 at 3-m spatial resolution). Numerical data formats such as HDF5 can store arbitrarily large datasets. However even in HPC environments, there are practical limits on the size of single files that can be stored and reliably backed up. Even when such large datasets can be stored, querying and analyzing these data can suffer from poor performance due to memory limitations and I/O bottlenecks, for example on single workstations where memory and bandwidth are limited, or in HPC environments where data are stored separately from computational nodes. The difficulty of storing and analyzing spatial data from ecohydrology models limits our ability to harness these powerful tools. Big Data tools such as distributed databases have the potential to surmount the data storage and analysis challenges inherent to large spatial datasets. Distributed databases solve these problems by storing data close to computational nodes while enabling horizontal scalability and fault tolerance. Here we present the architecture of and preliminary results from PatchDB, a distributed datastore for managing spatial output from the Regional Hydro-Ecological Simulation System (RHESSys). The initial version of PatchDB uses message queueing to asynchronously write RHESSys model output to an Apache Cassandra cluster. Once stored in the cluster, these data can be efficiently queried to quickly produce both spatial visualizations for a particular variable (e.g. maps and animations), as well as point time series of arbitrary variables at arbitrary points in space within a watershed or river basin. By treating ecohydrology modeling as a Big Data problem, we hope to provide a platform for answering transformative science and management questions related to water quantity and quality in a world of non-stationary climate.

Improved statistical method for temperature and salinity quality control

NASA Astrophysics Data System (ADS)

Gourrion, Jérôme; Szekely, Tanguy

2017-04-01

Climate research and Ocean monitoring benefit from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of an automatic quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to late 2015, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has already been implemented in the latest version of the delayed-time CMEMS in-situ dataset and will be deployed soon in the equivalent near-real time products.

Dataset on spatial distribution and location of universities in Nigeria.

PubMed

Adeyemi, G A; Edeki, S O

2018-06-01

Access to quality educational system, and the location of educational institutions are of great importance for future prospect of youth in any nation. These in return, have great effects on the economy growth and development of any country. Thus, the dataset contained in this article examines and explains the spatial distribution of universities in the Nigeria system of education. Data from the university commission, Nigeria, as at December 2017 are used. These include all the 40 federal universities, 44 states universities, and 69 private universities making a total of 153 universities in the Nigerian system of education. The data analysis is via the Geographic Information System (GIS) software. The dataset contained in this article will be of immense assistance to the national educational policy makers, parents, and potential students as regards smart and reliable decision making academically.

The importance of accurate road data for spatial applications in public health: customizing a road network

PubMed Central

Frizzelle, Brian G; Evenson, Kelly R; Rodriguez, Daniel A; Laraia, Barbara A

2009-01-01

Background Health researchers have increasingly adopted the use of geographic information systems (GIS) for analyzing environments in which people live and how those environments affect health. One aspect of this research that is often overlooked is the quality and detail of the road data and whether or not it is appropriate for the scale of analysis. Many readily available road datasets, both public domain and commercial, contain positional errors or generalizations that may not be compatible with highly accurate geospatial locations. This study examined the accuracy, completeness, and currency of four readily available public and commercial sources for road data (North Carolina Department of Transportation, StreetMap Pro, TIGER/Line 2000, TIGER/Line 2007) relative to a custom road dataset which we developed and used for comparison. Methods and Results A custom road network dataset was developed to examine associations between health behaviors and the environment among pregnant and postpartum women living in central North Carolina in the United States. Three analytical measures were developed to assess the comparative accuracy and utility of four publicly and commercially available road datasets and the custom dataset in relation to participants' residential locations over three time periods. The exclusion of road segments and positional errors in the four comparison road datasets resulted in between 5.9% and 64.4% of respondents lying farther than 15.24 meters from their nearest road, the distance of the threshold set by the project to facilitate spatial analysis. Agreement, using a Pearson's correlation coefficient, between the customized road dataset and the four comparison road datasets ranged from 0.01 to 0.82. Conclusion This study demonstrates the importance of examining available road datasets and assessing their completeness, accuracy, and currency for their particular study area. This paper serves as an example for assessing the feasibility of readily available commercial or public road datasets, and outlines the steps by which an improved custom dataset for a study area can be developed. PMID:19409088

Spatio-temporal synchrony of influenza in cities across Israel: the "Israel is one city" hypothesis.

PubMed

Barnea, Oren; Huppert, Amit; Katriel, Guy; Stone, Lewi

2014-01-01

We analysed an 11-year dataset (1998-2009) of Influenza-Like Illness (ILI) that was based on surveillance of ∽23% of Israel's population. We examined whether the level of synchrony of ILI epidemics in Israel's 12 largest cities is high enough to view Israel as a single epidemiological unit. Two methods were developed to assess the synchrony: (1) City-specific attack rates were fitted to a simple model in order to estimate the temporal differences in attack rates and spatial differences in reporting rates of ILI. The model showed good fit to the data (R2  =  0.76) and revealed considerable differences in reporting rates of ILI in different cities (up to a factor of 2.2). (2) A statistical test was developed to examine the null hypothesis (H0) that ILI incidence curves in two cities are essentially identical, and was tested using ILI data. Upon examining all possible pairs of incidence curves, 77.4% of pairs were found not to be different (H0 was not rejected). It was concluded that all cities generally have the same attack rate and follow the same epidemic curve each season, although the attack rate changes from season to season, providing strong support for the "Israel is one city" hypothesis. The cities which were the most out of synchronization were Bnei Brak, Beersheba and Haifa, the latter two being geographically remote from all other cities in the dataset and the former geographically very close to several other cities but socially separate due to being populated almost exclusively by ultra-orthodox Jews. Further evidence of assortative mixing of the ultra-orthodox population can be found in the 2001-2002 season, when ultra-orthodox cities and neighborhoods showed distinctly different incidence curves compared to the general population.

Wolves Recolonizing Islands: Genetic Consequences and Implications for Conservation and Management.

PubMed

Plumer, Liivi; Keis, Marju; Remm, Jaanus; Hindrikson, Maris; Jõgisalu, Inga; Männil, Peep; Kübarsepp, Marko; Saarma, Urmas

2016-01-01

After a long and deliberate persecution, the grey wolf (Canis lupus) is slowly recolonizing its former areas in Europe, and the genetic consequences of this process are of particular interest. Wolves, though present in mainland Estonia for a long time, have only recently started to recolonize the country's two largest islands, Saaremaa and Hiiumaa. The main objective of this study was to analyse wolf population structure and processes in Estonia, with particular attention to the recolonization of islands. Fifteen microsatellite loci were genotyped for 185 individuals across Estonia. As a methodological novelty, all putative wolf-dog hybrids were identified and removed (n = 17) from the dataset beforehand to avoid interference of dog alleles in wolf population analysis. After the preliminary filtering, our final dataset comprised of 168 "pure" wolves. We recommend using hybrid-removal step as a standard precautionary procedure not only for wolf population studies, but also for other taxa prone to hybridization. STRUCTURE indicated four genetic groups in Estonia. Spatially explicit DResD analysis identified two areas, one of them on Saaremaa island and the other in southwestern Estonia, where neighbouring individuals were genetically more similar than expected from an isolation-by-distance null model. Three blending areas and two contrasting transition zones were identified in central Estonia, where the sampled individuals exhibited strong local differentiation over relatively short distance. Wolves on the largest Estonian islands are part of human-wildlife conflict due to livestock depredation. Negative public attitude, especially on Saaremaa where sheep herding is widespread, poses a significant threat for island wolves. To maintain the long-term viability of the wolf population on Estonian islands, not only wolf hunting quota should be targeted with extreme care, but effective measures should be applied to avoid inbreeding and minimize conflicts with local communities and stakeholders.

Characterizing the heterogeneity of tumor tissues from spatially resolved molecular measures

PubMed Central

Zavodszky, Maria I.

2017-01-01

Background Tumor heterogeneity can manifest itself by sub-populations of cells having distinct phenotypic profiles expressed as diverse molecular, morphological and spatial distributions. This inherent heterogeneity poses challenges in terms of diagnosis, prognosis and efficient treatment. Consequently, tools and techniques are being developed to properly characterize and quantify tumor heterogeneity. Multiplexed immunofluorescence (MxIF) is one such technology that offers molecular insight into both inter-individual and intratumor heterogeneity. It enables the quantification of both the concentration and spatial distribution of 60+ proteins across a tissue section. Upon bioimage processing, protein expression data can be generated for each cell from a tissue field of view. Results The Multi-Omics Heterogeneity Analysis (MOHA) tool was developed to compute tissue heterogeneity metrics from MxIF spatially resolved tissue imaging data. This technique computes the molecular state of each cell in a sample based on a pathway or gene set. Spatial states are then computed based on the spatial arrangements of the cells as distinguished by their respective molecular states. MOHA computes tissue heterogeneity metrics from the distributions of these molecular and spatially defined states. A colorectal cancer cohort of approximately 700 subjects with MxIF data is presented to demonstrate the MOHA methodology. Within this dataset, statistically significant correlations were found between the intratumor AKT pathway state diversity and cancer stage and histological tumor grade. Furthermore, intratumor spatial diversity metrics were found to correlate with cancer recurrence. Conclusions MOHA provides a simple and robust approach to characterize molecular and spatial heterogeneity of tissues. Research projects that generate spatially resolved tissue imaging data can take full advantage of this useful technique. The MOHA algorithm is implemented as a freely available R script (see supplementary information). PMID:29190747

NLCD - MODIS albedo data

EPA Pesticide Factsheets

The NLCD-MODIS land cover-albedo database integrates high-quality MODIS albedo observations with areas of homogeneous land cover from NLCD. The spatial resolution (pixel size) of the database is 480m-x-480m aligned to the standardized UGSG Albers Equal-Area projection. The spatial extent of the database is the continental United States. This dataset is associated with the following publication:Wickham , J., C.A. Barnes, and T. Wade. Combining NLCD and MODIS to Create a Land Cover-Albedo Dataset for the Continental United States. REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 170(0): 143-153, (2015).

smokeSALUD: exploring the effect of demographic change on the smoking prevalence at municipality level in Austria.

PubMed

Tomintz, Melanie; Kosar, Bernhard; Clarke, Graham

2016-10-07

Reducing the smoking population is still high on the policy agenda, as smoking leads to many preventable diseases, such as lung cancer, heart disease, diabetes, and more. In Austria, data on smoking prevalence only exists at the federal state level. This provides an interesting overview about the current health situation, but for regional planning authorities these data are often insufficient as they can hide pockets of high and low smoking prevalence in certain municipalities. This paper presents a spatial-temporal change of estimated smokers for municipalities from 2001 and 2011. A synthetic dataset of smokers is built by combining individual large-scale survey data and small area census data using a deterministic spatial microsimulation approach. Statistical analysis, including chi-square test and binary logistic regression, are applied to find the best variables for the simulation model and to validate its results. As no easy-to-use spatial microsimulation software for non-programmers is available yet, a flexible web-based spatial microsimulation application for health decision support (called simSALUD) has been developed and used for these analyses. The results of the simulation show in general a decrease of smoking prevalence within municipalities between 2001 and 2011 and differences within areas are identified. These results are especially valuable to policy decision makers for future planning strategies. This case study shows the application of smokeSALUD to model the spatial-temporal changes in the smoking population in Austria between 2001 and 2011. This is important as no data on smoking exists at this geographical scale (municipality). However, spatial microsimulation models are useful tools to estimate small area health data and to overcome these problems. The simulations and analysis should support health decision makers to identify hot spots of smokers and this should help to show where to spend health resources best in order to reduce health inequalities.

Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

DOE PAGES

Wang, Kai; Mao, Jiafu; Dickinson, Robert; ...

2013-06-05

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR ’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. These findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns,more » but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. Here, we identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.« less

Spectral-spatial hyperspectral image classification using super-pixel-based spatial pyramid representation

NASA Astrophysics Data System (ADS)

Fan, Jiayuan; Tan, Hui Li; Toomik, Maria; Lu, Shijian

2016-10-01

Spatial pyramid matching has demonstrated its power for image recognition task by pooling features from spatially increasingly fine sub-regions. Motivated by the concept of feature pooling at multiple pyramid levels, we propose a novel spectral-spatial hyperspectral image classification approach using superpixel-based spatial pyramid representation. This technique first generates multiple superpixel maps by decreasing the superpixel number gradually along with the increased spatial regions for labelled samples. By using every superpixel map, sparse representation of pixels within every spatial region is then computed through local max pooling. Finally, features learned from training samples are aggregated and trained by a support vector machine (SVM) classifier. The proposed spectral-spatial hyperspectral image classification technique has been evaluated on two public hyperspectral datasets, including the Indian Pines image containing 16 different agricultural scene categories with a 20m resolution acquired by AVIRIS and the University of Pavia image containing 9 land-use categories with a 1.3m spatial resolution acquired by the ROSIS-03 sensor. Experimental results show significantly improved performance compared with the state-of-the-art works. The major contributions of this proposed technique include (1) a new spectral-spatial classification approach to generate feature representation for hyperspectral image, (2) a complementary yet effective feature pooling approach, i.e. the superpixel-based spatial pyramid representation that is used for the spatial correlation study, (3) evaluation on two public hyperspectral image datasets with superior image classification performance.

Geographic variation in the intended choice of adjuvant treatments for women diagnosed with screen-detected breast cancer in Queensland.

PubMed

Hsieh, Jeff Ching-Fu; Cramb, Susanna M; McGree, James M; Dunn, Nathan A M; Baade, Peter D; Mengersen, Kerrie L

2015-12-02

Although early diagnosis and improved treatment can reduce breast cancer mortality, there still appears to be a geographic differential in patient outcomes. This study aims to determine and quantify spatial inequalities in intended adjuvant (radio-, chemo- and hormonal) therapy usage among women with screen-detected breast cancer in Queensland, Australia. Linked population-based datasets from BreastScreen Queensland and the Queensland Cancer Registry during 1997-2008 for women aged 40-89 years were used. We adopted a Bayesian shared spatial component model to evaluate the relative intended use of each adjuvant therapy across 478 areas as well as common spatial patterns between treatments. Women living closer to a cancer treatment facility were more likely to intend to use adjuvant therapy. This was particularly marked for radiotherapy when travel time to the closest radiation facility was 4 + h (OR =0.41, 95 % CrI: [0.23, 0.74]) compared to <1 h. The shared spatial effect increased towards the centres with concentrations of radiotherapy facilities, in north-east (Townsville) and south-east (Brisbane) regions of Queensland. Moreover, the presence of residual shared spatial effects indicates that there are other unmeasured geographical barriers influencing women's treatment choices. This highlights the need to identify the additional barriers that impact on treatment intentions among women diagnosed with screen-detected breast cancer, particularly for those women living further away from cancer treatment centers.

Processing and population genetic analysis of multigenic datasets with ProSeq3 software.

PubMed

Filatov, Dmitry A

2009-12-01

The current tendency in molecular population genetics is to use increasing numbers of genes in the analysis. Here I describe a program for handling and population genetic analysis of DNA polymorphism data collected from multiple genes. The program includes a sequence/alignment editor and an internal relational database that simplify the preparation and manipulation of multigenic DNA polymorphism datasets. The most commonly used DNA polymorphism analyses are implemented in ProSeq3, facilitating population genetic analysis of large multigenic datasets. Extensive input/output options make ProSeq3 a convenient hub for sequence data processing and analysis. The program is available free of charge from http://dps.plants.ox.ac.uk/sequencing/proseq.htm.

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

Location Prediction Based on Transition Probability Matrices Constructing from Sequential Rules for Spatial-Temporal K-Anonymity Dataset

PubMed Central

Liu, Zhao; Zhu, Yunhong; Wu, Chenxue

2016-01-01

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502

Human population dynamics in Europe over the Last Glacial Maximum.

PubMed

Tallavaara, Miikka; Luoto, Miska; Korhonen, Natalia; Järvinen, Heikki; Seppä, Heikki

2015-07-07

The severe cooling and the expansion of the ice sheets during the Last Glacial Maximum (LGM), 27,000-19,000 y ago (27-19 ky ago) had a major impact on plant and animal populations, including humans. Changes in human population size and range have affected our genetic evolution, and recent modeling efforts have reaffirmed the importance of population dynamics in cultural and linguistic evolution, as well. However, in the absence of historical records, estimating past population levels has remained difficult. Here we show that it is possible to model spatially explicit human population dynamics from the pre-LGM at 30 ky ago through the LGM to the Late Glacial in Europe by using climate envelope modeling tools and modern ethnographic datasets to construct a population calibration model. The simulated range and size of the human population correspond significantly with spatiotemporal patterns in the archaeological data, suggesting that climate was a major driver of population dynamics 30-13 ky ago. The simulated population size declined from about 330,000 people at 30 ky ago to a minimum of 130,000 people at 23 ky ago. The Late Glacial population growth was fastest during Greenland interstadial 1, and by 13 ky ago, there were almost 410,000 people in Europe. Even during the coldest part of the LGM, the climatically suitable area for human habitation remained unfragmented and covered 36% of Europe.

Human population dynamics in Europe over the Last Glacial Maximum

PubMed Central

Tallavaara, Miikka; Luoto, Miska; Korhonen, Natalia; Järvinen, Heikki; Seppä, Heikki

2015-01-01

The severe cooling and the expansion of the ice sheets during the Last Glacial Maximum (LGM), 27,000–19,000 y ago (27–19 ky ago) had a major impact on plant and animal populations, including humans. Changes in human population size and range have affected our genetic evolution, and recent modeling efforts have reaffirmed the importance of population dynamics in cultural and linguistic evolution, as well. However, in the absence of historical records, estimating past population levels has remained difficult. Here we show that it is possible to model spatially explicit human population dynamics from the pre-LGM at 30 ky ago through the LGM to the Late Glacial in Europe by using climate envelope modeling tools and modern ethnographic datasets to construct a population calibration model. The simulated range and size of the human population correspond significantly with spatiotemporal patterns in the archaeological data, suggesting that climate was a major driver of population dynamics 30–13 ky ago. The simulated population size declined from about 330,000 people at 30 ky ago to a minimum of 130,000 people at 23 ky ago. The Late Glacial population growth was fastest during Greenland interstadial 1, and by 13 ky ago, there were almost 410,000 people in Europe. Even during the coldest part of the LGM, the climatically suitable area for human habitation remained unfragmented and covered 36% of Europe. PMID:26100880

HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition.

PubMed

James, Alex Pappachen; Fedorova, Irina; Ibrayev, Timur; Kudithipudi, Dhireesha

2017-06-01

Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.

A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Dey, Dipak K

2016-09-01

Our present work proposes a new survival model in a Bayesian context to analyze right-censored survival data for populations with a surviving fraction, assuming that the log failure time follows a generalized extreme value distribution. Many applications require a more flexible modeling of covariate information than a simple linear or parametric form for all covariate effects. It is also necessary to include the spatial variation in the model, since it is sometimes unexplained by the covariates considered in the analysis. Therefore, the nonlinear covariate effects and the spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have recently become extremely powerful in nonlinear regression. Our proposed model adopts a semiparametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of data availability and computational complexity, the conditionally autoregressive distribution is placed on the region-specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through analyses of simulated data examples as well as a dataset involving a colon cancer clinical trial from the state of Iowa. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mapping monkeypox transmission risk through time and space in the Congo Basin

USGS Publications Warehouse

Nakazawa, Yoshinori J.; Lash, R. Ryan; Carroll, Darin S.; Damon, Inger K.; Karem, Kevin L.; Reynolds, Mary G.; Osorio, Jorge E.; Rocke, Tonie E.; Malekani, Jean; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A. Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

Dataset on transcriptional profiles and the developmental characteristics of PDGFRα expressing lung fibroblasts.

PubMed

Endale, Mehari; Ahlfeld, Shawn; Bao, Erik; Chen, Xiaoting; Green, Jenna; Bess, Zach; Weirauch, Matthew; Xu, Yan; Perl, Anne Karina

2017-08-01

The following data are derived from key stages of acinar lung development and define the developmental role of lung interstitial fibroblasts expressing platelet-derived growth factor alpha (PDGFRα). This dataset is related to the research article entitled "Temporal, spatial, and phenotypical changes of PDGFRα expressing fibroblasts during late lung development" (Endale et al., 2017) [1]. At E16.5 (canalicular), E18.5 (saccular), P7 (early alveolar) and P28 (late alveolar), PDGFRα GFP mice, in conjunction with immunohistochemical markers, were utilized to define the spatiotemporal relationship of PDGFRα + fibroblasts to endothelial, stromal and epithelial cells in both the proximal and distal acinar lung. Complimentary analysis with flow cytometry was employed to determine changes in cellular proliferation, define lipofibroblast and myofibroblast populations via the presence of intracellular lipid or alpha smooth muscle actin (αSMA), and evaluate the expression of CD34, CD29, and Sca-1. Finally, PDGFRα + cells isolated at each stage of acinar lung development were subjected to RNA-Seq analysis, data was subjected to Bayesian timeline analysis and transcriptional factor promoter enrichment analysis.

Mapping monkeypox transmission risk through time and space in the Congo Basin.

PubMed

Nakazawa, Yoshinori; Lash, R Ryan; Carroll, Darin S; Damon, Inger K; Karem, Kevin L; Reynolds, Mary G; Osorio, Jorge E; Rocke, Tonie E; Malekani, Jean M; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

Mapping Monkeypox Transmission Risk through Time and Space in the Congo Basin

PubMed Central

Nakazawa, Yoshinori; Lash, R. Ryan; Carroll, Darin S.; Damon, Inger K.; Karem, Kevin L.; Reynolds, Mary G.; Osorio, Jorge E.; Rocke, Tonie E.; Malekani, Jean M.; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A. Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox. PMID:24040344

Oregon Cascades Play Fairway Analysis: Raster Datasets and Models

DOE Data Explorer

Adam Brandt

2015-11-15

This submission includes maps of the spatial distribution of basaltic, and felsic rocks in the Oregon Cascades. It also includes a final Play Fairway Analysis (PFA) model, with the heat and permeability composite risk segments (CRS) supplied separately. Metadata for each raster dataset can be found within the zip files, in the TIF images

PRISM Climate Group, Oregon State U

Science.gov Websites

FAQ PRISM Climate Data The PRISM Climate Group gathers climate observations from a wide range of monitoring networks, applies sophisticated quality control measures, and develops spatial climate datasets to reveal short- and long-term climate patterns. The resulting datasets incorporate a variety of modeling

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

USGS Publications Warehouse

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes

PubMed Central

Hurtado, Rafael G.; Floría, Luis Mario

2016-01-01

We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized. PMID:27853531

An agent-based approach for modeling dynamics of contagious disease spread

PubMed Central

Perez, Liliana; Dragicevic, Suzana

2009-01-01

Background The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts. Objective The objective of this study is to develop an agent-based modelling approach the integrates geographic information systems (GIS) to simulate the spread of a communicable disease in an urban environment, as a result of individuals' interactions in a geospatial context. Methods The methodology for simulating spatiotemporal dynamics of communicable disease propagation is presented and the model is implemented using measles outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by agents associated to places where they interact with other agents. They are endowed with mobility, through a transportation network allowing them to move between places within the urban environment, in order to represent the spatial heterogeneity and the complexity involved in infectious diseases diffusion. The model is implemented on georeferenced land use dataset from Metro Vancouver and makes use of census data sets from Statistics Canada for the municipality of Burnaby, BC, Canada study site. Results The results provide insights into the application of the model to calculate ratios of susceptible/infected in specific time frames and urban environments, due to its ability to depict the disease progression based on individuals' interactions. It is demonstrated that the dynamic spatial interactions within the population lead to high numbers of exposed individuals who perform stationary activities in areas after they have finished commuting. As a result, the sick individuals are concentrated in geographical locations like schools and universities. Conclusion The GIS-agent based model designed for this study can be easily customized to study the disease spread dynamics of any other communicable disease by simply adjusting the modeled disease timeline and/or the infection model and modifying the transmission process. This type of simulations can help to improve comprehension of disease spread dynamics and to take better steps towards the prevention and control of an epidemic outbreak. PMID:19656403

General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare

PubMed Central

2013-01-01

Background Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere. Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness. Method We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness. Results The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets. Conclusion In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets. PMID:24005003

Developing a Global Network of River Reaches in Preparation of SWOT

NASA Astrophysics Data System (ADS)

Lion, C.; Pavelsky, T.; Allen, G. H.; Beighley, E.; Schumann, G.; Durand, M. T.

2016-12-01

In 2020, the Surface Water and Ocean Topography satellite (SWOT), a joint mission of NASA/CNES/CSA/UK will be launched. One of its major products will be the measurements of continental water surfaces, including the width, height, and slope of rivers and the surface area and elevations of lakes. The mission will improve the monitoring of continental water and also our understanding of the interactions between different hydrologic reservoirs. For rivers, SWOT measurements of slope will be carried out over predefined river reaches. As such, an a priori dataset for rivers is needed in order to facilitate analysis of the raw SWOT data. The information required to produce this dataset includes measurements of river width, elevation, slope, planform, river network topology, and flow accumulation. To produce this product, we have linked two existing global datasets: the Global River Widths from Landsat (GRWL) database, which contains river centerline locations, widths, and a braiding index derived from Landsat imagery, and a modified version of the HydroSHEDS hydrologically corrected digital elevation product, which contains heights and flow accumulation measurements for streams at 3 arcseconds spatial resolution. Merging these two datasets requires considerable care. The difficulties, among others, lie in the difference of resolution: 30m versus 3 arseconds, and the age of the datasets: 2000 versus 2010 (some rivers have moved, the braided sections are different). As such, we have developed custom software to merge the two datasets, taking into account the spatial proximity of river channels in the two datasets and ensuring that flow accumulation in the final dataset always increases downstream. Here, we present our results for the globe.

Self-organizing maps: a versatile tool for the automatic analysis of untargeted imaging datasets.

PubMed

Franceschi, Pietro; Wehrens, Ron

2014-04-01

MS-based imaging approaches allow for location-specific identification of chemical components in biological samples, opening up possibilities of much more detailed understanding of biological processes and mechanisms. Data analysis, however, is challenging, mainly because of the sheer size of such datasets. This article presents a novel approach based on self-organizing maps, extending previous work in order to be able to handle the large number of variables present in high-resolution mass spectra. The key idea is to generate prototype images, representing spatial distributions of ions, rather than prototypical mass spectra. This allows for a two-stage approach, first generating typical spatial distributions and associated m/z bins, and later analyzing the interesting bins in more detail using accurate masses. The possibilities and advantages of the new approach are illustrated on an in-house dataset of apple slices. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatial interpolation quality assessments for soil sensor transect datasets

USDA-ARS?s Scientific Manuscript database

Near-ground geophysical soil sensors provide extremely valuable information for precision agriculture applications. Indeed, their readings can be used as proxy for many soil parameters. Typically, leave-one-out (loo) cross-validation (CV) of spatial interpolation of sensor data returns overly optimi...

TECA: A Parallel Toolkit for Extreme Climate Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prabhat, Mr; Ruebel, Oliver; Byna, Surendra

2012-03-12

We present TECA, a parallel toolkit for detecting extreme events in large climate datasets. Modern climate datasets expose parallelism across a number of dimensions: spatial locations, timesteps and ensemble members. We design TECA to exploit these modes of parallelism and demonstrate a prototype implementation for detecting and tracking three classes of extreme events: tropical cyclones, extra-tropical cyclones and atmospheric rivers. We process a modern TB-sized CAM5 simulation dataset with TECA, and demonstrate good runtime performance for the three case studies.

On the uncertainties associated with using gridded rainfall data as a proxy for observed

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

2011-09-01

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods)? This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia (SA) initially using gridded data as the source of rainfall input and then gauged rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged or point data. Rather the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

A high-resolution European dataset for hydrologic modeling

NASA Astrophysics Data System (ADS)

Ntegeka, Victor; Salamon, Peter; Gomes, Goncalo; Sint, Hadewij; Lorini, Valerio; Thielen, Jutta

2013-04-01

There is an increasing demand for large scale hydrological models not only in the field of modeling the impact of climate change on water resources but also for disaster risk assessments and flood or drought early warning systems. These large scale models need to be calibrated and verified against large amounts of observations in order to judge their capabilities to predict the future. However, the creation of large scale datasets is challenging for it requires collection, harmonization, and quality checking of large amounts of observations. For this reason, only a limited number of such datasets exist. In this work, we present a pan European, high-resolution gridded dataset of meteorological observations (EFAS-Meteo) which was designed with the aim to drive a large scale hydrological model. Similar European and global gridded datasets already exist, such as the HadGHCND (Caesar et al., 2006), the JRC MARS-STAT database (van der Goot and Orlandi, 2003) and the E-OBS gridded dataset (Haylock et al., 2008). However, none of those provide similarly high spatial resolution and/or a complete set of variables to force a hydrologic model. EFAS-Meteo contains daily maps of precipitation, surface temperature (mean, minimum and maximum), wind speed and vapour pressure at a spatial grid resolution of 5 x 5 km for the time period 1 January 1990 - 31 December 2011. It furthermore contains calculated radiation, which is calculated by using a staggered approach depending on the availability of sunshine duration, cloud cover and minimum and maximum temperature, and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The potential evapotranspiration was calculated using the Penman-Monteith equation with the above-mentioned meteorological variables. The dataset was created as part of the development of the European Flood Awareness System (EFAS) and has been continuously updated throughout the last years. The dataset variables are used as inputs to the hydrological calibration and validation of EFAS as well as for establishing long-term discharge "proxy" climatologies which can then in turn be used for statistical analysis to derive return periods or other time series derivatives. In addition, this dataset will be used to assess climatological trends in Europe. Unfortunately, to date no baseline dataset at the European scale exists to test the quality of the herein presented data. Hence, a comparison against other existing datasets can therefore only be an indication of data quality. Due to availability, a comparison was made for precipitation and temperature only, arguably the most important meteorological drivers for hydrologic models. A variety of analyses was undertaken at country scale against data reported to EUROSTAT and E-OBS datasets. The comparison revealed that while the datasets showed overall similar temporal and spatial patterns, there were some differences in magnitudes especially for precipitation. It is not straightforward to define the specific cause for these differences. However, in most cases the comparatively low observation station density appears to be the principal reason for the differences in magnitude.

Spatial, seasonal, and source variability in the stable oxygen and hydrogen isotopic composition of tap waters throughout the USA

USGS Publications Warehouse

Landwehr, Jurate M.; Coplen, Tyler B.; Stewart, David W.

2013-01-01

To assess spatial, seasonal, and source variability in stable isotopic composition of human drinking waters throughout the entire USA, we have constructed a database of δ18O and δ2H of US tap waters. An additional purpose was to create a publicly available dataset useful for evaluating the forensic applicability of these isotopes for human tissue source geolocation. Samples were obtained at 349 sites, from diverse population centres, grouped by surface hydrologic units for regional comparisons. Samples were taken concurrently during two contrasting seasons, summer and winter. Source supply (surface, groundwater, mixed, and cistern) and system (public and private) types were noted. The isotopic composition of tap waters exhibits large spatial and regional variation within each season as well as significant at-site differences between seasons at many locations, consistent with patterns found in environmental (river and precipitation) waters deriving from hydrologic processes influenced by geographic factors. However, anthropogenic factors, such as the population of a tap’s surrounding community and local availability from diverse sources, also influence the isotopic composition of tap waters. Even within a locale as small as a single metropolitan area, tap waters with greatly differing isotopic compositions can be found, so that tap water within a region may not exhibit the spatial or temporal coherence predicted for environmental water. Such heterogeneities can be confounding factors when attempting forensic inference of source water location, and they underscore the necessity of measurements, not just predictions, with which to characterize the isotopic composition of regional tap waters. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

A spatial hazard model for cluster detection on continuous indicators of disease: application to somatic cell score.

PubMed

Gay, Emilie; Senoussi, Rachid; Barnouin, Jacques

2007-01-01

Methods for spatial cluster detection dealing with diseases quantified by continuous variables are few, whereas several diseases are better approached by continuous indicators. For example, subclinical mastitis of the dairy cow is evaluated using a continuous marker of udder inflammation, the somatic cell score (SCS). Consequently, this study proposed to analyze spatialized risk and cluster components of herd SCS through a new method based on a spatial hazard model. The dataset included annual SCS for 34 142 French dairy herds for the year 2000, and important SCS risk factors: mean parity, percentage of winter and spring calvings, and herd size. The model allowed the simultaneous estimation of the effects of known risk factors and of potential spatial clusters on SCS, and the mapping of the estimated clusters and their range. Mean parity and winter and spring calvings were significantly associated with subclinical mastitis risk. The model with the presence of 3 clusters was highly significant, and the 3 clusters were attractive, i.e. closeness to cluster center increased the occurrence of high SCS. The three localizations were the following: close to the city of Troyes in the northeast of France; around the city of Limoges in the center-west; and in the southwest close to the city of Tarbes. The semi-parametric method based on spatial hazard modeling applies to continuous variables, and takes account of both risk factors and potential heterogeneity of the background population. This tool allows a quantitative detection but assumes a spatially specified form for clusters.

Emerging Technologies for Assessing Physical Activity Behaviors in Space and Time

PubMed Central

Hurvitz, Philip M.; Moudon, Anne Vernez; Kang, Bumjoon; Saelens, Brian E.; Duncan, Glen E.

2014-01-01

Precise measurement of physical activity is important for health research, providing a better understanding of activity location, type, duration, and intensity. This article describes a novel suite of tools to measure and analyze physical activity behaviors in spatial epidemiology research. We use individual-level, high-resolution, objective data collected in a space-time framework to investigate built and social environment influences on activity. First, we collect data with accelerometers, global positioning system units, and smartphone-based digital travel and photo diaries to overcome many limitations inherent in self-reported data. Behaviors are measured continuously over the full spectrum of environmental exposures in daily life, instead of focusing exclusively on the home neighborhood. Second, data streams are integrated using common timestamps into a single data structure, the “LifeLog.” A graphic interface tool, “LifeLog View,” enables simultaneous visualization of all LifeLog data streams. Finally, we use geographic information system SmartMap rasters to measure spatially continuous environmental variables to capture exposures at the same spatial and temporal scale as in the LifeLog. These technologies enable precise measurement of behaviors in their spatial and temporal settings but also generate very large datasets; we discuss current limitations and promising methods for processing and analyzing such large datasets. Finally, we provide applications of these methods in spatially oriented research, including a natural experiment to evaluate the effects of new transportation infrastructure on activity levels, and a study of neighborhood environmental effects on activity using twins as quasi-causal controls to overcome self-selection and reverse causation problems. In summary, the integrative characteristics of large datasets contained in LifeLogs and SmartMaps hold great promise for advancing spatial epidemiologic research to promote healthy behaviors. PMID:24479113

Demonstrating the robustness of population surveillance data: implications of error rates on demographic and mortality estimates.

PubMed

Fottrell, Edward; Byass, Peter; Berhane, Yemane

2008-03-25

As in any measurement process, a certain amount of error may be expected in routine population surveillance operations such as those in demographic surveillance sites (DSSs). Vital events are likely to be missed and errors made no matter what method of data capture is used or what quality control procedures are in place. The extent to which random errors in large, longitudinal datasets affect overall health and demographic profiles has important implications for the role of DSSs as platforms for public health research and clinical trials. Such knowledge is also of particular importance if the outputs of DSSs are to be extrapolated and aggregated with realistic margins of error and validity. This study uses the first 10-year dataset from the Butajira Rural Health Project (BRHP) DSS, Ethiopia, covering approximately 336,000 person-years of data. Simple programmes were written to introduce random errors and omissions into new versions of the definitive 10-year Butajira dataset. Key parameters of sex, age, death, literacy and roof material (an indicator of poverty) were selected for the introduction of errors based on their obvious importance in demographic and health surveillance and their established significant associations with mortality. Defining the original 10-year dataset as the 'gold standard' for the purposes of this investigation, population, age and sex compositions and Poisson regression models of mortality rate ratios were compared between each of the intentionally erroneous datasets and the original 'gold standard' 10-year data. The composition of the Butajira population was well represented despite introducing random errors, and differences between population pyramids based on the derived datasets were subtle. Regression analyses of well-established mortality risk factors were largely unaffected even by relatively high levels of random errors in the data. The low sensitivity of parameter estimates and regression analyses to significant amounts of randomly introduced errors indicates a high level of robustness of the dataset. This apparent inertia of population parameter estimates to simulated errors is largely due to the size of the dataset. Tolerable margins of random error in DSS data may exceed 20%. While this is not an argument in favour of poor quality data, reducing the time and valuable resources spent on detecting and correcting random errors in routine DSS operations may be justifiable as the returns from such procedures diminish with increasing overall accuracy. The money and effort currently spent on endlessly correcting DSS datasets would perhaps be better spent on increasing the surveillance population size and geographic spread of DSSs and analysing and disseminating research findings.

Spatially resolved RNA-sequencing of the embryonic heart identifies a role for Wnt/β-catenin signaling in autonomic control of heart rate

PubMed Central

Burkhard, Silja Barbara

2018-01-01

Development of specialized cells and structures in the heart is regulated by spatially -restricted molecular pathways. Disruptions in these pathways can cause severe congenital cardiac malformations or functional defects. To better understand these pathways and how they regulate cardiac development we used tomo-seq, combining high-throughput RNA-sequencing with tissue-sectioning, to establish a genome-wide expression dataset with high spatial resolution for the developing zebrafish heart. Analysis of the dataset revealed over 1100 genes differentially expressed in sub-compartments. Pacemaker cells in the sinoatrial region induce heart contractions, but little is known about the mechanisms underlying their development. Using our transcriptome map, we identified spatially restricted Wnt/β-catenin signaling activity in pacemaker cells, which was controlled by Islet-1 activity. Moreover, Wnt/β-catenin signaling controls heart rate by regulating pacemaker cellular response to parasympathetic stimuli. Thus, this high-resolution transcriptome map incorporating all cell types in the embryonic heart can expose spatially restricted molecular pathways critical for specific cardiac functions. PMID:29400650

Automating an integrated spatial data-mining model for landfill site selection

NASA Astrophysics Data System (ADS)

Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Aziz, Hamidi Abdul

2017-10-01

An integrated programming environment represents a robust approach to building a valid model for landfill site selection. One of the main challenges in the integrated model is the complicated processing and modelling due to the programming stages and several limitations. An automation process helps avoid the limitations and improve the interoperability between integrated programming environments. This work targets the automation of a spatial data-mining model for landfill site selection by integrating between spatial programming environment (Python-ArcGIS) and non-spatial environment (MATLAB). The model was constructed using neural networks and is divided into nine stages distributed between Matlab and Python-ArcGIS. A case study was taken from the north part of Peninsular Malaysia. 22 criteria were selected to utilise as input data and to build the training and testing datasets. The outcomes show a high-performance accuracy percentage of 98.2% in the testing dataset using 10-fold cross validation. The automated spatial data mining model provides a solid platform for decision makers to performing landfill site selection and planning operations on a regional scale.

Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

NASA Astrophysics Data System (ADS)

Hiebl, Johann; Frei, Christoph

2018-04-01

Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.

Evaluation of reanalysis datasets against observational soil temperature data over China

NASA Astrophysics Data System (ADS)

Yang, Kai; Zhang, Jingyong

2018-01-01

Soil temperature is a key land surface variable, and is a potential predictor for seasonal climate anomalies and extremes. Using observational soil temperature data in China for 1981-2005, we evaluate four reanalysis datasets, the land surface reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA-Interim/Land), the second modern-era retrospective analysis for research and applications (MERRA-2), the National Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), and version 2 of the Global Land Data Assimilation System (GLDAS-2.0), with a focus on 40 cm soil layer. The results show that reanalysis data can mainly reproduce the spatial distributions of soil temperature in summer and winter, especially over the east of China, but generally underestimate their magnitudes. Owing to the influence of precipitation on soil temperature, the four datasets perform better in winter than in summer. The ERA-Interim/Land and GLDAS-2.0 produce spatial characteristics of the climatological mean that are similar to observations. The interannual variability of soil temperature is well reproduced by the ERA-Interim/Land dataset in summer and by the CFSR dataset in winter. The linear trend of soil temperature in summer is well rebuilt by reanalysis datasets. We demonstrate that soil heat fluxes in April-June and in winter are highly correlated with the soil temperature in summer and winter, respectively. Different estimations of surface energy balance components can contribute to different behaviors in reanalysis products in terms of estimating soil temperature. In addition, reanalysis datasets can mainly rebuild the northwest-southeast gradient of soil temperature memory over China.

Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse.

PubMed

Soranno, Patricia A; Bissell, Edward G; Cheruvelil, Kendra S; Christel, Samuel T; Collins, Sarah M; Fergus, C Emi; Filstrup, Christopher T; Lapierre, Jean-Francois; Lottig, Noah R; Oliver, Samantha K; Scott, Caren E; Smith, Nicole J; Stopyak, Scott; Yuan, Shuai; Bremigan, Mary Tate; Downing, John A; Gries, Corinna; Henry, Emily N; Skaff, Nick K; Stanley, Emily H; Stow, Craig A; Tan, Pang-Ning; Wagner, Tyler; Webster, Katherine E

2015-01-01

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km(2)). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Building a multi-scaled geospatial temporal ecology database from disparate data sources: Fostering open science through data reuse

USGS Publications Warehouse

Soranno, Patricia A.; Bissell, E.G.; Cheruvelil, Kendra S.; Christel, Samuel T.; Collins, Sarah M.; Fergus, C. Emi; Filstrup, Christopher T.; Lapierre, Jean-Francois; Lotting, Noah R.; Oliver, Samantha K.; Scott, Caren E.; Smith, Nicole J.; Stopyak, Scott; Yuan, Shuai; Bremigan, Mary Tate; Downing, John A.; Gries, Corinna; Henry, Emily N.; Skaff, Nick K.; Stanley, Emily H.; Stow, Craig A.; Tan, Pang-Ning; Wagner, Tyler; Webster, Katherine E.

2015-01-01

Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km2). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.

Small-Area Estimation of Spatial Access to Care and Its Implications for Policy.

PubMed

Gentili, Monica; Isett, Kim; Serban, Nicoleta; Swann, Julie

2015-10-01

Local or small-area estimates to capture emerging trends across large geographic regions are critical in identifying and addressing community-level health interventions. However, they are often unavailable due to lack of analytic capabilities in compiling and integrating extensive datasets and complementing them with the knowledge about variations in state-level health policies. This study introduces a modeling approach for small-area estimation of spatial access to pediatric primary care that is data "rich" and mathematically rigorous, integrating data and health policy in a systematic way. We illustrate the sensitivity of the model to policy decision making across large geographic regions by performing a systematic comparison of the estimates at the census tract and county levels for Georgia and California. Our results show the proposed approach is able to overcome limitations of other existing models by capturing patient and provider preferences and by incorporating possible changes in health policies. The primary finding is systematic underestimation of spatial access, and inaccurate estimates of disparities across population and across geography at the county level with respect to those at the census tract level with implications on where to focus and which type of interventions to consider.

Developing, sharing and using large community datasets to evaluate regional hydrologic change in Northern Brazil

NASA Astrophysics Data System (ADS)

Thompson, S. E.; Levy, M. C.

2016-12-01

Quantifying regional water cycle changes resulting from the physical transformation of the earth's surface is essential for water security. Although hydrology has a rich legacy of "paired basin" experiments that identify water cycle responses to imposed land use or land cover change (i) there is a deficit of such studies across many representative biomes worldwide, including the tropics, and (ii) the paired basins generally do not provide a representative sample of regional river systems in a way that can inform policy. Larger sample, empirical analyses are needed for such policy-relevant understanding - and these analyses must be supported by regional data. Northern Brazil is a global agricultural and biodiversity center, where regional climate and hydrology are projected (through modeling) to have strong sensitivities to land cover change. Dramatic land cover change has and continues to occur in this region. We used a causal statistical anlaysis framework to explore the effects of deforestation and land cover conversion on regional hydrology. Firstly, we used a comparative approach to address the `data selection uncertainty' problem associated with rainfall datasets covering this sparsely monitored region. We compared 9 remotely-sensed (RS) and in-situ (IS) rainfall datasets, demonstrating that rainfall characterization and trends were sensitive to the selected data sources and identifying which of these datasets had the strongest fidelity to independently measured streamflow occurrence. Next, we employed a "differences-in-differences" regression technique to evaluate the effects of land use change on the quantiles of the flow duration curve between populations of basins experiencing different levels of land conversion. Regionally, controlling for climate and other variables, deforestation significantly increased flow in the lowest third of the flow duration curve. Addressing this problem required harmonizing 9 separate spatial datasets (in addition to the 9 rainfall datasets originally considered), and relied extensively on the use of newly developed data acquisition and analysis platforms such as Google Earth Engine and Columbia IRI/LDEO. The datasets developed in this project have been made discoverable through collaboration with CUAHSI.

Logistic regression model for detecting radon prone areas in Ireland.

PubMed

Elío, J; Crowley, Q; Scanlon, R; Hodgson, J; Long, S

2017-12-01

A new high spatial resolution radon risk map of Ireland has been developed, based on a combination of indoor radon measurements (n=31,910) and relevant geological information (i.e. Bedrock Geology, Quaternary Geology, soil permeability and aquifer type). Logistic regression was used to predict the probability of having an indoor radon concentration above the national reference level of 200Bqm -3 in Ireland. The four geological datasets evaluated were found to be statistically significant, and, based on combinations of these four variables, the predicted probabilities ranged from 0.57% to 75.5%. Results show that the Republic of Ireland may be divided in three main radon risk categories: High (HR), Medium (MR) and Low (LR). The probability of having an indoor radon concentration above 200Bqm -3 in each area was found to be 19%, 8% and 3%; respectively. In the Republic of Ireland, the population affected by radon concentrations above 200Bqm -3 is estimated at ca. 460k (about 10% of the total population). Of these, 57% (265k), 35% (160k) and 8% (35k) are in High, Medium and Low Risk Areas, respectively. Our results provide a high spatial resolution utility which permit customised radon-awareness information to be targeted at specific geographic areas. Copyright © 2017 Elsevier B.V. All rights reserved.

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rose, Amy N.; Nagle, Nicholas N.

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internalmore » validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.« less

Quantifying Crop Specific Blue and Green Water Footprints and the Spatial Allocation of Virtual Water in China

NASA Astrophysics Data System (ADS)

Pan, J.; Smith, T.; McLaughlin, D.

2016-12-01

China, which had a population of 1.38 billion in 2013, is expected to peak at about 1.45 billion around 2030, with per capita food demand likely to increase significantly. The population growth and diet change make prospects of future available water and food worrisome for China. Quantitative estimates of crop specific blue and green water footprints provide useful insight about the roles of different water sources and give guidance for agricultural and water resource planning. This study uses reanalysis methods to merge diverse datasets, including information on water fluxes and land use, to estimate crop-specific green and blue water consumption at 0.5 degree spatial resolution. The estimates incorporate, through constraints in the reanalysis procedure, important physical connections between the water and land resources that support agriculture. These connections are important since land use affects evapotranspiration and runoff while water availability and crop area affect crop production and virtual water content. The results show that green water accounts for 86% and blue water accounts for 14% of the total national agricultural water footprint, respectively. The water footprints of cereals (wheat, maize and rice) and soybeans account for 51% of the total agricultural water footprint. Cereals and soybeans together account for 85% of the total blue water footprint.

Validation of spatiodemographic estimates produced through data fusion of small area census records and household microdata

DOE PAGES

Rose, Amy N.; Nagle, Nicholas N.

2016-08-01

Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internalmore » validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations.« less

The MIND PALACE: A Multi-Spectral Imaging and Spectroscopy Database for Planetary Science

NASA Astrophysics Data System (ADS)

Eshelman, E.; Doloboff, I.; Hara, E. K.; Uckert, K.; Sapers, H. M.; Abbey, W.; Beegle, L. W.; Bhartia, R.

2017-12-01

The Multi-Instrument Database (MIND) is the web-based home to a well-characterized set of analytical data collected by a suite of deep-UV fluorescence/Raman instruments built at the Jet Propulsion Laboratory (JPL). Samples derive from a growing body of planetary surface analogs, mineral and microbial standards, meteorites, spacecraft materials, and other astrobiologically relevant materials. In addition to deep-UV spectroscopy, datasets stored in MIND are obtained from a variety of analytical techniques obtained over multiple spatial and spectral scales including electron microscopy, optical microscopy, infrared spectroscopy, X-ray fluorescence, and direct fluorescence imaging. Multivariate statistical analysis techniques, primarily Principal Component Analysis (PCA), are used to guide interpretation of these large multi-analytical spectral datasets. Spatial co-referencing of integrated spectral/visual maps is performed using QGIS (geographic information system software). Georeferencing techniques transform individual instrument data maps into a layered co-registered data cube for analysis across spectral and spatial scales. The body of data in MIND is intended to serve as a permanent, reliable, and expanding database of deep-UV spectroscopy datasets generated by this unique suite of JPL-based instruments on samples of broad planetary science interest.

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

NASA Astrophysics Data System (ADS)

Poyatos, Rafael; Sus, Oliver; Badiella, Llorenç; Mencuccini, Maurizio; Martínez-Vilalta, Jordi

2018-05-01

The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2). We simulated gaps at different missingness levels (10-80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets.

EPA Tribal Areas (4 of 4): Alaska Native Allotments

EPA Pesticide Factsheets

This dataset is a spatial representation of the Public Land Survey System (PLSS) in Alaska, generated from land survey records. The data represents a seamless spatial portrayal of native allotment land parcels, their legal descriptions, corner positioning and markings, and survey measurements. This data is intended for mapping purposes only and is not a substitute or replacement for the legal land survey records or other legal documents.Measurement and attribute data are collected from survey records using data entry screens into a relational database. The database design is based upon the FGDC Cadastral Content Data Standard. Corner positions are derived by geodetic calculations using measurement records. Closure and edgematching are applied to produce a seamless dataset. The resultant features do not preserve the original geometry of survey measurements, but the record measurements are reported as attributes. Additional boundary data are derived by spatial capture, protraction and GIS processing. The spatial features are stored and managed within the relational database, with active links to the represented measurement and attribute data.

SNPs and Haplotypes in Native American Populations

PubMed Central

Kidd, Judith R.; Friedlaender, Françoise; Pakstis, Andrew J.; Furtado, Manohar; Fang, Rixun; Wang, Xudong; Nievergelt, Caroline M.; Kidd, Kenneth K.

2013-01-01

Autosomal DNA polymorphisms can provide new information and understanding of both the origins of and relationships among modern Native American populations. At the same time that autosomal markers can be highly informative, they are also susceptible to ascertainment biases in the selection of the markers to use. Identifying markers that can be used for ancestry inference among Native American populations can be considered separate from identifying markers to further the quest for history. In the current study we are using data on nine Native American populations to compare the results based on a large haplotype-based dataset with relatively small independent sets of SNPs. We are interested in what types of limited datasets an individual laboratory might be able to collect are best for addressing two different questions of interest. First, how well can we differentiate the Native American populations and/or infer ancestry by assigning an individual to her population(s) of origin? Second, how well can we infer the historical/evolutionary relationships among Native American populations and their Eurasian origins. We conclude that only a large comprehensive dataset involving multiple autosomal markers on multiple populations will be able to answer both questions; different small sets of markers are able to answer only one or the other of these questions. Using our largest dataset we see a general increasing distance from Old World populations from North to South in the New World except for an unexplained close relationship between our Maya and Quechua samples. PMID:21913176

A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed.

PubMed

Cooper, K M; Barry, J

2017-09-29

In this study we produce a standardised dataset for benthic macrofauna and sediments through integration of data (33,198 samples) from 777 grab surveys. The resulting dataset is used to identify spatial and temporal patterns in faunal distribution around the UK, and the role of sediment composition and other explanatory variables in determining such patterns. We show how insight into natural variability afforded by the dataset can be used to improve the sustainability of activities which affect sediment composition, by identifying conditions which should remain favourable for faunal recolonisation. Other big data applications and uses of the dataset are discussed.

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

NASA Astrophysics Data System (ADS)

Lussana, Cristian; Saloranta, Tuomo; Skaugen, Thomas; Magnusson, Jan; Tveito, Ole Einar; Andersen, Jess

2018-02-01

The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

Genome-wide SNPs reveal fine-scale differentiation among wingless alpine stonefly populations and introgression between winged and wingless forms.

PubMed

Dussex, Nicolas; Chuah, Aaron; Waters, Jonathan M

2016-01-01

Insect flight loss is a repeated phenomenon in alpine habitats, where wing reduction is thought to enhance local recruitment and increase fecundity. One predicted consequence of flight loss is reduced dispersal ability, which should lead to population genetic differentiation and perhaps ultimately to speciation. Using a dataset of 15,123 SNP loci, we present comparative analyses of fine-scale population structure in codistributed Zelandoperla stonefly species, across three parallel altitudinal transects in New Zealand's Rock and Pillar mountain range. We find that winged populations (altitude 200-500 m; Zelandoperla decorata) show no genetic structuring within or among streams, suggesting substantial dispersal mediated by flight. By contrast, wingless populations (Zelandoperla fenestrata; altitude 200-1100 m) exhibit distinct genetic clusters associated with each stream, and additional evidence of isolation by distance within streams. Our data support the hypothesis that wing-loss can initiate diversification in alpine insect populations over small spatial scales. The often deep phylogenetic placement of lowland Z. fenestrata within their stream-specific clades suggests the possibility of independent alpine colonization events for each stream. Additionally, the detection of winged, interspecific hybrid individuals raises the intriguing possibility that a previously flightless lineage could reacquire flight via introgression. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Assessing sufficiency of thermal riverscapes for resilient ...

EPA Pesticide Factsheets

Resilient salmon populations require river networks that provide water temperature regimes sufficient to support a diversity of salmonid life histories across space and time. Efforts to protect, enhance and restore watershed thermal regimes for salmon may target specific locations and features within stream networks hypothesized to provide disproportionately high-value functional resilience to salmon populations. These include relatively small-scale features such as thermal refuges, and larger-scale features such as entire watersheds or aquifers that support thermal regimes buffered from local climatic conditions. Quantifying the value of both small and large scale thermal features to salmon populations has been challenged by both the difficulty of mapping thermal regimes at sufficient spatial and temporal resolutions, and integrating thermal regimes into population models. We attempt to address these challenges by using newly-available datasets and modeling approaches to link thermal regimes to salmon populations across scales. We will describe an individual-based modeling approach for assessing sufficiency of thermal refuges for migrating salmon and steelhead in large rivers, as well as a population modeling approach for assessing large-scale climate refugia for salmon in the Pacific Northwest. Many rivers and streams in the Pacific Northwest are currently listed as impaired under the Clean Water Act as a result of high summer water temperatures. Adverse effec

DownscaleConcept 2.3 User Manual. Downscaled, Spatially Distributed Soil Moisture Calculator

DTIC Science & Technology

2011-01-01

be first presented with the dataset 28 results to your query. From this page, check the box next to the ASTER GDEM dataset and press the "List...information for verification. No charge will be associated with GDEM data archives. 14. Select "Submit Order Now!" to process your order. 15. Wait for

Basin Assessment Spatial Planning Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

The tool is intended to facilitate hydropower development and water resource planning by improving synthesis and interpretation of disparate spatial datasets that are considered in development actions (e.g., hydrological characteristics, environmentally and culturally sensitive areas, existing or proposed water power resources, climate-informed forecasts). The tool enables this capability by providing a unique framework for assimilating, relating, summarizing, and visualizing disparate spatial data through the use of spatial aggregation techniques, relational geodatabase platforms, and an interactive web-based Geographic Information Systems (GIS). Data are aggregated and related based on shared intersections with a common spatial unit; in this case, industry-standard hydrologic drainagemore » areas for the U.S. (National Hydrography Dataset) are used as the spatial unit to associate planning data. This process is performed using all available scalar delineations of drainage areas (i.e., region, sub-region, basin, sub-basin, watershed, sub-watershed, catchment) to create spatially hierarchical relationships among planning data and drainages. These entity-relationships are stored in a relational geodatabase that provides back-end structure to the web GIS and its widgets. The full technology stack was built using all open-source software in modern programming languages. Interactive widgets that function within the viewport are also compatible with all modern browsers.« less

From conservation genetics to conservation genomics: a genome-wide assessment of blue whales (Balaenoptera musculus) in Australian feeding aggregations

PubMed Central

Sandoval-Castillo, Jonathan; Jenner, K. Curt S.; Gill, Peter C.; Jenner, Micheline-Nicole M.; Morrice, Margaret G.

2018-01-01

Genetic datasets of tens of markers have been superseded through next-generation sequencing technology with genome-wide datasets of thousands of markers. Genomic datasets improve our power to detect low population structure and identify adaptive divergence. The increased population-level knowledge can inform the conservation management of endangered species, such as the blue whale (Balaenoptera musculus). In Australia, there are two known feeding aggregations of the pygmy blue whale (B. m. brevicauda) which have shown no evidence of genetic structure based on a small dataset of 10 microsatellites and mtDNA. Here, we develop and implement a high-resolution dataset of 8294 genome-wide filtered single nucleotide polymorphisms, the first of its kind for blue whales. We use these data to assess whether the Australian feeding aggregations constitute one population and to test for the first time whether there is adaptive divergence between the feeding aggregations. We found no evidence of neutral population structure and negligible evidence of adaptive divergence. We propose that individuals likely travel widely between feeding areas and to breeding areas, which would require them to be adapted to a wide range of environmental conditions. This has important implications for their conservation as this blue whale population is likely vulnerable to a range of anthropogenic threats both off Australia and elsewhere. PMID:29410806

The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

NASA Astrophysics Data System (ADS)

Koch, J.; Jensen, K. H.; Stisen, S.

2017-12-01

Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion.

PubMed

Yang, Y X; Teo, S-K; Van Reeth, E; Tan, C H; Tham, I W K; Poh, C L

2015-08-01

Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors' proposed approach. A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors' proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.

GIEMS-D3: A new long-term, dynamical, high-spatial resolution inundation extent dataset at global scale

NASA Astrophysics Data System (ADS)

Aires, Filipe; Miolane, Léo; Prigent, Catherine; Pham Duc, Binh; Papa, Fabrice; Fluet-Chouinard, Etienne; Lehner, Bernhard

2017-04-01

The Global Inundation Extent from Multi-Satellites (GIEMS) provides multi-year monthly variations of the global surface water extent at 25kmx25km resolution. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. A new procedure is introduced to downscale the GIEMS low spatial resolution inundations to a 3 arc second (90 m) dataset. The methodology is based on topography and hydrography information from the HydroSHEDS database. A new floodability index is adopted and an innovative smoothing procedure is developed to ensure the smooth transition, in the high-resolution maps, between the low-resolution boxes from GIEMS. Topography information is relevant for natural hydrology environments controlled by elevation, but is more limited in human-modified basins. However, the proposed downscaling approach is compatible with forthcoming fusion with other more pertinent satellite information in these difficult regions. The resulting GIEMS-D3 database is the only high spatial resolution inundation database available globally at the monthly time scale over the 1993-2007 period. GIEMS-D3 is assessed by analyzing its spatial and temporal variability, and evaluated by comparisons to other independent satellite observations from visible (Google Earth and Landsat), infrared (MODIS) and active microwave (SAR).

Spatial distribution, sampling precision and survey design optimisation with non-normal variables: The case of anchovy (Engraulis encrasicolus) recruitment in Spanish Mediterranean waters

NASA Astrophysics Data System (ADS)

Tugores, M. Pilar; Iglesias, Magdalena; Oñate, Dolores; Miquel, Joan

2016-02-01

In the Mediterranean Sea, the European anchovy (Engraulis encrasicolus) displays a key role in ecological and economical terms. Ensuring stock sustainability requires the provision of crucial information, such as species spatial distribution or unbiased abundance and precision estimates, so that management strategies can be defined (e.g. fishing quotas, temporal closure areas or marine protected areas MPA). Furthermore, the estimation of the precision of global abundance at different sampling intensities can be used for survey design optimisation. Geostatistics provide a priori unbiased estimations of the spatial structure, global abundance and precision for autocorrelated data. However, their application to non-Gaussian data introduces difficulties in the analysis in conjunction with low robustness or unbiasedness. The present study applied intrinsic geostatistics in two dimensions in order to (i) analyse the spatial distribution of anchovy in Spanish Western Mediterranean waters during the species' recruitment season, (ii) produce distribution maps, (iii) estimate global abundance and its precision, (iv) analyse the effect of changing the sampling intensity on the precision of global abundance estimates and, (v) evaluate the effects of several methodological options on the robustness of all the analysed parameters. The results suggested that while the spatial structure was usually non-robust to the tested methodological options when working with the original dataset, it became more robust for the transformed datasets (especially for the log-backtransformed dataset). The global abundance was always highly robust and the global precision was highly or moderately robust to most of the methodological options, except for data transformation.

Stability of Spatial Distributions of Stink Bugs, Boll Injury, and NDVI in Cotton.

PubMed

Reay-Jones, Francis P F; Greene, Jeremy K; Bauer, Philip J

2016-10-01

A 3-yr study was conducted to determine the degree of aggregation of stink bugs and boll injury in cotton, Gossypium hirsutum L., and their spatial association with a multispectral vegetation index (normalized difference vegetation index [NDVI]). Using the spatial analysis by distance indices analyses, stink bugs were less frequently aggregated (17% for adults and 4% for nymphs) than boll injury (36%). NDVI values were also significantly aggregated within fields in 19 of 48 analyses (40%), with the majority of significant indices occurring in July and August. Paired NDVI datasets from different sampling dates were frequently associated (86.5% for weekly intervals among datasets). Spatial distributions of both stink bugs and boll injury were less stable than for NDVI, with positive associations varying from 12.5 to 25% for adult stink bugs for weekly intervals, depending on species. Spatial distributions of boll injury from stink bug feeding were more stable than stink bugs, with 46% positive associations among paired datasets with weekly intervals. NDVI values were positively associated with boll injury from stink bug feeding in 11 out of 22 analyses, with no significant negative associations. This indicates that NDVI has potential as a component of site-specific management. Future work should continue to examine the value of remote sensing for insect management in cotton, with an aim to develop tools such as risk assessment maps that will help growers to reduce insecticide inputs. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

GeoPAT: A toolbox for pattern-based information retrieval from large geospatial databases

NASA Astrophysics Data System (ADS)

Jasiewicz, Jarosław; Netzel, Paweł; Stepinski, Tomasz

2015-07-01

Geospatial Pattern Analysis Toolbox (GeoPAT) is a collection of GRASS GIS modules for carrying out pattern-based geospatial analysis of images and other spatial datasets. The need for pattern-based analysis arises when images/rasters contain rich spatial information either because of their very high resolution or their very large spatial extent. Elementary units of pattern-based analysis are scenes - patches of surface consisting of a complex arrangement of individual pixels (patterns). GeoPAT modules implement popular GIS algorithms, such as query, overlay, and segmentation, to operate on the grid of scenes. To achieve these capabilities GeoPAT includes a library of scene signatures - compact numerical descriptors of patterns, and a library of distance functions - providing numerical means of assessing dissimilarity between scenes. Ancillary GeoPAT modules use these functions to construct a grid of scenes or to assign signatures to individual scenes having regular or irregular geometries. Thus GeoPAT combines knowledge retrieval from patterns with mapping tasks within a single integrated GIS environment. GeoPAT is designed to identify and analyze complex, highly generalized classes in spatial datasets. Examples include distinguishing between different styles of urban settlements using VHR images, delineating different landscape types in land cover maps, and mapping physiographic units from DEM. The concept of pattern-based spatial analysis is explained and the roles of all modules and functions are described. A case study example pertaining to delineation of landscape types in a subregion of NLCD is given. Performance evaluation is included to highlight GeoPAT's applicability to very large datasets. The GeoPAT toolbox is available for download from

Metagenomics Reveals Pervasive Bacterial Populations and Reduced Community Diversity across the Alaska Tundra Ecosystem

DOE PAGES

Johnston, Eric R.; Rodriguez-R, Luis M.; Luo, Chengwei; ...

2016-04-25

How soil microbial communities contrast with respect to taxonomic and functional composition within and between ecosystems remains an unresolved question that is central to predicting how global anthropogenic change will affect soil functioning and services. In particular, it remains unclear how small-scale observations of soil communities based on the typical volume sampled (1-2 g) are generalizable to ecosystem-scale responses and processes. This is especially relevant for remote, northern latitude soils, which are challenging to sample and are also thought to be more vulnerable to climate change compared to temperate soils. Here, we employed well-replicated shotgun metagenome and 16S rRNA genemore » amplicon sequencing to characterize community composition and metabolic potential in Alaskan tundra soils, combining our own datasets with those publically available from distant tundra and temperate grassland and agriculture habitats. We found that the abundance of many taxa and metabolic functions differed substantially between tundra soil metagenomes relative to those from temperate soils, and that a high degree of OTU-sharing exists between tundra locations. Tundra soils were an order of magnitude less complex than their temperate counterparts, allowing for near-complete coverage of microbial community richness (~92% breadth) by sequencing, and the recovery of 27 high-quality, almost complete ( > 80% completeness) population bins. These population bins, collectively, made up to ~10% of the metagenomic datasets, and represented diverse taxonomic groups and metabolic lifestyles tuned toward sulfur cycling, hydrogen metabolism, methanotrophy, and organic matter oxidation. Several population bins, including members of Acidobacteria, Actinobacteria, and Proteobacteria, were also present in geographically distant (~100-530 km apart) tundra habitats (full genome representation and up to 99.6% genome-derived average nucleotide identity). Collectively, our results revealed that Alaska tundra microbial communities are less diverse and more homogenous across spatial scales than previously anticipated, and provided DNA sequences of abundant populations and genes that would be relevant for future studies of the effects of environmental change on tundra ecosystems.« less

Metagenomics Reveals Pervasive Bacterial Populations and Reduced Community Diversity across the Alaska Tundra Ecosystem.

PubMed

Johnston, Eric R; Rodriguez-R, Luis M; Luo, Chengwei; Yuan, Mengting M; Wu, Liyou; He, Zhili; Schuur, Edward A G; Luo, Yiqi; Tiedje, James M; Zhou, Jizhong; Konstantinidis, Konstantinos T

2016-01-01

How soil microbial communities contrast with respect to taxonomic and functional composition within and between ecosystems remains an unresolved question that is central to predicting how global anthropogenic change will affect soil functioning and services. In particular, it remains unclear how small-scale observations of soil communities based on the typical volume sampled (1-2 g) are generalizable to ecosystem-scale responses and processes. This is especially relevant for remote, northern latitude soils, which are challenging to sample and are also thought to be more vulnerable to climate change compared to temperate soils. Here, we employed well-replicated shotgun metagenome and 16S rRNA gene amplicon sequencing to characterize community composition and metabolic potential in Alaskan tundra soils, combining our own datasets with those publically available from distant tundra and temperate grassland and agriculture habitats. We found that the abundance of many taxa and metabolic functions differed substantially between tundra soil metagenomes relative to those from temperate soils, and that a high degree of OTU-sharing exists between tundra locations. Tundra soils were an order of magnitude less complex than their temperate counterparts, allowing for near-complete coverage of microbial community richness (~92% breadth) by sequencing, and the recovery of 27 high-quality, almost complete (>80% completeness) population bins. These population bins, collectively, made up to ~10% of the metagenomic datasets, and represented diverse taxonomic groups and metabolic lifestyles tuned toward sulfur cycling, hydrogen metabolism, methanotrophy, and organic matter oxidation. Several population bins, including members of Acidobacteria, Actinobacteria, and Proteobacteria, were also present in geographically distant (~100-530 km apart) tundra habitats (full genome representation and up to 99.6% genome-derived average nucleotide identity). Collectively, our results revealed that Alaska tundra microbial communities are less diverse and more homogenous across spatial scales than previously anticipated, and provided DNA sequences of abundant populations and genes that would be relevant for future studies of the effects of environmental change on tundra ecosystems.

Metagenomics Reveals Pervasive Bacterial Populations and Reduced Community Diversity across the Alaska Tundra Ecosystem

PubMed Central

Johnston, Eric R.; Rodriguez-R, Luis M.; Luo, Chengwei; Yuan, Mengting M.; Wu, Liyou; He, Zhili; Schuur, Edward A. G.; Luo, Yiqi; Tiedje, James M.; Zhou, Jizhong; Konstantinidis, Konstantinos T.

2016-01-01

How soil microbial communities contrast with respect to taxonomic and functional composition within and between ecosystems remains an unresolved question that is central to predicting how global anthropogenic change will affect soil functioning and services. In particular, it remains unclear how small-scale observations of soil communities based on the typical volume sampled (1–2 g) are generalizable to ecosystem-scale responses and processes. This is especially relevant for remote, northern latitude soils, which are challenging to sample and are also thought to be more vulnerable to climate change compared to temperate soils. Here, we employed well-replicated shotgun metagenome and 16S rRNA gene amplicon sequencing to characterize community composition and metabolic potential in Alaskan tundra soils, combining our own datasets with those publically available from distant tundra and temperate grassland and agriculture habitats. We found that the abundance of many taxa and metabolic functions differed substantially between tundra soil metagenomes relative to those from temperate soils, and that a high degree of OTU-sharing exists between tundra locations. Tundra soils were an order of magnitude less complex than their temperate counterparts, allowing for near-complete coverage of microbial community richness (~92% breadth) by sequencing, and the recovery of 27 high-quality, almost complete (>80% completeness) population bins. These population bins, collectively, made up to ~10% of the metagenomic datasets, and represented diverse taxonomic groups and metabolic lifestyles tuned toward sulfur cycling, hydrogen metabolism, methanotrophy, and organic matter oxidation. Several population bins, including members of Acidobacteria, Actinobacteria, and Proteobacteria, were also present in geographically distant (~100–530 km apart) tundra habitats (full genome representation and up to 99.6% genome-derived average nucleotide identity). Collectively, our results revealed that Alaska tundra microbial communities are less diverse and more homogenous across spatial scales than previously anticipated, and provided DNA sequences of abundant populations and genes that would be relevant for future studies of the effects of environmental change on tundra ecosystems. PMID:27199914

Continental-Scale Mapping of Adelie Penguin Colonies from Landsat Imagery

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.; Southwell, Colin; Emmerson, Louise

2013-01-01

Breeding distribution of the Adlie penguin, Pygoscelis adeliae, was surveyed with Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data in an area covering approximately 330 of longitude along the coastline of Antarctica.An algorithm was designed to minimize radiometric noise and to retrieve Adlie penguin colony location and spatial extent from the ETM+data. In all, 9143 individual pixels were classified as belonging to an Adlie penguin colony class out of the entire dataset of 195 ETM+ scenes, where the dimension of each pixel is 30 m by 30 m,and each scene is approximately 180 km by 180 km. Pixel clustering identified a total of 187 individual Adlie penguin colonies, ranging in size from a single pixel (900 sq m) to a maximum of 875 pixels (0.788 sq km). Colony retrievals have a very low error of commission, on the order of 1% or less, and the error of omission was estimated to be 3% to 4% by population based on comparisons with direct observations from surveys across east Antarctica. Thus, the Landsat retrievals successfully located Adlie penguin colonies that accounted for 96 to 97% of the regional population used as ground truth. Geographic coordinates and the spatial extent of each colony retrieved from the Landsat data are available publically. Regional analysis found several areas where the Landsat retrievals suggest populations that are significantly larger than published estimates. Six Adlie penguin colonies were found that are believed to be previously unreported in the literature.

Spatial and temporal predictions of agricultural land prices using DSM techniques.

NASA Astrophysics Data System (ADS)

Carré, F.; Grandgirard, D.; Diafas, I.; Reuter, H. I.; Julien, V.; Lemercier, B.

2009-04-01

Agricultural land prices highly impacts land accessibility to farmers and by consequence the evolution of agricultural landscapes (crop changes, land conversion to urban infrastructures…) which can turn to irreversible soil degradation. The economic value of agricultural land has been studied spatially, in every one of the 374 French Agricultural Counties, and temporally- from 1995 to 2007, by using data of the SAFER Institute. To this aim, agricultural land price was considered as a digital soil property. The spatial and temporal predictions were done using Digital Soil Mapping techniques combined with tools mainly used for studying temporal financial behaviors. For making both predictions, a first classification of the Agricultural Counties was done for the 1995-2006 periods (2007 was excluded and served as the date of prediction) using a fuzzy k-means clustering. The Agricultural Counties were then aggregated according to land price at the different times. The clustering allows for characterizing the counties by their memberships to each class centroid. The memberships were used for the spatial prediction, whereas the centroids were used for the temporal prediction. For the spatial prediction, from the 374 Agricultural counties, three fourths were used for modeling and one fourth for validating. Random sampling was done by class to ensure that all classes are represented by at least one county in the modeling and validation datasets. The prediction was done for each class by testing the relationships between the memberships and the following factors: (i) soil variable (organic matter from the French BDAT database), (ii) soil covariates (land use classes from CORINE LANDCOVER, bioclimatic zones from the WorldClim Database, landform attributes and landform classes from the SRTM, major roads and hydrographic densities from EUROSTAT, average field sizes estimated by automatic classification of remote sensed images) and (iii) socio-economic factors (population density, gross domestic product and its combination with the population density obtained from EUROSTAT). Linear (Generalized Linear Models) and non-linear models (neural network) were used for building the relationships. For the validation, the relationships were applied to the validation datasets. The RMSE and the coefficient of determination (from a linear regression) between predicted and actual memberships, and the contingency table between the predicted and actual allocation classes were used as validation criteria. The temporal prediction was done on the year 2007 from the centroid land prices characterizing the 1995-2006 period. For each class, the land prices of the time-series 1995-2006 were modeled using an Auto-Regressive Moving Average approach. For the validation, the models were applied to the year 2007. The RMSE between predicted and actual prices is used as the validation criteria. We then discussed the methods and the results of the spatial and temporal validation. Based on this methodology, an extrapolation will be tested on another European country with land price market similar to France (to be determined).

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

PubMed Central

Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

2012-01-01

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns. PMID:23077494

Economic development and coastal ecosystem change in China.

PubMed

He, Qiang; Bertness, Mark D; Bruno, John F; Li, Bo; Chen, Guoqian; Coverdale, Tyler C; Altieri, Andrew H; Bai, Junhong; Sun, Tao; Pennings, Steven C; Liu, Jianguo; Ehrlich, Paul R; Cui, Baoshan

2014-08-08

Despite their value, coastal ecosystems are globally threatened by anthropogenic impacts, yet how these impacts are driven by economic development is not well understood. We compiled a multifaceted dataset to quantify coastal trends and examine the role of economic growth in China's coastal degradation since the 1950s. Although China's coastal population growth did not change following the 1978 economic reforms, its coastal economy increased by orders of magnitude. All 15 coastal human impacts examined increased over time, especially after the reforms. Econometric analysis revealed positive relationships between most impacts and GDP across temporal and spatial scales, often lacking dropping thresholds. These relationships generally held when influences of population growth were addressed by analyzing per capita impacts, and when population density was included as explanatory variables. Historical trends in physical and biotic indicators showed that China's coastal ecosystems changed little or slowly between the 1950s and 1978, but have degraded at accelerated rates since 1978. Thus economic growth has been the cause of accelerating human damage to China's coastal ecosystems. China's GDP per capita remains very low. Without strict conservation efforts, continuing economic growth will further degrade China's coastal ecosystems.

A global wind resource atlas including high-resolution terrain effects

NASA Astrophysics Data System (ADS)

Hahmann, Andrea; Badger, Jake; Olsen, Bjarke; Davis, Neil; Larsen, Xiaoli; Badger, Merete

2015-04-01

Currently no accurate global wind resource dataset is available to fill the needs of policy makers and strategic energy planners. Evaluating wind resources directly from coarse resolution reanalysis datasets underestimate the true wind energy resource, as the small-scale spatial variability of winds is missing. This missing variability can account for a large part of the local wind resource. Crucially, it is the windiest sites that suffer the largest wind resource errors: in simple terrain the windiest sites may be underestimated by 25%, in complex terrain the underestimate can be as large as 100%. The small-scale spatial variability of winds can be modelled using novel statistical methods and by application of established microscale models within WAsP developed at DTU Wind Energy. We present the framework for a single global methodology, which is relative fast and economical to complete. The method employs reanalysis datasets, which are downscaled to high-resolution wind resource datasets via a so-called generalization step, and microscale modelling using WAsP. This method will create the first global wind atlas (GWA) that covers all land areas (except Antarctica) and 30 km coastal zone over water. Verification of the GWA estimates will be done at carefully selected test regions, against verified estimates from mesoscale modelling and satellite synthetic aperture radar (SAR). This verification exercise will also help in the estimation of the uncertainty of the new wind climate dataset. Uncertainty will be assessed as a function of spatial aggregation. It is expected that the uncertainty at verification sites will be larger than that of dedicated assessments, but the uncertainty will be reduced at levels of aggregation appropriate for energy planning, and importantly much improved relative to what is used today. In this presentation we discuss the methodology used, which includes the generalization of wind climatologies, and the differences in local and spatially aggregated wind resources that result from using different reanalyses in the various verification regions. A prototype web interface for the public access to the data will also be showcased.

Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

NASA Astrophysics Data System (ADS)

Gibson, Justin; Franz, Trenton E.

2018-06-01

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Variations in population exposure and sensitivity to lahar hazards from Mount Rainier, Washington

NASA Astrophysics Data System (ADS)

Wood, Nathan; Soulard, Christopher

2009-12-01

Although much has been done to understand, quantify, and delineate volcanic hazards, there are fewer efforts to assess societal vulnerability to these hazards, particularly demographic differences in exposed populations or spatial variations in exposure to regional hazards. To better understand population diversity in volcanic hazard zones, we assess the number and types of people in a single type of hazard zone (lahars) for 27 communities downstream of Mount Rainier, Washington (USA). Using various socioeconomic and hazard datasets, we estimate that there are more than 78 000 residents, 59 000 employees, several dependent-population facilities (e.g., child-day-care centers, nursing homes) and numerous public venues (e.g., churches, hotels, museums) in a Mount Rainier lahar-hazard zone. We find that communities vary in the primary category of individuals in lahar-prone areas—exposed populations are dominated by residents in some communities (e.g., Auburn), employees in others (e.g., Tacoma), and tourists likely outnumber both of these groups in yet other areas (e.g., unincorporated Lewis County). Population exposure to potential lahar inundation varies considerably—some communities (e.g., Auburn) have large numbers of people but low percentages of them in hazard zones, whereas others (e.g., Orting) have fewer people but they comprise the majority of a community. A composite lahar-exposure index is developed to help emergency managers understand spatial variations in community exposure to lahars and results suggest that Puyallup has the highest combination of high numbers and percentages of people and assets in lahar-prone areas. Risk education and preparedness needs will vary based on who is threatened by future lahars, such as residents, employees, tourists at a public venue, or special-needs populations at a dependent-care facility. Emergency managers must first understand the people whom they are trying to prepare before they can expect these people to take protective measures after recognizing natural cues or receiving an official lahar warning.

Variations in population exposure and sensitivity to lahar hazards from Mount Rainier, Washington

USGS Publications Warehouse

Wood, N.; Soulard, C.

2009-01-01

Although much has been done to understand, quantify, and delineate volcanic hazards, there are fewer efforts to assess societal vulnerability to these hazards, particularly demographic differences in exposed populations or spatial variations in exposure to regional hazards. To better understand population diversity in volcanic hazard zones, we assess the number and types of people in a single type of hazard zone (lahars) for 27 communities downstream of Mount Rainier, Washington (USA). Using various socioeconomic and hazard datasets, we estimate that there are more than 78 000 residents, 59 000 employees, several dependent-population facilities (e.g., child-day-care centers, nursing homes) and numerous public venues (e.g., churches, hotels, museums) in a Mount Rainier lahar-hazard zone. We find that communities vary in the primary category of individuals in lahar-prone areas-exposed populations are dominated by residents in some communities (e.g., Auburn), employees in others (e.g., Tacoma), and tourists likely outnumber both of these groups in yet other areas (e.g., unincorporated Lewis County). Population exposure to potential lahar inundation varies considerably-some communities (e.g., Auburn) have large numbers of people but low percentages of them in hazard zones, whereas others (e.g., Orting) have fewer people but they comprise the majority of a community. A composite lahar-exposure index is developed to help emergency managers understand spatial variations in community exposure to lahars and results suggest that Puyallup has the highest combination of high numbers and percentages of people and assets in lahar-prone areas. Risk education and preparedness needs will vary based on who is threatened by future lahars, such as residents, employees, tourists at a public venue, or special-needs populations at a dependent-care facility. Emergency managers must first understand the people whom they are trying to prepare before they can expect these people to take protective measures after recognizing natural cues or receiving an official lahar warning.

How accurately are climatological characteristics and surface water and energy balances represented for the Colombian Caribbean Catchment Basin?

NASA Astrophysics Data System (ADS)

Hoyos, Isabel; Baquero-Bernal, Astrid; Hagemann, Stefan

2013-09-01

In Colombia, the access to climate related observational data is restricted and their quantity is limited. But information about the current climate is fundamental for studies on present and future climate changes and their impacts. In this respect, this information is especially important over the Colombian Caribbean Catchment Basin (CCCB) that comprises over 80 % of the population of Colombia and produces about 85 % of its GDP. Consequently, an ensemble of several datasets has been evaluated and compared with respect to their capability to represent the climate over the CCCB. The comparison includes observations, reconstructed data (CPC, Delaware), reanalyses (ERA-40, NCEP/NCAR), and simulated data produced with the regional climate model REMO. The capabilities to represent the average annual state, the seasonal cycle, and the interannual variability are investigated. The analyses focus on surface air temperature and precipitation as well as on surface water and energy balances. On one hand the CCCB characteristics poses some difficulties to the datasets as the CCCB includes a mountainous region with three mountain ranges, where the dynamical core of models and model parameterizations can fail. On the other hand, it has the most dense network of stations, with the longest records, in the country. The results can be summarised as follows: all of the datasets demonstrate a cold bias in the average temperature of CCCB. However, the variability of the average temperature of CCCB is most poorly represented by the NCEP/NCAR dataset. The average precipitation in CCCB is overestimated by all datasets. For the ERA-40, NCEP/NCAR, and REMO datasets, the amplitude of the annual cycle is extremely high. The variability of the average precipitation in CCCB is better represented by the reconstructed data of CPC and Delaware, as well as by NCEP/NCAR. Regarding the capability to represent the spatial behaviour of CCCB, temperature is better represented by Delaware and REMO, while precipitation is better represented by Delaware. Among the three datasets that permit an analysis of surface water and energy balances (REMO, ERA-40, and NCEP/NCAR), REMO best demonstrates the closure property of the surface water balance within the basin, while NCEP/NCAR does not demonstrate this property well. The three datasets represent the energy balance fairly well, although some inconsistencies were found in the individual balance components for NCEP/NCAR.

The Wind Integration National Dataset (WIND) toolkit (Presentation)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caroline Draxl: NREL

2014-01-01

Regional wind integration studies require detailed wind power output data at many locations to perform simulations of how the power system will operate under high penetration scenarios. The wind datasets that serve as inputs into the study must realistically reflect the ramping characteristics, spatial and temporal correlations, and capacity factors of the simulated wind plants, as well as being time synchronized with available load profiles.As described in this presentation, the WIND Toolkit fulfills these requirements by providing a state-of-the-art national (US) wind resource, power production and forecast dataset.

DOTAGWA: A CASE STUDY IN WEB-BASED ARCHITECTURES FOR CONNECTING SURFACE WATER MODELS TO SPATIALLY ENABLED WEB APPLICATIONS

EPA Science Inventory

The Automated Geospatial Watershed Assessment (AGWA) tool is a desktop application that uses widely available standardized spatial datasets to derive inputs for multi-scale hydrologic models (Miller et al., 2007). The required data sets include topography (DEM data), soils, clima...

Variability of Upper-Tropospheric Precipitable from Satellite and Model Reanalysis Datasets

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Iwai, Hisaki

1999-01-01

Numerous datasets have been used to quantify water vapor and its variability in the upper-troposphere from satellite and model reanalysis data. These investigations have shown some usefulness in monitoring seasonal and inter-annual variations in moisture either globally, with polar orbiting satellite data or global model output analysis, or regionally, with the higher spatial and temporal resolution geostationary measurements. The datasets are not without limitations, however, due to coverage or limited temporal sampling, and may also contain bias in their representation of moisture processes. The research presented in this conference paper inter-compares the NVAP, NCEP/NCAR and DAO reanalysis models, and GOES satellite measurements of upper-tropospheric,precipitable water for the period from 1988-1994. This period captures several dramatic swings in climate events associated with ENSO events. The data are evaluated for temporal and spatial continuity, inter-compared to assess reliability and potential bias, and analyzed in light of expected trends due to changes in precipitation and synoptic-scale weather features. This work is the follow-on to previous research which evaluated total precipitable water over the same period. The relationship between total and upper-level precipitable water in the datasets will be discussed as well.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa

PubMed Central

Petegrosso, Raphael; Tolar, Jakub

2018-01-01

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. PMID:29630593

Spatial Query for Planetary Data

NASA Technical Reports Server (NTRS)

Shams, Khawaja S.; Crockett, Thomas M.; Powell, Mark W.; Joswig, Joseph C.; Fox, Jason M.

2011-01-01

Science investigators need to quickly and effectively assess past observations of specific locations on a planetary surface. This innovation involves a location-based search technology that was adapted and applied to planetary science data to support a spatial query capability for mission operations software. High-performance location-based searching requires the use of spatial data structures for database organization. Spatial data structures are designed to organize datasets based on their coordinates in a way that is optimized for location-based retrieval. The particular spatial data structure that was adapted for planetary data search is the R+ tree.

The allometric exponent for scaling clearance varies with age: a study on seven propofol datasets ranging from preterm neonates to adults.

PubMed

Wang, Chenguang; Allegaert, Karel; Peeters, Mariska Y M; Tibboel, Dick; Danhof, Meindert; Knibbe, Catherijne A J

2014-01-01

For scaling clearance between adults and children, allometric scaling with a fixed exponent of 0.75 is often applied. In this analysis, we performed a systematic study on the allometric exponent for scaling propofol clearance between two subpopulations selected from neonates, infants, toddlers, children, adolescents and adults. Seven propofol studies were included in the analysis (neonates, infants, toddlers, children, adolescents, adults1 and adults2). In a systematic manner, two out of the six study populations were selected resulting in 15 combined datasets. In addition, the data of the seven studies were regrouped into five age groups (FDA Guidance 1998), from which four combined datasets were prepared consisting of one paediatric age group and the adult group. In each of these 19 combined datasets, the allometric scaling exponent for clearance was estimated using population pharmacokinetic modelling (nonmem 7.2). The allometric exponent for propofol clearance varied between 1.11 and 2.01 in cases where the neonate dataset was included. When two paediatric datasets were analyzed, the exponent varied between 0.2 and 2.01, while it varied between 0.56 and 0.81 when the adult population and a paediatric dataset except for neonates were selected. Scaling from adults to adolescents, children, infants and neonates resulted in exponents of 0.74, 0.70, 0.60 and 1.11 respectively. For scaling clearance, ¾ allometric scaling may be of value for scaling between adults and adolescents or children, while it can neither be used for neonates nor for two paediatric populations. For scaling to neonates an exponent between 1 and 2 was identified. © 2013 The British Pharmacological Society.

Modelling land cover change in the Ganga basin

NASA Astrophysics Data System (ADS)

Moulds, S.; Tsarouchi, G.; Mijic, A.; Buytaert, W.

2013-12-01

Over recent decades the green revolution in India has driven substantial environmental change. Modelling experiments have identified northern India as a 'hot spot' of land-atmosphere coupling strength during the boreal summer. However, there is a wide range of sensitivity of atmospheric variables to soil moisture between individual climate models. The lack of a comprehensive land cover change dataset to force climate models has been identified as a major contributor to model uncertainty. In this work a time series dataset of land cover change between 1970 and 2010 is constructed for northern India to improve the quantification of regional hydrometeorological feedbacks. The MODIS instrument on board the Aqua and Terra satellites provides near-continuous remotely sensed datasets from 2000 to the present day. However, the quality of satellite products before 2000 is poor. To complete the dataset MODIS images are extrapolated back in time using the Conversion of Land Use and its Effects at small regional extent (CLUE-s) modelling framework. Non-spatial estimates of land cover area from national agriculture and forest statistics, available on a state-wise, annual basis, are used as a direct model input. Land cover change is allocated spatially as a function of biophysical and socioeconomic drivers identified using logistic regression. This dataset will provide an essential input to a high resolution, physically based land surface model to generate the lower boundary condition to assess the impact of land cover change on regional climate.

Analysis of the precipitation and streamflow extremes in Northern Italy using high resolution reanalysis dataset Express-Hydro

NASA Astrophysics Data System (ADS)

Silvestro, Francesco; Parodi, Antonio; Campo, Lorenzo

2017-04-01

The characterization of the hydrometeorological extremes, both in terms of rainfall and streamflow, in a given region plays a key role in the environmental monitoring provided by the flood alert services. In last years meteorological simulations (both near real-time and historical reanalysis) were available at increasing spatial and temporal resolutions, making possible long-period hydrological reanalysis in which the meteo dataset is used as input in distributed hydrological models. In this work, a very high resolution meteorological reanalysis dataset, namely Express-Hydro (CIMA, ISAC-CNR, GAUSS Special Project PR45DE), was employed as input in the hydrological model Continuum in order to produce long time series of streamflows in the Liguria territory, located in the Northern part of Italy. The original dataset covers the whole Europe territory in the 1979-2008 period, at 4 km of spatial resolution and 3 hours of time resolution. Analyses in terms of comparison between the rainfall estimated by the dataset and the observations (available from the local raingauges network) were carried out, and a bias correction was also performed in order to better match the observed climatology. An extreme analysis was eventually carried on the streamflows time series obtained by the simulations, by comparing them with the results of the same hydrological model fed with the observed time series of rainfall. The results of the analysis are shown and discussed.

Harmonization of Multiple Forest Disturbance Data to Create a 1986-2011 Database for the Conterminous United States

NASA Astrophysics Data System (ADS)

Soulard, C. E.; Acevedo, W.; Yang, Z.; Cohen, W. B.; Stehman, S. V.; Taylor, J. L.

2015-12-01

A wide range of spatial forest disturbance data exist for the conterminous United States, yet inconsistencies between map products arise because of differing programmatic objectives and methodologies. Researchers on the Land Change Research Project (LCRP) are working to assess spatial agreement, characterize uncertainties, and resolve discrepancies between these national level datasets, in regard to forest disturbance. Disturbance maps from the Global Forest Change (GFC), Landfire Vegetation Disturbance (LVD), National Land Cover Dataset (NLCD), Vegetation Change Tracker (VCT), Web-enabled Landsat Data (WELD), and Monitoring Trends in Burn Severity (MTBS) were harmonized using a pixel-based data fusion process. The harmonization process reconciled forest harvesting, forest fire, and remaining forest disturbance across four intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011) by relying on convergence of evidence across all datasets available for each interval. Pixels with high agreement across datasets were retained, while moderate-to-low agreement pixels were visually assessed and either manually edited using reference imagery or discarded from the final disturbance map(s). National results show that annual rates of forest harvest and overall fire have increased over the past 25 years. Overall, this study shows that leveraging the best elements of readily-available data improves forest loss monitoring relative to using a single dataset to monitor forest change, particularly by reducing commission errors.

Long-term effects of wildfire on greater sage-grouse - integrating population and ecosystem concepts for management in the Great Basin

USGS Publications Warehouse

Coates, Peter S.; Ricca, Mark A.; Prochazka, Brian G.; Doherty, Kevin E.; Brooks, Matthew L.; Casazza, Michael L.

2015-09-10

Greater sage-grouse (Centrocercus urophasianus; hereinafter, sage-grouse) are a sagebrush obligate species that has declined concomitantly with the loss and fragmentation of sagebrush ecosystems across most of its geographical range. The species currently is listed as a candidate for federal protection under the Endangered Species Act (ESA). Increasing wildfire frequency and changing climate frequently are identified as two environmental drivers that contribute to the decline of sage-grouse populations, yet few studies have rigorously quantified their effects on sage-grouse populations across broad spatial scales and long time periods. To help inform a threat assessment within the Great Basin for listing sage-grouse in 2015 under the ESA, we conducted an extensive analysis of wildfire and climatic effects on sage-grouse population growth derived from 30 years of lek-count data collected across the hydrographic Great Basin of Western North America. Annual (1984–2013) patterns of wildfire were derived from an extensive dataset of remotely sensed 30-meter imagery and precipitation derived from locally downscaled spatially explicit data. In the sagebrush ecosystem, underlying soil conditions also contribute strongly to variation in resilience to disturbance and resistance to plant community changes (R&R). Thus, we developed predictions from models of post-wildfire recovery and chronic effects of wildfire based on three spatially explicit R&R classes derived from soil moisture and temperature regimes. We found evidence of an interaction between the effects of wildfire (chronically affected burned area within 5 kilometers of a lek) and climatic conditions (spring through fall precipitation) after accounting for a consistent density-dependent effect. Specifically, burned areas near leks nullifies population growth that normally follows years with relatively high precipitation. In models, this effect results in long-term population declines for sage-grouse despite cyclic periods of high precipitation. Based on 30-year projections of burn and recovery rates, our population model predicted steady and substantial long-term declines in population size across the Great Basin. Further, example management scenarios that may help offset adverse wildfire effects are provided by models of varying levels of fire suppression and post-wildfire restoration that focus on areas especially important to sage-grouse populations. These models illustrate how sage-grouse population persistence likely will be compromised as sagebrush ecosystems and sage-grouse habitat are degraded by wildfire, especially in a warmer and drier climate, and by invasion of annual grasses that can increase wildfire frequency and size in the Great Basin.

Quantifying the impact of human activity on temperatures in Germany

NASA Astrophysics Data System (ADS)

Benz, Susanne A.; Bayer, Peter; Blum, Philipp

2017-04-01

Human activity directly influences ambient air, surface and groundwater temperatures. Alterations of surface cover and land use influence the ambient thermal regime causing spatial temperature anomalies, most commonly heat islands. These local temperature anomalies are primarily described within the bounds of large and densely populated urban settlements, where they form so-called urban heat islands (UHI). This study explores the anthropogenic impact not only for selected cities, but for the thermal regime on a countrywide scale, by analyzing mean annual temperature datasets in Germany in three different compartments: measured surface air temperature (SAT), measured groundwater temperature (GWT), and satellite-derived land surface temperature (LST). As a universal parameter to quantify anthropogenic heat anomalies, the anthropogenic heat intensity (AHI) is introduced. It is closely related to the urban heat island intensity, but determined for each pixel (for satellite-derived LST) or measurement point (for SAT and GWT) of a large, even global, dataset individually, regardless of land use and location. Hence, it provides the unique opportunity to a) compare the anthropogenic impact on temperatures in air, surface and subsurface, b) to find main instances of anthropogenic temperature anomalies within the study area, in this case Germany, and c) to study the impact of smaller settlements or industrial sites on temperatures. For all three analyzed temperature datasets, anthropogenic heat intensity grows with increasing nighttime lights and declines with increasing vegetation, whereas population density has only minor effects. While surface anthropogenic heat intensity cannot be linked to specific land cover types in the studied resolution (1 km × 1 km) and classification system, both air and groundwater show increased heat intensities for artificial surfaces. Overall, groundwater temperature appears most vulnerable to human activity; unlike land surface temperature and surface air temperature, groundwater temperatures are elevated in cultivated areas as well. At the surface of Germany, the highest anthropogenic heat intensity with 4.5 K is found at an open-pit lignite mine near Jülich, followed by three large cities (Munich, Düsseldorf and Nuremberg) with annual mean anthropogenic heat intensities > 4 K. Overall, surface anthropogenic heat intensities > 0 K and therefore urban heat islands are observed in communities down to a population of 5,000.

Potential distribution dataset of honeybees in Indian Ocean Islands: Case study of Zanzibar Island.

PubMed

Mwalusepo, Sizah; Muli, Eliud; Nkoba, Kiatoko; Nguku, Everlyn; Kilonzo, Joseph; Abdel-Rahman, Elfatih M; Landmann, Tobias; Fakih, Asha; Raina, Suresh

2017-10-01

Honeybees ( Apis mellifera ) are principal insect pollinators, whose worldwide distribution and abundance is known to largely depend on climatic conditions. However, the presence records dataset on potential distribution of honeybees in Indian Ocean Islands remain less documented. Presence records in shape format and probability of occurrence of honeybees with different temperature change scenarios is provided in this article across Zanzibar Island. Maximum entropy (Maxent) package was used to analyse the potential distribution of honeybees. The dataset provides information on the current and future distribution of the honey bees in Zanzibar Island. The dataset is of great importance for improving stakeholders understanding of the role of temperature change on the spatial distribution of honeybees.

From mobile phone data to the spatial structure of cities

PubMed Central

Louail, Thomas; Lenormand, Maxime; Cantu Ros, Oliva G.; Picornell, Miguel; Herranz, Ricardo; Frias-Martinez, Enrique; Ramasco, José J.; Barthelemy, Marc

2014-01-01

Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data. PMID:24923248

Urban-Rural Differentials: A Spatial Analysis of Alabama Students’ Recent Alcohol Use and Marijuana Use

PubMed Central

Lo, Celia C.; Weber, Joe; Cheng, Tyrone C.

2013-01-01

Background and Objectives This study of Alabama public school students sought urban-rural differences in social and spatial mechanisms connecting structural factors to recent use of alcohol and marijuana. Methods Its dataset comprised a state-sponsored 2002 need-assessment survey of Alabama students; Alabama education department data; U. S. Census data; and alcohol-outlet locations listed by Alabama’s Alcoholic Beverage Control Board. It measured structural-disadvantage factors (population disadvantages, community instability, alcohol-outlet density), social-organization factors (protective role of community, protective role of school), and recent-use factors. Using Geographic Information Systems (GIS), it generated maps of school catchment areas (SCAs)—the units of analysis for the study—that outline spatial patterns (across areas deemed urban or rural) of students’ recent use of alcohol and marijuana. Results In the final sample of 370 SCAs, significant urban-versus-rural differences were observed for certain structural factors and in how these factors were associated with substance use. These differences aside, spatial analysis weighing the SCAs’ particular geographic characteristics suggested location’s importance, showing that a school playing a strong protective role significantly reduced not just its own students’ recent substance use, but that of students in neighboring SCAs as well. Conclusions and Scientific Significance The findings show students’ recent use of alcohol and marijuana are associated with characteristics of the environment. PMID:23617858

[Spatial domain display for interference image dataset].

PubMed

Wang, Cai-Ling; Li, Yu-Shan; Liu, Xue-Bin; Hu, Bing-Liang; Jing, Juan-Juan; Wen, Jia

2011-11-01

The requirements of imaging interferometer visualization is imminent for the user of image interpretation and information extraction. However, the conventional researches on visualization only focus on the spectral image dataset in spectral domain. Hence, the quick show of interference spectral image dataset display is one of the nodes in interference image processing. The conventional visualization of interference dataset chooses classical spectral image dataset display method after Fourier transformation. In the present paper, the problem of quick view of interferometer imager in image domain is addressed and the algorithm is proposed which simplifies the matter. The Fourier transformation is an obstacle since its computation time is very large and the complexion would be even deteriorated with the size of dataset increasing. The algorithm proposed, named interference weighted envelopes, makes the dataset divorced from transformation. The authors choose three interference weighted envelopes respectively based on the Fourier transformation, features of interference data and human visual system. After comparing the proposed with the conventional methods, the results show the huge difference in display time.

Implementing DOIs for Oceanographic Satellite Data at PO.DAAC

NASA Astrophysics Data System (ADS)

Hausman, J.; Tauer, E.; Chung, N.; Chen, C.; Moroni, D. F.

2013-12-01

The Physical Oceanographic Distributed Active Archive Center (PO.DAAC) is NASA's archive for physical oceanographic satellite data. It distributes over 500 datasets from gravity, ocean wind, sea surface topography, sea ice, ocean currents, salinity, and sea surface temperature satellite missions. A dataset is a collection of granules/files that share the same mission/project, versioning, processing level, spatial, and temporal characteristics. The large number of datasets is partially due to the number of satellite missions, but mostly because a single satellite mission typically has multiple versions or even temporal and spatial resolutions of data. As a result, a user might mistake one dataset for a different dataset from the same satellite mission. Due to the PO.DAAC'S vast variety and volume of data and growing requirements to report dataset usage, it has begun implementing DOIs for the datasets it archives and distributes. However, this was not as simple as registering a name for a DOI and providing a URL. Before implementing DOIs multiple questions needed to be answered. What are the sponsor and end-user expectations regarding DOIs? At what level does a DOI get assigned (dataset, file/granule)? Do all data get a DOI, or only selected data? How do we create a DOI? How do we create landing pages and manage them? What changes need to be made to the data archive, life cycle policy and web portal to accommodate DOIs? What if the data also exists at another archive and a DOI already exists? How is a DOI included if the data were obtained via a subsetting tool? How does a researcher or author provide a unique, definitive reference (standard citation) for a given dataset? This presentation will discuss how these questions were answered through changes in policy, process, and system design. Implementing DOIs is not a trivial undertaking, but as DOIs are rapidly becoming the de facto approach, it is worth the effort. Researchers have historically referenced the source satellite and data center (or archive), but scientific writings do not typically provide enough detail to point to a singular, uniquely identifiable dataset. DOIs provide the means to help researchers be precise in their data citations and provide needed clarity, standardization and permanence.

ASSESSING THE IMPORTANCE OF THERMAL REFUGE ...

EPA Pesticide Factsheets

Salmon populations require river networks that provide water temperature regimes sufficient to support a diversity of salmonid life histories across space and time. The importance of cold water refuges for migrating adult salmon and steelhead may seem intuitive, and refuges are clearly used by fish during warm water episodes. But quantifying the value of both small and large scale thermal features to salmon populations has been challenging due to the difficulty of mapping thermal regimes at sufficient spatial and temporal resolutions, and integrating thermal regimes into population models. We attempt to address these challenges by using newly-available datasets and modeling approaches to link thermal regimes to salmon populations across scales. We discuss the challenges and opportunities to simulating fish behaviors and linking exposures to migratory and reproductive fitness. In this talk and companion poster, we describe an individual-based modeling approach for assessing sufficiency of thermal refuges for migrating salmon and steelhead in the Columbia River. Many rivers and streams in the Pacific Northwest are currently listed as impaired under the Clean Water Act as a result of high summer water temperatures. Adverse effects of warm waters include impacts to salmon and steelhead populations that may already be stressed by habitat alteration, disease, predation, and fishing pressures. Much effort is being expended to improve conditions for salmon and steelhea

Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface.

PubMed

Zhang, Yu; Zhou, Guoxu; Jin, Jing; Wang, Xingyu; Cichocki, Andrzej

2015-11-30

Common spatial pattern (CSP) has been most popularly applied to motor-imagery (MI) feature extraction for classification in brain-computer interface (BCI) application. Successful application of CSP depends on the filter band selection to a large degree. However, the most proper band is typically subject-specific and can hardly be determined manually. This study proposes a sparse filter band common spatial pattern (SFBCSP) for optimizing the spatial patterns. SFBCSP estimates CSP features on multiple signals that are filtered from raw EEG data at a set of overlapping bands. The filter bands that result in significant CSP features are then selected in a supervised way by exploiting sparse regression. A support vector machine (SVM) is implemented on the selected features for MI classification. Two public EEG datasets (BCI Competition III dataset IVa and BCI Competition IV IIb) are used to validate the proposed SFBCSP method. Experimental results demonstrate that SFBCSP help improve the classification performance of MI. The optimized spatial patterns by SFBCSP give overall better MI classification accuracy in comparison with several competing methods. The proposed SFBCSP is a potential method for improving the performance of MI-based BCI. Copyright © 2015 Elsevier B.V. All rights reserved.

Mapping the Decadal Spatio-temporal Variation of Social Vulnerability to Hydro-climatic Extremes over India

NASA Astrophysics Data System (ADS)

H, V.; Karmakar, S.; Ghosh, S.

2015-12-01

Human induced global warming is unequivocal and observational studies shows that, this has led to increase in the intensity and frequency of hydro-climatic extremes, most importantly precipitation extreme, heat waves and drought; and also is expected to be increased in the future. The occurrence of these extremes have a devastating effects on nation's economy and on societal well-being. Previous studies on India provided the evidences of significant changes in the precipitation extreme from pre- to post-1950, with huge spatial heterogeneity; and projections of heat waves indicated that significant part of India will experience heat stress conditions in the future. Under these circumstance, it is necessary to develop a nation-wide social vulnerability map to scrutinize the adequacy of existing emergency management. Yet there has been no systematic past efforts on mapping social vulnerability to hydro-climatic extremes at nation-wide for India. Therefore, immediate efforts are required to quantify the social vulnerability, particularly developing country like India, where major transformations in demographic characteristics and development patterns are evident during past decades. In the present study, we perform a comprehensive spatio-temporal social vulnerability analysis by considering multiple sensitive indicators for three decades (1990-2010) which identifies the hot-spots, with higher vulnerability to hydro-climatic extremes. The population datasets are procured from Census of India and the meteorological datasets are obtained from India Meteorological Department (IMD). The study derives interesting results on decadal changes of spatial distribution of risk, considering social vulnerability and hazard to extremes.

Scaling identity connects human mobility and social interactions.

PubMed

Deville, Pierre; Song, Chaoming; Eagle, Nathan; Blondel, Vincent D; Barabási, Albert-László; Wang, Dashun

2016-06-28

Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality.

Integrated web system of geospatial data services for climate research

NASA Astrophysics Data System (ADS)

Okladnikov, Igor; Gordov, Evgeny; Titov, Alexander

2016-04-01

Georeferenced datasets are currently actively used for modeling, interpretation and forecasting of climatic and ecosystem changes on different spatial and temporal scales. Due to inherent heterogeneity of environmental datasets as well as their huge size (up to tens terabytes for a single dataset) a special software supporting studies in the climate and environmental change areas is required. An approach for integrated analysis of georefernced climatological data sets based on combination of web and GIS technologies in the framework of spatial data infrastructure paradigm is presented. According to this approach a dedicated data-processing web system for integrated analysis of heterogeneous georeferenced climatological and meteorological data is being developed. It is based on Open Geospatial Consortium (OGC) standards and involves many modern solutions such as object-oriented programming model, modular composition, and JavaScript libraries based on GeoExt library, ExtJS Framework and OpenLayers software. This work is supported by the Ministry of Education and Science of the Russian Federation, Agreement #14.613.21.0037.

Impact of Land Cover Characterization and Properties on Snow Albedo in Climate Models

NASA Astrophysics Data System (ADS)

Wang, L.; Bartlett, P. A.; Chan, E.; Montesano, P.

2017-12-01

The simulation of winter albedo in boreal and northern environments has been a particular challenge for land surface modellers. Assessments of output from CMIP3 and CMIP5 climate models have revealed that many simulations are characterized by overestimation of albedo in the boreal forest. Recent studies suggest that inaccurate representation of vegetation distribution, improper simulation of leaf area index, and poor treatment of canopy-snow processes are the primary causes of albedo errors. While several land cover datasets are commonly used to derive plant functional types (PFT) for use in climate models, new land cover and vegetation datasets with higher spatial resolution have become available in recent years. In this study, we compare the spatial distribution of the dominant PFTs and canopy cover fractions based on different land cover datasets, and present results from offline simulations of the latest version Canadian Land Surface Scheme (CLASS) over the northern Hemisphere land. We discuss the impact of land cover representation and surface properties on winter albedo simulations in climate models.

Scaling identity connects human mobility and social interactions

PubMed Central

Deville, Pierre; Song, Chaoming; Eagle, Nathan; Blondel, Vincent D.; Barabási, Albert-László; Wang, Dashun

2016-01-01

Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality. PMID:27274050

Predicting Virtual World User Population Fluctuations with Deep Learning

PubMed Central

Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds. PMID:27936009

Predicting Virtual World User Population Fluctuations with Deep Learning.

PubMed

Kim, Young Bin; Park, Nuri; Zhang, Qimeng; Kim, Jun Gi; Kang, Shin Jin; Kim, Chang Hun

2016-01-01

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Y. X.; Van Reeth, E.; Poh, C. L., E-mail: clpoh@ntu.edu.sg

2015-08-15

Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors’ proposed approach. Methods: A novel hybrid approach based on deformable image registration (DIR) and finite elementmore » method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors’ proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.« less

A framework for automatic creation of gold-standard rigid 3D-2D registration datasets.

PubMed

Madan, Hennadii; Pernuš, Franjo; Likar, Boštjan; Špiclin, Žiga

2017-02-01

Advanced image-guided medical procedures incorporate 2D intra-interventional information into pre-interventional 3D image and plan of the procedure through 3D/2D image registration (32R). To enter clinical use, and even for publication purposes, novel and existing 32R methods have to be rigorously validated. The performance of a 32R method can be estimated by comparing it to an accurate reference or gold standard method (usually based on fiducial markers) on the same set of images (gold standard dataset). Objective validation and comparison of methods are possible only if evaluation methodology is standardized, and the gold standard  dataset is made publicly available. Currently, very few such datasets exist and only one contains images of multiple patients acquired during a procedure. To encourage the creation of gold standard 32R datasets, we propose an automatic framework. The framework is based on rigid registration of fiducial markers. The main novelty is spatial grouping of fiducial markers on the carrier device, which enables automatic marker localization and identification across the 3D and 2D images. The proposed framework was demonstrated on clinical angiograms of 20 patients. Rigid 32R computed by the framework was more accurate than that obtained manually, with the respective target registration error below 0.027 mm compared to 0.040 mm. The framework is applicable for gold standard setup on any rigid anatomy, provided that the acquired images contain spatially grouped fiducial markers. The gold standard datasets and software will be made publicly available.

Identifying spatially similar gene expression patterns in early stage fruit fly embryo images: binary feature versus invariant moment digital representations

PubMed Central

Gurunathan, Rajalakshmi; Van Emden, Bernard; Panchanathan, Sethuraman; Kumar, Sudhir

2004-01-01

Background Modern developmental biology relies heavily on the analysis of embryonic gene expression patterns. Investigators manually inspect hundreds or thousands of expression patterns to identify those that are spatially similar and to ultimately infer potential gene interactions. However, the rapid accumulation of gene expression pattern data over the last two decades, facilitated by high-throughput techniques, has produced a need for the development of efficient approaches for direct comparison of images, rather than their textual descriptions, to identify spatially similar expression patterns. Results The effectiveness of the Binary Feature Vector (BFV) and Invariant Moment Vector (IMV) based digital representations of the gene expression patterns in finding biologically meaningful patterns was compared for a small (226 images) and a large (1819 images) dataset. For each dataset, an ordered list of images, with respect to a query image, was generated to identify overlapping and similar gene expression patterns, in a manner comparable to what a developmental biologist might do. The results showed that the BFV representation consistently outperforms the IMV representation in finding biologically meaningful matches when spatial overlap of the gene expression pattern and the genes involved are considered. Furthermore, we explored the value of conducting image-content based searches in a dataset where individual expression components (or domains) of multi-domain expression patterns were also included separately. We found that this technique improves performance of both IMV and BFV based searches. Conclusions We conclude that the BFV representation consistently produces a more extensive and better list of biologically useful patterns than the IMV representation. The high quality of results obtained scales well as the search database becomes larger, which encourages efforts to build automated image query and retrieval systems for spatial gene expression patterns. PMID:15603586

Improvement in the accuracy of back trajectories using WRF to identify pollen sources in southern Iberian Peninsula.

PubMed

Hernández-Ceballos, M A; Skjøth, C A; García-Mozo, H; Bolívar, J P; Galán, C

2014-12-01

Airborne pollen transport at micro-, meso-gamma and meso-beta scales must be studied by atmospheric models, having special relevance in complex terrain. In these cases, the accuracy of these models is mainly determined by the spatial resolution of the underlying meteorological dataset. This work examines how meteorological datasets determine the results obtained from atmospheric transport models used to describe pollen transport in the atmosphere. We investigate the effect of the spatial resolution when computing backward trajectories with the HYSPLIT model. We have used meteorological datasets from the WRF model with 27, 9 and 3 km resolutions and from the GDAS files with 1° resolution. This work allows characterizing atmospheric transport of Olea pollen in a region with complex flows. The results show that the complex terrain affects the trajectories and this effect varies with the different meteorological datasets. Overall, the change from GDAS to WRF-ARW inputs improves the analyses with the HYSPLIT model, thereby increasing the understanding the pollen episode. The results indicate that a spatial resolution of at least 9 km is needed to simulate atmospheric flows that are considerable affected by the relief of the landscape. The results suggest that the appropriate meteorological files should be considered when atmospheric models are used to characterize the atmospheric transport of pollen on micro-, meso-gamma and meso-beta scales. Furthermore, at these scales, the results are believed to be generally applicable for related areas such as the description of atmospheric transport of radionuclides or in the definition of nuclear-radioactivity emergency preparedness.

Improvement in the accuracy of back trajectories using WRF to identify pollen sources in southern Iberian Peninsula

NASA Astrophysics Data System (ADS)

Hernández-Ceballos, M. A.; Skjøth, C. A.; García-Mozo, H.; Bolívar, J. P.; Galán, C.

2014-12-01

Airborne pollen transport at micro-, meso-gamma and meso-beta scales must be studied by atmospheric models, having special relevance in complex terrain. In these cases, the accuracy of these models is mainly determined by the spatial resolution of the underlying meteorological dataset. This work examines how meteorological datasets determine the results obtained from atmospheric transport models used to describe pollen transport in the atmosphere. We investigate the effect of the spatial resolution when computing backward trajectories with the HYSPLIT model. We have used meteorological datasets from the WRF model with 27, 9 and 3 km resolutions and from the GDAS files with 1 ° resolution. This work allows characterizing atmospheric transport of Olea pollen in a region with complex flows. The results show that the complex terrain affects the trajectories and this effect varies with the different meteorological datasets. Overall, the change from GDAS to WRF-ARW inputs improves the analyses with the HYSPLIT model, thereby increasing the understanding the pollen episode. The results indicate that a spatial resolution of at least 9 km is needed to simulate atmospheric flows that are considerable affected by the relief of the landscape. The results suggest that the appropriate meteorological files should be considered when atmospheric models are used to characterize the atmospheric transport of pollen on micro-, meso-gamma and meso-beta scales. Furthermore, at these scales, the results are believed to be generally applicable for related areas such as the description of atmospheric transport of radionuclides or in the definition of nuclear-radioactivity emergency preparedness.

Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers' recording behaviour.

PubMed

Boakes, Elizabeth H; Gliozzo, Gianfranco; Seymour, Valentine; Harvey, Martin; Smith, Chloë; Roy, David B; Haklay, Muki

2016-09-13

The often opportunistic nature of biological recording via citizen science leads to taxonomic, spatial and temporal biases which add uncertainty to biodiversity estimates. However, such biases may also give valuable insight into volunteers' recording behaviour. Using Greater London as a case-study we examined the composition of three citizen science datasets - from Greenspace Information for Greater London CIC, iSpot and iRecord - with respect to recorder contribution and spatial and taxonomic biases, i.e. when, where and what volunteers record. We found most volunteers contributed few records and were active for just one day. Each dataset had its own taxonomic and spatial signature suggesting that volunteers' personal recording preferences may attract them towards particular schemes. There were also patterns across datasets: species' abundance and ease of identification were positively associated with number of records, as was plant height. We found clear hotspots of recording activity, the 10 most popular sites containing open water. We note that biases are accrued as part of the recording process (e.g. species' detectability) as well as from volunteer preferences. An increased understanding of volunteer behaviour gained from analysing the composition of records could thus enhance the fit between volunteers' interests and the needs of scientific projects.

Digital Astronaut Photography: A Discovery Dataset for Archaeology

NASA Technical Reports Server (NTRS)

Stefanov, William L.

2010-01-01

Astronaut photography acquired from the International Space Station (ISS) using commercial off-the-shelf cameras offers a freely-accessible source for high to very high resolution (4-20 m/pixel) visible-wavelength digital data of Earth. Since ISS Expedition 1 in 2000, over 373,000 images of the Earth-Moon system (including land surface, ocean, atmospheric, and lunar images) have been added to the Gateway to Astronaut Photography of Earth online database (http://eol.jsc.nasa.gov ). Handheld astronaut photographs vary in look angle, time of acquisition, solar illumination, and spatial resolution. These attributes of digital astronaut photography result from a unique combination of ISS orbital dynamics, mission operations, camera systems, and the individual skills of the astronaut. The variable nature of astronaut photography makes the dataset uniquely useful for archaeological applications in comparison with more traditional nadir-viewing multispectral datasets acquired from unmanned orbital platforms. For example, surface features such as trenches, walls, ruins, urban patterns, and vegetation clearing and regrowth patterns may be accentuated by low sun angles and oblique viewing conditions (Fig. 1). High spatial resolution digital astronaut photographs can also be used with sophisticated land cover classification and spatial analysis approaches like Object Based Image Analysis, increasing the potential for use in archaeological characterization of landscapes and specific sites.

Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour

PubMed Central

Boakes, Elizabeth H.; Gliozzo, Gianfranco; Seymour, Valentine; Harvey, Martin; Smith, Chloë; Roy, David B.; Haklay, Muki

2016-01-01

The often opportunistic nature of biological recording via citizen science leads to taxonomic, spatial and temporal biases which add uncertainty to biodiversity estimates. However, such biases may also give valuable insight into volunteers’ recording behaviour. Using Greater London as a case-study we examined the composition of three citizen science datasets – from Greenspace Information for Greater London CIC, iSpot and iRecord - with respect to recorder contribution and spatial and taxonomic biases, i.e. when, where and what volunteers record. We found most volunteers contributed few records and were active for just one day. Each dataset had its own taxonomic and spatial signature suggesting that volunteers’ personal recording preferences may attract them towards particular schemes. There were also patterns across datasets: species’ abundance and ease of identification were positively associated with number of records, as was plant height. We found clear hotspots of recording activity, the 10 most popular sites containing open water. We note that biases are accrued as part of the recording process (e.g. species’ detectability) as well as from volunteer preferences. An increased understanding of volunteer behaviour gained from analysing the composition of records could thus enhance the fit between volunteers’ interests and the needs of scientific projects. PMID:27619155

An Archive of Downscaled WCRP CMIP3 Climate Projections for Planning Applications in the Contiguous United States

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Duffy, P. B.

2007-12-01

Incorporating climate change information into long-term evaluations of water and energy resources requires analysts to have access to climate projection data that have been spatially downscaled to "basin-relevant" resolution. This is necessary in order to develop system-specific hydrology and demand scenarios consistent with projected climate scenarios. Analysts currently have access to "climate model" resolution data (e.g., at LLNL PCMDI), but not spatially downscaled translations of these datasets. Motivated by a common interest in supporting regional and local assessments, the U.S. Bureau of Reclamation and LLNL (through support from the DOE National Energy Technology Laboratory) have teamed to develop an archive of downscaled climate projections (temperature and precipitation) with geographic coverage consistent with the North American Land Data Assimilation System domain, encompassing the contiguous United States. A web-based information service, hosted at LLNL Green Data Oasis, has been developed to provide Reclamation, LLNL, and other interested analysts free access to archive content. A contemporary statistical method was used to bias-correct and spatially disaggregate projection datasets, and was applied to 112 projections included in the WCRP CMIP3 multi-model dataset hosted by LLNL PCMDI (i.e. 16 GCMs and their multiple simulations of SRES A2, A1b, and B1 emissions pathways).

High quality high spatial resolution functional classification in low dose dynamic CT perfusion using singular value decomposition (SVD) and k-means clustering

NASA Astrophysics Data System (ADS)

Pisana, Francesco; Henzler, Thomas; Schönberg, Stefan; Klotz, Ernst; Schmidt, Bernhard; Kachelrieß, Marc

2017-03-01

Dynamic CT perfusion acquisitions are intrinsically high-dose examinations, due to repeated scanning. To keep radiation dose under control, relatively noisy images are acquired. Noise is then further enhanced during the extraction of functional parameters from the post-processing of the time attenuation curves of the voxels (TACs) and normally some smoothing filter needs to be employed to better visualize any perfusion abnormality, but sacrificing spatial resolution. In this study we propose a new method to detect perfusion abnormalities keeping both high spatial resolution and high CNR. To do this we first perform the singular value decomposition (SVD) of the original noisy spatial temporal data matrix to extract basis functions of the TACs. Then we iteratively cluster the voxels based on a smoothed version of the three most significant singular vectors. Finally, we create high spatial resolution 3D volumes where to each voxel is assigned a distance from the centroid of each cluster, showing how functionally similar each voxel is compared to the others. The method was tested on three noisy clinical datasets: one brain perfusion case with an occlusion in the left internal carotid, one healthy brain perfusion case, and one liver case with an enhancing lesion. Our method successfully detected all perfusion abnormalities with higher spatial precision when compared to the functional maps obtained with a commercially available software. We conclude this method might be employed to have a rapid qualitative indication of functional abnormalities in low dose dynamic CT perfusion datasets. The method seems to be very robust with respect to both spatial and temporal noise and does not require any special a priori assumption. While being more robust respect to noise and with higher spatial resolution and CNR when compared to the functional maps, our method is not quantitative and a potential usage in clinical routine could be as a second reader to assist in the maps evaluation, or to guide a dataset smoothing before the modeling part.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

PubMed

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

PubMed Central

Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

2015-01-01

We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

Spatiotemporal patterns of livestock manure nutrient production in the conterminous United States from 1930 to 2012.

PubMed

Yang, Qichun; Tian, Hanqin; Li, Xia; Ren, Wei; Zhang, Bowen; Zhang, Xuesong; Wolf, Julie

2016-01-15

Manure nitrogen (N) and phosphorus (P) from livestock husbandry are important components of terrestrial biogeochemical cycling. Assessment of the impacts of livestock manure on terrestrial biogeochemistry requires a compilation and analysis of spatial and temporal patterns of manure nutrients. In this study, we reconstructed county-level manure nutrient data of the conterminous United States (U.S.) in 4- to 5-year increments from 1930 to 2012. Manure N and P were 5.8 9 ± 0.64 Tg N yr.(-1) (Mean ± Standard Deviation) and 1.73 ± 0.29 Tg Pyr.(-1) (1 Tg = 10(12)g), and increased by 46% and 92% from 1930 to 2012, respectively. Prior to 1970, manure provided more N to the U.S. lands than chemical fertilizer use. Since 1970, however, increasing chemical N fertilizer use has exceeded manure N production. Manure was the primary P source in the U.S. during 1930-1969 and 1987-2012, but was lower than P fertilizer use in 1974, 1978, and 1982. High-nutrient-production regions shifted towards eastern and western areas of the U.S. Decreasing small farms and increasing Concentrated Animal Feeding Operations (CAFOs) induced concentrated spatial patterns in manure nutrient loads. Counties with cattle or poultry as the primary manure nutrient contributors expanded significantly from 1930 to 2012, whereas regions with sheep and hog as the primary contributors decreased. We identified regions facing environmental threats associated with livestock farming. Effective management of manure should consider the impacts of CAFOs in manure production, and changes in livestock population structure. The long-term county-level manure nutrient dataset provides improved spatial and temporal information on manure nutrients in the U.S. This dataset is expected to help advance research on nutrient cycling, ammonia volatilization, greenhouse gas (GHG) emissions from livestock husbandry, recovery and reuse of manure nutrients, and impacts of livestock feeding on human health in the context of global change. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatiotemporal patterns of livestock manure nutrient production in the conterminous United States from 1930 to 2012

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Qichun; Tian, Hanqin; Li, Xia

Manure nitrogen (N) and phosphorus (P) from livestock husbandry are important components of terrestrial biogeochemical cycling. Assessment of the impacts of livestock manure on terrestrial biogeochemistry requires a compilation and analysis of spatial and temporal patterns of manure nutrients. In this study, we reconstructed county-level manure nutrient data of the conterminous United States (U.S.) in 4- to 5-year increments from 1930 to 2012. Manure N and P were 5.89 +/- 0.64 Tg N yr.(-1) (Mean +/- Standard Deviation) and 1.73 +/- 0.29 Tg P yr.(-1) (1 Tg=10(12) g), and increased by 46% and 92% from 1930 to 2012, respectively. Priormore » to 1970, manure provided more N to the U.S. lands than chemical fertilizer use. Since 1970, however, increasing chemical N fertilizer use has exceeded manure N production. Manure was the primary P source in the U.S. during 1930-1969 and 1987-2012, but was lower than P fertilizer use in 1974, 1978, and 1982. High-nutrient-production regions shifted towards eastern and western areas of the U.S. Decreasing small farms and increasing Concentrated Animal Feeding Operations (CAFOs) induced concentrated spatial patterns in manure nutrient loads. Counties with cattle or poultry as the primary manure nutrient contributors expanded significantly from 1930 to 2012, whereas regions with sheep and hog as the primary contributors decreased. We identified regions facing environmental threats associated with livestock farming. Effective management of manure should consider the impacts of CAFOs inmanure production, and changes in livestock population structure. The long-term county-level manure nutrient dataset provides improved spatial and temporal information on manure nutrients in the U.S. This dataset is expected to help advance research on nutrient cycling, ammonia volatilization, greenhouse gas (GHG) emissions from livestock husbandry, recovery and reuse of manure nutrients, and impacts of livestock feeding on human health in the context of global change.« less

Web-GIS visualisation of permafrost-related Remote Sensing products for ESA GlobPermafrost

NASA Astrophysics Data System (ADS)

Haas, A.; Heim, B.; Schaefer-Neth, C.; Laboor, S.; Nitze, I.; Grosse, G.; Bartsch, A.; Kaab, A.; Strozzi, T.; Wiesmann, A.; Seifert, F. M.

2016-12-01

The ESA GlobPermafrost (www.globpermafrost.info) provides a remote sensing service for permafrost research and applications. The service comprises of data product generation for various sites and regions as well as specific infrastructure allowing overview and access to datasets. Based on an online user survey conducted within the project, the user community extensively applies GIS software to handle remote sensing-derived datasets and requires preview functionalities before accessing them. In response, we develop the Permafrost Information System PerSys which is conceptualized as an open access geospatial data dissemination and visualization portal. PerSys will allow visualisation of GlobPermafrost raster and vector products such as land cover classifications, Landsat multispectral index trend datasets, lake and wetland extents, InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, and land surface temperature datasets. The datasets will be published as WebGIS services relying on OGC-standardized Web Mapping Service (WMS) and Web Feature Service (WFS) technologies for data display and visualization. The WebGIS environment will be hosted at the AWI computing centre where a geodata infrastructure has been implemented comprising of ArcGIS for Server 10.4, PostgreSQL 9.2 and a browser-driven data viewer based on Leaflet (http://leafletjs.com). Independently, we will provide an `Access - Restricted Data Dissemination Service', which will be available to registered users for testing frequently updated versions of project datasets. PerSys will become a core project of the Arctic Permafrost Geospatial Centre (APGC) within the ERC-funded PETA-CARB project (www.awi.de/petacarb). The APGC Data Catalogue will contain all final products of GlobPermafrost, allow in-depth dataset search via keywords, spatial and temporal coverage, data type, etc., and will provide DOI-based links to the datasets archived in the long-term, open access PANGAEA data repository.

On the uncertainties associated with using gridded rainfall data as a proxy for observed

NASA Astrophysics Data System (ADS)

Tozer, C. R.; Kiem, A. S.; Verdon-Kidd, D. C.

2012-05-01

Gridded rainfall datasets are used in many hydrological and climatological studies, in Australia and elsewhere, including for hydroclimatic forecasting, climate attribution studies and climate model performance assessments. The attraction of the spatial coverage provided by gridded data is clear, particularly in Australia where the spatial and temporal resolution of the rainfall gauge network is sparse. However, the question that must be asked is whether it is suitable to use gridded data as a proxy for observed point data, given that gridded data is inherently "smoothed" and may not necessarily capture the temporal and spatial variability of Australian rainfall which leads to hydroclimatic extremes (i.e. droughts, floods). This study investigates this question through a statistical analysis of three monthly gridded Australian rainfall datasets - the Bureau of Meteorology (BOM) dataset, the Australian Water Availability Project (AWAP) and the SILO dataset. The results of the monthly, seasonal and annual comparisons show that not only are the three gridded datasets different relative to each other, there are also marked differences between the gridded rainfall data and the rainfall observed at gauges within the corresponding grids - particularly for extremely wet or extremely dry conditions. Also important is that the differences observed appear to be non-systematic. To demonstrate the hydrological implications of using gridded data as a proxy for gauged data, a rainfall-runoff model is applied to one catchment in South Australia initially using gauged data as the source of rainfall input and then gridded rainfall data. The results indicate a markedly different runoff response associated with each of the different sources of rainfall data. It should be noted that this study does not seek to identify which gridded dataset is the "best" for Australia, as each gridded data source has its pros and cons, as does gauged data. Rather, the intention is to quantify differences between various gridded data sources and how they compare with gauged data so that these differences can be considered and accounted for in studies that utilise these gridded datasets. Ultimately, if key decisions are going to be based on the outputs of models that use gridded data, an estimate (or at least an understanding) of the uncertainties relating to the assumptions made in the development of gridded data and how that gridded data compares with reality should be made.

Creating Digital Environments for Multi-Agent Simulation

DTIC Science & Technology

2003-12-01

foliage on a polygon to represent a tree). Tile A spatial partition of a coverage that shares the same set of feature classes with the same... orthophoto datasets can be made from rectified grayscale aerial images. These datasets can support various weapon systems, Command, Control...Raster Product Format (RPF) Standard. This data consists of unclassified seamless orthophotos , made from rectified grayscale aerial images. DOI 10

[Spatial and Temporal Variations in Spectrum-Derived Vegetation Growth Trend in Qinghai-Tibetan Plateau from 1982 to 2014].

PubMed

Wang, Zhi-wei; Wu, Xiao-dong; Yue, Guang-yang; Zhao, Lin; Wang, Qian; Nan, Zhuo-tong; Qin, Yu; Wu, Tong-hua; Shi, Jian-zong; Zou, De-fu

2016-02-01

Recently considerable researches have focused on monitoring vegetation changes because of its important role in regula- ting the terrestrial carbon cycle and the climate system. There were the largest areas with high-altitudes in the Qinghai-Tibet Plateau (QTP), which is often referred to as the third pole of the world. And vegetation in this region is significantly sensitive to the global warming. Meanwhile NDVI dataset was one of the most useful tools to monitor the vegetation activity with high spatial and temporal resolution, which is a normalized transform of the near-infrared radiation (NIR) to red reflectance ratio. Therefore, an extended GIMMS NDVI dataset from 1982-2006 to 1982-2014 was presented using a unary linear regression by MODIS dataset from 2000 to 2014 in QTP. Compared with previous researches, the accuracy of the extended NDVI dataset was improved again with consideration the residuals derived from scale transformation. So the model of extend NDVI dataset could be a new method to integrate different NDVI products. With the extended NDVI dataset, we found that in growing season there was a statistically significant increase (0.000 4 yr⁻¹, r² = 0.585 9, p < 0.001) in QTP from 1982 to 2014. During the study pe- riod, the trends of NDVI were significantly increased in spring (0.000 5 yr⁻¹, r² = 0.295 4, p = 0.001), summer (0.000 3 yr⁻¹, r² = 0.105 3, p = 0.065) and autumn respectively (0.000 6 yr⁻¹, r² = 0.436 7, p < 0.001). Due to the increased vegeta- tion activity in Qinghai-Tibet Plateau from 1982 to 2014, the magnitude of carbon sink was accumulated in this region also at this same period. Then the data of temperature and precipitation was used to explore the reason of vegetation changed. Although the trends of them are both increased, the correlation between NDVI and temperature is higher than precipitation in vegetation grow- ing season, spring, summer and autumn. Furthermore, there is significant spatial heterogeneity of the changing trends for ND- VI, temperature and precipitation at Qinghai-Tibet Plateau scale.

interPopula: a Python API to access the HapMap Project dataset

PubMed Central

2010-01-01

Background The HapMap project is a publicly available catalogue of common genetic variants that occur in humans, currently including several million SNPs across 1115 individuals spanning 11 different populations. This important database does not provide any programmatic access to the dataset, furthermore no standard relational database interface is provided. Results interPopula is a Python API to access the HapMap dataset. interPopula provides integration facilities with both the Python ecology of software (e.g. Biopython and matplotlib) and other relevant human population datasets (e.g. Ensembl gene annotation and UCSC Known Genes). A set of guidelines and code examples to address possible inconsistencies across heterogeneous data sources is also provided. Conclusions interPopula is a straightforward and flexible Python API that facilitates the construction of scripts and applications that require access to the HapMap dataset. PMID:21210977

Statistical Inference and Spatial Patterns in Correlates of IQ

ERIC Educational Resources Information Center

Hassall, Christopher; Sherratt, Thomas N.

2011-01-01

Cross-national comparisons of IQ have become common since the release of a large dataset of international IQ scores. However, these studies have consistently failed to consider the potential lack of independence of these scores based on spatial proximity. To demonstrate the importance of this omission, we present a re-evaluation of several…

A geologic and mineral exploration spatial database for the Stillwater Complex, Montana

USGS Publications Warehouse

Zientek, Michael L.; Parks, Heather L.

2014-01-01

This report provides essential spatially referenced datasets based on geologic mapping and mineral exploration activities conducted from the 1920s to the 1990s. This information will facilitate research on the complex and provide background material needed to explore for mineral resources and to develop sound land-management policy.

EXIMS: an improved data analysis pipeline based on a new peak picking method for EXploring Imaging Mass Spectrometry data.

PubMed

Wijetunge, Chalini D; Saeed, Isaam; Boughton, Berin A; Spraggins, Jeffrey M; Caprioli, Richard M; Bacic, Antony; Roessner, Ute; Halgamuge, Saman K

2015-10-01

Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in 'omics' data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets. The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups. We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization. The source code and sample data are freely available at http://exims.sourceforge.net/. awgcdw@student.unimelb.edu.au or chalini_w@live.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Intensity-Duration-Frequency curves from remote sensing datasets: direct comparison of weather radar and CMORPH over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Morin, Efrat; Marra, Francesco; Peleg, Nadav; Mei, Yiwen; Anagnostou, Emmanouil N.

2017-04-01

Rainfall frequency analysis is used to quantify the probability of occurrence of extreme rainfall and is traditionally based on rain gauge records. The limited spatial coverage of rain gauges is insufficient to sample the spatiotemporal variability of extreme rainfall and to provide the areal information required by management and design applications. Conversely, remote sensing instruments, even if quantitative uncertain, offer coverage and spatiotemporal detail that allow overcoming these issues. In recent years, remote sensing datasets began to be used for frequency analyses, taking advantage of increased record lengths and quantitative adjustments of the data. However, the studies so far made use of concepts and techniques developed for rain gauge (i.e. point or multiple-point) data and have been validated by comparison with gauge-derived analyses. These procedures add further sources of uncertainty and prevent from isolating between data and methodological uncertainties and from fully exploiting the available information. In this study, we step out of the gauge-centered concept presenting a direct comparison between at-site Intensity-Duration-Frequency (IDF) curves derived from different remote sensing datasets on corresponding spatial scales, temporal resolutions and records. We analyzed 16 years of homogeneously corrected and gauge-adjusted C-Band weather radar estimates, high-resolution CMORPH and gauge-adjusted high-resolution CMORPH over the Eastern Mediterranean. Results of this study include: (a) good spatial correlation between radar and satellite IDFs ( 0.7 for 2-5 years return period); (b) consistent correlation and dispersion in the raw and gauge adjusted CMORPH; (c) bias is almost uniform with return period for 12-24 h durations; (d) radar identifies thicker tail distributions than CMORPH and the tail of the distributions depends on the spatial and temporal scales. These results demonstrate the potential of remote sensing datasets for rainfall frequency analysis for management (e.g. warning and early-warning systems) and design (e.g. sewer design, large scale drainage planning)

Spatio-Temporal Gap Analysis of OBIS-SEAMAP Project Data: Assessment and Way Forward

PubMed Central

Kot, Connie Y.; Fujioka, Ei; Hazen, Lucie J.; Best, Benjamin D.; Read, Andrew J.; Halpin, Patrick N.

2010-01-01

The OBIS-SEAMAP project has acquired and served high-quality marine mammal, seabird, and sea turtle data to the public since its inception in 2002. As data accumulated, spatial and temporal biases resulted and a comprehensive gap analysis was needed in order to assess coverage to direct data acquisition for the OBIS-SEAMAP project and for taxa researchers should true gaps in knowledge exist. All datasets published on OBIS-SEAMAP up to February 2009 were summarized spatially and temporally. Seabirds comprised the greatest number of records, compared to the other two taxa, and most records were from shipboard surveys, compared to the other three platforms. Many of the point observations and polyline tracklines were located in northern and central Atlantic and the northeastern and central-eastern Pacific. The Southern Hemisphere generally had the lowest representation of data, with the least number of records in the southern Atlantic and western Pacific regions. Temporally, records of observations for all taxa were the lowest in fall although the number of animals sighted was lowest in the winter. Oceanographic coverage of observations varied by platform for each taxa, which showed that using two or more platforms represented habitat ranges better than using only one alone. Accessible and published datasets not already incorporated do exist within spatial and temporal gaps identified. Other related open-source data portals also contain data that fill gaps, emphasizing the importance of dedicated data exchange. Temporal and spatial gaps were mostly a result of data acquisition effort, development of regional partnerships and collaborations, and ease of field data collection. Future directions should include fostering partnerships with researchers in the Southern Hemisphere while targeting datasets containing species with limited representation. These results can facilitate prioritizing datasets needed to be represented and for planning research for true gaps in space and time. PMID:20886047

Modeling the Hydrological Regime of Turkana Lake (Kenya, Ethiopia) by Combining Spatially Distributed Hydrological Modeling and Remote Sensing Datasets

NASA Astrophysics Data System (ADS)

Anghileri, D.; Kaelin, A.; Peleg, N.; Fatichi, S.; Molnar, P.; Roques, C.; Longuevergne, L.; Burlando, P.

2017-12-01

Hydrological modeling in poorly gauged basins can benefit from the use of remote sensing datasets although there are challenges associated with the mismatch in spatial and temporal scales between catchment scale hydrological models and remote sensing products. We model the hydrological processes and long-term water budget of the Lake Turkana catchment, a transboundary basin between Kenya and Ethiopia, by integrating several remote sensing products into a spatially distributed and physically explicit model, Topkapi-ETH. Lake Turkana is the world largest desert lake draining a catchment of 145'500 km2. It has three main contributing rivers: the Omo river, which contributes most of the annual lake inflow, the Turkwel river, and the Kerio rivers, which contribute the remaining part. The lake levels have shown great variations in the last decades due to long-term climate fluctuations and the regulation of three reservoirs, Gibe I, II, and III, which significantly alter the hydrological seasonality. Another large reservoir is planned and may be built in the next decade, generating concerns about the fate of Lake Turkana in the long run because of this additional anthropogenic pressure and increasing evaporation driven by climate change. We consider different remote sensing datasets, i.e., TRMM-V7 for precipitation, MERRA-2 for temperature, as inputs to the spatially distributed hydrological model. We validate the simulation results with other remote sensing datasets, i.e., GRACE for total water storage anomalies, GLDAS-NOAH for soil moisture, ERA-Interim/Land for surface runoff, and TOPEX/Poseidon for satellite altimetry data. Results highlight how different remote sensing products can be integrated into a hydrological modeling framework accounting for their relative uncertainties. We also carried out simulations with the artificial reservoirs planned in the north part of the catchment and without any reservoirs, to assess their impacts on the catchment hydrological regime and the Lake Turkana level variability.

Spatio-temporal gap analysis of OBIS-SEAMAP project data: assessment and way forward.

PubMed

Kot, Connie Y; Fujioka, Ei; Hazen, Lucie J; Best, Benjamin D; Read, Andrew J; Halpin, Patrick N

2010-09-24

The OBIS-SEAMAP project has acquired and served high-quality marine mammal, seabird, and sea turtle data to the public since its inception in 2002. As data accumulated, spatial and temporal biases resulted and a comprehensive gap analysis was needed in order to assess coverage to direct data acquisition for the OBIS-SEAMAP project and for taxa researchers should true gaps in knowledge exist. All datasets published on OBIS-SEAMAP up to February 2009 were summarized spatially and temporally. Seabirds comprised the greatest number of records, compared to the other two taxa, and most records were from shipboard surveys, compared to the other three platforms. Many of the point observations and polyline tracklines were located in northern and central Atlantic and the northeastern and central-eastern Pacific. The Southern Hemisphere generally had the lowest representation of data, with the least number of records in the southern Atlantic and western Pacific regions. Temporally, records of observations for all taxa were the lowest in fall although the number of animals sighted was lowest in the winter. Oceanographic coverage of observations varied by platform for each taxa, which showed that using two or more platforms represented habitat ranges better than using only one alone. Accessible and published datasets not already incorporated do exist within spatial and temporal gaps identified. Other related open-source data portals also contain data that fill gaps, emphasizing the importance of dedicated data exchange. Temporal and spatial gaps were mostly a result of data acquisition effort, development of regional partnerships and collaborations, and ease of field data collection. Future directions should include fostering partnerships with researchers in the Southern Hemisphere while targeting datasets containing species with limited representation. These results can facilitate prioritizing datasets needed to be represented and for planning research for true gaps in space and time.

Application of spatial technology in malaria research & control: some new insights.

PubMed

Saxena, Rekha; Nagpal, B N; Srivastava, Aruna; Gupta, S K; Dash, A P

2009-08-01

Geographical information System (GIS) has emerged as the core of the spatial technology which integrates wide range of dataset available from different sources including Remote Sensing (RS) and Global Positioning System (GPS). Literature published during the decade (1998-2007) has been compiled and grouped into six categories according to the usage of the technology in malaria epidemiology. Different GIS modules like spatial data sources, mapping and geo-processing tools, distance calculation, digital elevation model (DEM), buffer zone and geo-statistical analysis have been investigated in detail, illustrated with examples as per the derived results. These GIS tools have contributed immensely in understanding the epidemiological processes of malaria and examples drawn have shown that GIS is now widely used for research and decision making in malaria control. Statistical data analysis currently is the most consistent and established set of tools to analyze spatial datasets. The desired future development of GIS is in line with the utilization of geo-statistical tools which combined with high quality data has capability to provide new insight into malaria epidemiology and the complexity of its transmission potential in endemic areas.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

MVIRI/SEVIRI TOA Radiation Datasets within the Climate Monitoring SAF

NASA Astrophysics Data System (ADS)

Urbain, Manon; Clerbaux, Nicolas; Ipe, Alessandro; Baudrez, Edward; Velazquez Blazquez, Almudena; Moreels, Johan

2016-04-01

Within CM SAF, Interim Climate Data Records (ICDR) of Top-Of-Atmosphere (TOA) radiation products from the Geostationary Earth Radiation Budget (GERB) instruments on the Meteosat Second Generation (MSG) satellites have been released in 2013. These datasets (referred to as CM-113 and CM-115, resp. for shortwave (SW) and longwave (LW) radiation) are based on the instantaneous TOA fluxes from the GERB Edition-1 dataset. They cover the time period 2004-2011. Extending these datasets backward in the past is not possible as no GERB instruments were available on the Meteosat First Generation (MFG) satellites. As an alternative, it is proposed to rely on the Meteosat Visible and InfraRed Imager (MVIRI - from 1982 until 2004) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI - from 2004 onward) to generate a long Thematic Climate Data Record (TCDR) from Meteosat instruments. Combining MVIRI and SEVIRI allows an unprecedented temporal (30 minutes / 15 minutes) and spatial (2.5 km / 3 km) resolution compared to the Clouds and the Earth's Radiant Energy System (CERES) products. This is a step forward as it helps to increase the knowledge of the diurnal cycle and the small-scale spatial variations of radiation. The MVIRI/SEVIRI datasets (referred to as CM-23311 and CM-23341, resp. for SW and LW radiation) will provide daily and monthly averaged TOA Reflected Solar (TRS) and Emitted Thermal (TET) radiation in "all-sky" conditions (no clear-sky conditions for this first version of the datasets), as well as monthly averaged of the hourly integrated values. The SEVIRI Solar Channels Calibration (SSCC) and the operational calibration have been used resp. for the SW and LW channels. For MFG, it is foreseen to replace the latter by the EUMETSAT/GSICS recalibration of MVIRI using HIRS. The CERES TRMM angular dependency models have been used to compute TRS fluxes while theoretical models have been used for TET fluxes. The CM-23311 and CM-23341 datasets will cover a 32 years time period, from 1st February 1982 to 31st January 2014. TRS and TET fluxes will be provided on a regular latitude-longitude grid at a spatial resolution of 0.05° (i.e. about 5.5 km) to ensure consistency with other CM SAF products. Validation will be performed at lower resolution (e.g. 1° x 1°) by intercomparison with several other datasets (CERES EBAF, CERES SYN 1deg-day, HIRS OLR, ISCCP-FD, NCDC daily OLR, etc.).

Definition of radon prone areas in Friuli Venezia Giulia region, Italy, using geostatistical tools.

PubMed

Cafaro, C; Bossew, P; Giovani, C; Garavaglia, M

2014-12-01

Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m(3) and 183 Bq/m(3). The geometric mean value is 100 Bq/m(3). Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

An Evaluation of Data Fusion Products for the Analysis of Dryland Forest Phenology

NASA Astrophysics Data System (ADS)

Walker, J. J.; de Beurs, K.; Wynne, R. H.; Gao, F.

2010-12-01

Semi-arid forest areas cover a significant proportion of the world’s land surface; in the interior western U.S. alone, dryland forests extend across more than 56 million hectares. The scarcity of water in these systems makes them acutely sensitive to sustained weather fluctuations, such as the higher temperatures and altered water regimes predicted under most climate change scenarios. To understand, monitor, and predict the anticipated spatial and temporal changes in these areas, it is vital to characterize current phenological patterns. Phenological analysis of western U.S. drylands is complicated by patchy land cover and mosaics of plant phenology states at a variety of spatial scales. Our aim is to use complementary satellite sensors to mitigate these difficulties and gain greater insight into phenological patterns in dryland forests. In this study we applied the spatial and temporal adaptive reflectance model (STARFM; Gao et al. 2006) to fuse Landsat and MODIS imagery to create synthetic images at Landsat spatial resolution and MODIS temporal resolution. To determine which MODIS dataset is most appropriate for the creation of synthetic images intended for the analysis of dryland forest phenology, we examined the effect of temporal compositing and BRDF function adjustment on the accuracy of STARFM imagery. We assembled seven Landsat 5 scenes (path/row 37/36) and temporally-coincident 500m MODIS datasets (seven daily (MOD09GA), seven 8-day composite (MOD09A1), and fourteen 16-day nadir BRDF-adjusted composite (MCD43A4) images) spanning the 2006 April - October growing season in northern Arizona, which is characterized by large tracts of dryland forest. The STARFM algorithm was applied to each MODIS data series to produce four synthetic images (one daily; one 8-day composite; and two 16-day composites) corresponding to each Landsat image. Validation of the accuracy of the synthetic images was achieved by comparing the reflectance values of a random sample of the identified dryland forest pixels in both images. Preliminary data analysis of the effect of the temporal resolution and dataset parameters indicates that the MODIS 8-day composite image may be a suitable and sufficient dataset for phenological analysis in this dryland forest ecosystem. Overall, this work demonstrates the feasibility of using data fusion products to assemble an imagery dataset at sufficiently high temporal and spatial scales to permit a more detailed examination of the underlying phenological processes and trends in dryland forest areas.

Experimental feasibility of multistatic holography for breast microwave radar image reconstruction.

PubMed

Flores-Tapia, Daniel; Rodriguez, Diego; Solis, Mario; Kopotun, Nikita; Latif, Saeed; Maizlish, Oleksandr; Fu, Lei; Gui, Yonsheng; Hu, Can-Ming; Pistorius, Stephen

2016-08-01

The goal of this study was to assess the experimental feasibility of circular multistatic holography, a novel breast microwave radar reconstruction approach, using experimental datasets recorded using a preclinical experimental setup. The performance of this approach was quantitatively evaluated by calculating the signal to clutter ratio (SCR), contrast to clutter ratio (CCR), tumor to fibroglandular response ratio (TFRR), spatial accuracy, and reconstruction time. Five datasets were recorded using synthetic phantoms with the dielectric properties of breast tissue in the 1-6 GHz range using a custom radar system developed by the authors. The datasets contained synthetic structures that mimic the dielectric properties of fibroglandular breast tissues. Four of these datasets the authors covered an 8 mm inclusion that emulated a tumor. A custom microwave radar system developed at the University of Manitoba was used to record the radar responses from the phantoms. The datasets were reconstructed using the proposed multistatic approach as well as with a monostatic holography approach that has been previously shown to yield the images with the highest contrast and focal quality. For all reconstructions, the location of the synthetic tumors in the experimental setup was consistent with the position in the both the monostatic and multistatic reconstructed images. The average spatial error was less than 4 mm, which is half the spatial resolution of the data acquisition system. The average SCR, CCR, and TFRR of the images reconstructed with the multistatic approach were 15.0, 9.4, and 10.0 dB, respectively. In comparison, monostatic images obtained using the datasets from the same experimental setups yielded average SCR, CCR, and TFRR values of 12.8, 4.9, and 5.9 dB. No artifacts, defined as responses generated by the reconstruction method of at least half the energy of the tumor signatures, were noted in the multistatic reconstructions. The average execution time of the images formed using the proposed approach was 4 s, which is one order of magnitude faster than the current state-of-the-art time-domain multistatic breast microwave radar reconstruction algorithms. The images generated by the proposed method show that multistatic holography is capable of forming spatially accurate images in real-time with signal to clutter levels and contrast values higher than other published monostatic and multistatic cylindrical radar reconstruction approaches. In comparison to the monostatic holographic approach, the images generated by the proposed multistatic approach had SCR values that were at least 50% higher. The multistatic images had CCR and TFRR values at least 200% greater than those formed using a monostatic approach.

A Bioacoustic Record of a Conservancy in the Mount Kenya Ecosystem

PubMed Central

Muchiri, David; Njoroge, Peter

2016-01-01

Abstract Background Environmental degradation is a major threat facing ecosystems around the world. In order to determine ecosystems in need of conservation interventions, we must monitor the biodiversity of these ecosystems effectively. Bioacoustic approaches offer a means to monitor ecosystems of interest in a sustainable manner. In this work we show how a bioacoustic record from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, was obtained in a cost effective manner. A subset of the dataset was annotated with the identities of bird species present since they serve as useful indicator species. These data reveal the spatial distribution of species within the conservancy and also point to the effects of major highways on bird populations. This dataset will provide data to train automatic species recognition systems for birds found within the Mount Kenya ecosystem. Such systems are necessary if bioacoustic approaches are to be employed at the large scales necessary to influence wildlife conservation measures. New information We provide acoustic recordings from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, obtained using a low cost acoustic recorder. A total of 2701 minute long recordings are provided including both daytime and nighttime recordings. We present an annotation of a subset of the daytime recordings indicating the bird species present in the recordings. The dataset contains recordings of at least 36 bird species. In addition, the presence of a few nocturnal species within the conservancy is also confirmed. PMID:27932917

A Bioacoustic Record of a Conservancy in the Mount Kenya Ecosystem.

PubMed

Wa Maina, Ciira; Muchiri, David; Njoroge, Peter

2016-01-01

Environmental degradation is a major threat facing ecosystems around the world. In order to determine ecosystems in need of conservation interventions, we must monitor the biodiversity of these ecosystems effectively. Bioacoustic approaches offer a means to monitor ecosystems of interest in a sustainable manner. In this work we show how a bioacoustic record from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, was obtained in a cost effective manner. A subset of the dataset was annotated with the identities of bird species present since they serve as useful indicator species. These data reveal the spatial distribution of species within the conservancy and also point to the effects of major highways on bird populations. This dataset will provide data to train automatic species recognition systems for birds found within the Mount Kenya ecosystem. Such systems are necessary if bioacoustic approaches are to be employed at the large scales necessary to influence wildlife conservation measures. We provide acoustic recordings from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, obtained using a low cost acoustic recorder. A total of 2701 minute long recordings are provided including both daytime and nighttime recordings. We present an annotation of a subset of the daytime recordings indicating the bird species present in the recordings. The dataset contains recordings of at least 36 bird species. In addition, the presence of a few nocturnal species within the conservancy is also confirmed.

Using classical population genetics tools with heterochroneous data: time matters!

PubMed

Depaulis, Frantz; Orlando, Ludovic; Hänni, Catherine

2009-01-01

New polymorphism datasets from heterochroneous data have arisen thanks to recent advances in experimental and microbial molecular evolution, and the sequencing of ancient DNA (aDNA). However, classical tools for population genetics analyses do not take into account heterochrony between subsets, despite potential bias on neutrality and population structure tests. Here, we characterize the extent of such possible biases using serial coalescent simulations. We first use a coalescent framework to generate datasets assuming no or different levels of heterochrony and contrast most classical population genetic statistics. We show that even weak levels of heterochrony ( approximately 10% of the average depth of a standard population tree) affect the distribution of polymorphism substantially, leading to overestimate the level of polymorphism theta, to star like trees, with an excess of rare mutations and a deficit of linkage disequilibrium, which are the hallmark of e.g. population expansion (possibly after a drastic bottleneck). Substantial departures of the tests are detected in the opposite direction for more heterochroneous and equilibrated datasets, with balanced trees mimicking in particular population contraction, balancing selection, and population differentiation. We therefore introduce simple corrections to classical estimators of polymorphism and of the genetic distance between populations, in order to remove heterochrony-driven bias. Finally, we show that these effects do occur on real aDNA datasets, taking advantage of the currently available sequence data for Cave Bears (Ursus spelaeus), for which large mtDNA haplotypes have been reported over a substantial time period (22-130 thousand years ago (KYA)). Considering serial sampling changed the conclusion of several tests, indicating that neglecting heterochrony could provide significant support for false past history of populations and inappropriate conservation decisions. We therefore argue for systematically considering heterochroneous models when analyzing heterochroneous samples covering a large time scale.

Calibrating a numerical model's morphology using high-resolution spatial and temporal datasets from multithread channel flume experiments.

NASA Astrophysics Data System (ADS)

Javernick, L.; Bertoldi, W.; Redolfi, M.

2017-12-01

Accessing or acquiring high quality, low-cost topographic data has never been easier due to recent developments of the photogrammetric techniques of Structure-from-Motion (SfM). Researchers can acquire the necessary SfM imagery with various platforms, with the ability to capture millimetre resolution and accuracy, or large-scale areas with the help of unmanned platforms. Such datasets in combination with numerical modelling have opened up new opportunities to study river environments physical and ecological relationships. While numerical models overall predictive accuracy is most influenced by topography, proper model calibration requires hydraulic data and morphological data; however, rich hydraulic and morphological datasets remain scarce. This lack in field and laboratory data has limited model advancement through the inability to properly calibrate, assess sensitivity, and validate the models performance. However, new time-lapse imagery techniques have shown success in identifying instantaneous sediment transport in flume experiments and their ability to improve hydraulic model calibration. With new capabilities to capture high resolution spatial and temporal datasets of flume experiments, there is a need to further assess model performance. To address this demand, this research used braided river flume experiments and captured time-lapse observed sediment transport and repeat SfM elevation surveys to provide unprecedented spatial and temporal datasets. Through newly created metrics that quantified observed and modeled activation, deactivation, and bank erosion rates, the numerical model Delft3d was calibrated. This increased temporal data of both high-resolution time series and long-term temporal coverage provided significantly improved calibration routines that refined calibration parameterization. Model results show that there is a trade-off between achieving quantitative statistical and qualitative morphological representations. Specifically, statistical agreement simulations suffered to represent braiding planforms (evolving toward meandering), and parameterization that ensured braided produced exaggerated activation and bank erosion rates. Marie Sklodowska-Curie Individual Fellowship: River-HMV, 656917

EnviroAtlas - Percentage of Working Age Population Who Are Employed by Block Group for the Conterminous United States

EPA Pesticide Factsheets

This EnviroAtlas dataset shows the employment rate, or the percent of the population aged 16-64 who have worked in the past 12 months. The employment rate is a measure of the percent of the working-age population who are employed. It is an indicator of the prevalence of unemployment, which is often used to assess labor market conditions by economists. It is a widely used metric to evaluate the sustainable development of communities (NRC, 2011, UNECE, 2009). This dataset is based on the American Community Survey 5-year data for 2008-2012. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Spatial and Temporal Mapping of the Evolution of the Miami-Fort Lauderdale-West Palm Beach Metropolitan Statistical Area (MSA)

NASA Astrophysics Data System (ADS)

Rochelo, Mark

Urbanization is a fundamental reality in the developed and developing countries around the world creating large concentrations of the population centering on cities and urban centers. Cities can offer many opportunities for those residing there, including infrastructure, health services, rescue services and more. The living space density of cities allows for the opportunity of more effective and environmentally friendly housing, transportation and resources. Cities play a vital role in generating economic production as entities by themselves and as a part of larger urban complex. The benefits can provide for extraordinary amount of people, but only if proper planning and consideration is undertaken. Global urbanization is a progressive evolution, unique in spatial location while consistent to an overall growth pattern and trend. Remotely sensing these patterns from the last forty years of space borne satellites to understand how urbanization has developed is important to understanding past growth as well as planning for the future. Imagery from the Landsat sensor program provides the temporal component, it was the first satellite launched in 1972, providing appropriate spatial resolution needed to cover a large metropolitan statistical area to monitor urban growth and change on a large scale. This research maps the urban spatial and population growth over the Miami - Fort Lauderdale - West Palm Beach Metropolitan Statistical Area (MSA) covering Miami-Dade, Broward, and Palm Beach counties in Southeast Florida from 1974 to 2010 using Landsat imagery. Supervised Maximum Likelihood classification was performed with a combination of spectral and textural training fields employed in ERDAS Image 2014 to classify the images into urban and non-urban areas. Dasymetric mapping of the classification results were combined with census tract data then created a coherent depiction of the Miami - Fort Lauderdale - West Palm Beach MSA. Static maps and animated files were created from the final datasets for enhanced visualizations and understanding of the MSA evolution from 60-meter resolution remotely sensed Landsat images. The simplified methodology will create a database for urban planning and population growth as well as future work in this area.

Correction of elevation offsets in multiple co-located lidar datasets

USGS Publications Warehouse

Thompson, David M.; Dalyander, P. Soupy; Long, Joseph W.; Plant, Nathaniel G.

2017-04-07

IntroductionTopographic elevation data collected with airborne light detection and ranging (lidar) can be used to analyze short- and long-term changes to beach and dune systems. Analysis of multiple lidar datasets at Dauphin Island, Alabama, revealed systematic, island-wide elevation differences on the order of 10s of centimeters (cm) that were not attributable to real-world change and, therefore, were likely to represent systematic sampling offsets. These offsets vary between the datasets, but appear spatially consistent within a given survey. This report describes a method that was developed to identify and correct offsets between lidar datasets collected over the same site at different times so that true elevation changes over time, associated with sediment accumulation or erosion, can be analyzed.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Spatial patterns and broad-scale weather cues of beech mast seeding in Europe.

PubMed

Vacchiano, Giorgio; Hacket-Pain, Andrew; Turco, Marco; Motta, Renzo; Maringer, Janet; Conedera, Marco; Drobyshev, Igor; Ascoli, Davide

2017-07-01

Mast seeding is a crucial population process in many tree species, but its spatio-temporal patterns and drivers at the continental scale remain unknown . Using a large dataset (8000 masting observations across Europe for years 1950-2014) we analysed the spatial pattern of masting across the entire geographical range of European beech, how it is influenced by precipitation, temperature and drought, and the temporal and spatial stability of masting-weather correlations. Beech masting exhibited a general distance-dependent synchronicity and a pattern structured in three broad geographical groups consistent with continental climate regimes. Spearman's correlations and logistic regression revealed a general pattern of beech masting correlating negatively with temperature in the summer 2 yr before masting, and positively with summer temperature 1 yr before masting (i.e. 2T model). The temperature difference between the two previous summers (DeltaT model) was also a good predictor. Moving correlation analysis applied to the longest eight chronologies (74-114 yr) revealed stable correlations between temperature and masting, confirming consistency in weather cues across space and time. These results confirm widespread dependency of masting on temperature and lend robustness to the attempts to reconstruct and predict mast years using temperature data. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

Assessing the phylogeographic history of the montane caddisfly Thremma gallicum using mitochondrial and restriction-site-associated DNA (RAD) markers

PubMed Central

Macher, Jan-Niklas; Rozenberg, Andrey; Pauls, Steffen U; Tollrian, Ralph; Wagner, Rüdiger; Leese, Florian

2015-01-01

Repeated Quaternary glaciations have significantly shaped the present distribution and diversity of several European species in aquatic and terrestrial habitats. To study the phylogeography of freshwater invertebrates, patterns of intraspecific variation have been examined primarily using mitochondrial DNA markers that may yield results unrepresentative of the true species history. Here, population genetic parameters were inferred for a montane aquatic caddisfly, Thremma gallicum, by sequencing a 658-bp fragment of the mitochondrial CO1 gene, and 12,514 nuclear RAD loci. T. gallicum has a highly disjunct distribution in southern and central Europe, with known populations in the Cantabrian Mountains, Pyrenees, Massif Central, and Black Forest. Both datasets represented rangewide sampling of T. gallicum. For the CO1 dataset, this included 352 specimens from 26 populations, and for the RAD dataset, 17 specimens from eight populations. We tested 20 competing phylogeographic scenarios using approximate Bayesian computation (ABC) and estimated genetic diversity patterns. Support for phylogeographic scenarios and diversity estimates differed between datasets with the RAD data favouring a southern origin of extant populations and indicating the Cantabrian Mountains and Massif Central populations to represent highly diverse populations as compared with the Pyrenees and Black Forest populations. The CO1 data supported a vicariance scenario (north–south) and yielded inconsistent diversity estimates. Permutation tests suggest that a few hundred polymorphic RAD SNPs are necessary for reliable parameter estimates. Our results highlight the potential of RAD and ABC-based hypothesis testing to complement phylogeographic studies on non-model species. PMID:25691988

European Neolithic societies showed early warning signals of population collapse.

PubMed

Downey, Sean S; Haas, W Randall; Shennan, Stephen J

2016-08-30

Ecosystems on the verge of major reorganization-regime shift-may exhibit declining resilience, which can be detected using a collection of generic statistical tests known as early warning signals (EWSs). This study explores whether EWSs anticipated human population collapse during the European Neolithic. It analyzes recent reconstructions of European Neolithic (8-4 kya) population trends that reveal regime shifts from a period of rapid growth following the introduction of agriculture to a period of instability and collapse. We find statistical support for EWSs in advance of population collapse. Seven of nine regional datasets exhibit increasing autocorrelation and variance leading up to collapse, suggesting that these societies began to recover from perturbation more slowly as resilience declined. We derive EWS statistics from a prehistoric population proxy based on summed archaeological radiocarbon date probability densities. We use simulation to validate our methods and show that sampling biases, atmospheric effects, radiocarbon calibration error, and taphonomic processes are unlikely to explain the observed EWS patterns. The implications of these results for understanding the dynamics of Neolithic ecosystems are discussed, and we present a general framework for analyzing societal regime shifts using EWS at large spatial and temporal scales. We suggest that our findings are consistent with an adaptive cycling model that highlights both the vulnerability and resilience of early European populations. We close by discussing the implications of the detection of EWS in human systems for archaeology and sustainability science.

Characterizing the spatial distribution of ambient ultrafine particles in Toronto, Canada: A land use regression model.

PubMed

Weichenthal, Scott; Van Ryswyk, Keith; Goldstein, Alon; Shekarrizfard, Maryam; Hatzopoulou, Marianne

2016-01-01

Exposure models are needed to evaluate the chronic health effects of ambient ultrafine particles (<0.1 μm) (UFPs). We developed a land use regression model for ambient UFPs in Toronto, Canada using mobile monitoring data collected during summer/winter 2010-2011. In total, 405 road segments were included in the analysis. The final model explained 67% of the spatial variation in mean UFPs and included terms for the logarithm of distances to highways, major roads, the central business district, Pearson airport, and bus routes as well as variables for the number of on-street trees, parks, open space, and the length of bus routes within a 100 m buffer. There was no systematic difference between measured and predicted values when the model was evaluated in an external dataset, although the R(2) value decreased (R(2) = 50%). This model will be used to evaluate the chronic health effects of UFPs using population-based cohorts in the Toronto area. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Global Priorities for Marine Biodiversity Conservation

PubMed Central

Selig, Elizabeth R.; Turner, Will R.; Troëng, Sebastian; Wallace, Bryan P.; Halpern, Benjamin S.; Kaschner, Kristin; Lascelles, Ben G.; Carpenter, Kent E.; Mittermeier, Russell A.

2014-01-01

In recent decades, many marine populations have experienced major declines in abundance, but we still know little about where management interventions may help protect the highest levels of marine biodiversity. We used modeled spatial distribution data for nearly 12,500 species to quantify global patterns of species richness and two measures of endemism. By combining these data with spatial information on cumulative human impacts, we identified priority areas where marine biodiversity is most and least impacted by human activities, both within Exclusive Economic Zones (EEZs) and Areas Beyond National Jurisdiction (ABNJ). Our analyses highlighted places that are both accepted priorities for marine conservation like the Coral Triangle, as well as less well-known locations in the southwest Indian Ocean, western Pacific Ocean, Arctic and Antarctic Oceans, and within semi-enclosed seas like the Mediterranean and Baltic Seas. Within highly impacted priority areas, climate and fishing were the biggest stressors. Although new priorities may arise as we continue to improve marine species range datasets, results from this work are an essential first step in guiding limited resources to regions where investment could best sustain marine biodiversity. PMID:24416151

Characterization of mesoscale convective systems over the eastern Pacific during boreal summer

NASA Astrophysics Data System (ADS)

Berthet, Sarah; Rouquié, Bastien; Roca, Rémy

2015-04-01

The eastern Pacific Ocean is one of the most active tropical disturbances formation regions on earth. This preliminary study is part of a broader project that aims to investigate how mesoscale convective systems (MCS) may be related to these synoptic disturbances with emphasis on local initiation of tropical depressions. As a first step, the main characteristics of the MCS over the eastern Pacific are documented with the help of the recently developed TOOCAN tracking algorithm (Fiolleau and Roca, 2013) applied to the infrared satellite imagery data from GOES-W and -E for the period JJAS 2012-2014. More specifically, the spatial distribution of the MCS population, the statistics of their spatial extensions and durations, as well as their trajectories and propagation speeds are summarized. In addition the environment of the MCS will be investigated using various Global Precipitation Mission datasets and the Megha-Tropiques/SAPHIR humidity microwave sounder derived products. Reference: Fiolleau T. and R. Roca, (2013), An Algorithm For The Detection And Tracking Of Tropical Mesoscale Convective Systems Using Infrared Images From Geostationary Satellite, Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2012.2227762.

Global priorities for marine biodiversity conservation.

PubMed

Selig, Elizabeth R; Turner, Will R; Troëng, Sebastian; Wallace, Bryan P; Halpern, Benjamin S; Kaschner, Kristin; Lascelles, Ben G; Carpenter, Kent E; Mittermeier, Russell A

2014-01-01

In recent decades, many marine populations have experienced major declines in abundance, but we still know little about where management interventions may help protect the highest levels of marine biodiversity. We used modeled spatial distribution data for nearly 12,500 species to quantify global patterns of species richness and two measures of endemism. By combining these data with spatial information on cumulative human impacts, we identified priority areas where marine biodiversity is most and least impacted by human activities, both within Exclusive Economic Zones (EEZs) and Areas Beyond National Jurisdiction (ABNJ). Our analyses highlighted places that are both accepted priorities for marine conservation like the Coral Triangle, as well as less well-known locations in the southwest Indian Ocean, western Pacific Ocean, Arctic and Antarctic Oceans, and within semi-enclosed seas like the Mediterranean and Baltic Seas. Within highly impacted priority areas, climate and fishing were the biggest stressors. Although new priorities may arise as we continue to improve marine species range datasets, results from this work are an essential first step in guiding limited resources to regions where investment could best sustain marine biodiversity.

Factors Controlling Vegetation Fires in Protected and Non-Protected Areas of Myanmar

PubMed Central

Biswas, Sumalika; Vadrevu, Krishna Prasad; Lwin, Zin Mar; Lasko, Kristofer; Justice, Christopher O.

2015-01-01

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar. PMID:25909632

Factors controlling vegetation fires in protected and non-protected areas of myanmar.

PubMed

Biswas, Sumalika; Vadrevu, Krishna Prasad; Lwin, Zin Mar; Lasko, Kristofer; Justice, Christopher O

2015-01-01

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.

Wolves Recolonizing Islands: Genetic Consequences and Implications for Conservation and Management

PubMed Central

Remm, Jaanus; Hindrikson, Maris; Jõgisalu, Inga; Männil, Peep; Kübarsepp, Marko; Saarma, Urmas

2016-01-01

After a long and deliberate persecution, the grey wolf (Canis lupus) is slowly recolonizing its former areas in Europe, and the genetic consequences of this process are of particular interest. Wolves, though present in mainland Estonia for a long time, have only recently started to recolonize the country’s two largest islands, Saaremaa and Hiiumaa. The main objective of this study was to analyse wolf population structure and processes in Estonia, with particular attention to the recolonization of islands. Fifteen microsatellite loci were genotyped for 185 individuals across Estonia. As a methodological novelty, all putative wolf-dog hybrids were identified and removed (n = 17) from the dataset beforehand to avoid interference of dog alleles in wolf population analysis. After the preliminary filtering, our final dataset comprised of 168 “pure” wolves. We recommend using hybrid-removal step as a standard precautionary procedure not only for wolf population studies, but also for other taxa prone to hybridization. STRUCTURE indicated four genetic groups in Estonia. Spatially explicit DResD analysis identified two areas, one of them on Saaremaa island and the other in southwestern Estonia, where neighbouring individuals were genetically more similar than expected from an isolation-by-distance null model. Three blending areas and two contrasting transition zones were identified in central Estonia, where the sampled individuals exhibited strong local differentiation over relatively short distance. Wolves on the largest Estonian islands are part of human-wildlife conflict due to livestock depredation. Negative public attitude, especially on Saaremaa where sheep herding is widespread, poses a significant threat for island wolves. To maintain the long-term viability of the wolf population on Estonian islands, not only wolf hunting quota should be targeted with extreme care, but effective measures should be applied to avoid inbreeding and minimize conflicts with local communities and stakeholders. PMID:27384049

Domestic well locations and populations served in the contiguous U.S.: 1990

USGS Publications Warehouse

Johnson, Tyler; Belitz, Kenneth

2017-01-01

We estimate the location and population served by domestic wells in the contiguous United States in two ways: (1) the “Block Group Method” or BGM, uses data from the 1990 census, and (2) the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally-consistent domestic-well population datasets.While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map has a smaller amount of spatial bias (Type 1 and Type 2 errors nearly equal vs biased in Type 1), total error (10.9% vs 23.7%), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a calibration map in California. However, the BGM map is more inclusive of all potential locations for domestic wells. Independent domestic well datasets from the USGS, and the States of MN, NV, and TX show that the BGM captures about 5 to 10% more wells than the REM.One key difference between the BGM and the REM is the mapping of low density areas. The REM reduces areas mapped as low density by 57%, concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used.

Spatio-Temporal Synchrony of Influenza in Cities across Israel: The “Israel Is One City” Hypothesis

PubMed Central

Barnea, Oren; Huppert, Amit; Katriel, Guy; Stone, Lewi

2014-01-01

We analysed an 11-year dataset (1998–2009) of Influenza-Like Illness (ILI) that was based on surveillance of ∽23% of Israel's population. We examined whether the level of synchrony of ILI epidemics in Israel's 12 largest cities is high enough to view Israel as a single epidemiological unit. Two methods were developed to assess the synchrony: (1) City-specific attack rates were fitted to a simple model in order to estimate the temporal differences in attack rates and spatial differences in reporting rates of ILI. The model showed good fit to the data (R2  =  0.76) and revealed considerable differences in reporting rates of ILI in different cities (up to a factor of 2.2). (2) A statistical test was developed to examine the null hypothesis (H0) that ILI incidence curves in two cities are essentially identical, and was tested using ILI data. Upon examining all possible pairs of incidence curves, 77.4% of pairs were found not to be different (H0 was not rejected). It was concluded that all cities generally have the same attack rate and follow the same epidemic curve each season, although the attack rate changes from season to season, providing strong support for the “Israel is one city” hypothesis. The cities which were the most out of synchronization were Bnei Brak, Beersheba and Haifa, the latter two being geographically remote from all other cities in the dataset and the former geographically very close to several other cities but socially separate due to being populated almost exclusively by ultra-orthodox Jews. Further evidence of assortative mixing of the ultra-orthodox population can be found in the 2001–2002 season, when ultra-orthodox cities and neighborhoods showed distinctly different incidence curves compared to the general population. PMID:24622820

Earth-Science Data Co-Locating Tool

NASA Technical Reports Server (NTRS)

Lee, Seungwon; Pan, Lei; Block, Gary L.

2012-01-01

This software is used to locate Earth-science satellite data and climate-model analysis outputs in space and time. This enables the direct comparison of any set of data with different spatial and temporal resolutions. It is written in three separate modules that are clearly separated for their functionality and interface with other modules. This enables a fast development of supporting any new data set. In this updated version of the tool, several new front ends are developed for new products. This software finds co-locatable data pairs for given sets of data products and creates new data products that share the same spatial and temporal coordinates. This facilitates the direct comparison between the two heterogeneous datasets and the comprehensive and synergistic use of the datasets.

A Web-based graphical user interface for evidence-based decision making for health care allocations in rural areas

PubMed Central

Schuurman, Nadine; Leight, Margo; Berube, Myriam

2008-01-01

Background The creation of successful health policy and location of resources increasingly relies on evidence-based decision-making. The development of intuitive, accessible tools to analyse, display and disseminate spatial data potentially provides the basis for sound policy and resource allocation decisions. As health services are rationalized, the development of tools such graphical user interfaces (GUIs) is especially valuable at they assist decision makers in allocating resources such that the maximum number of people are served. GIS can used to develop GUIs that enable spatial decision making. Results We have created a Web-based GUI (wGUI) to assist health policy makers and administrators in the Canadian province of British Columbia make well-informed decisions about the location and allocation of time-sensitive service capacities in rural regions of the province. This tool integrates datasets for existing hospitals and services, regional populations and road networks to allow users to ascertain the percentage of population in any given service catchment who are served by a specific health service, or baskets of linked services. The wGUI allows policy makers to map trauma and obstetric services against rural populations within pre-specified travel distances, illustrating service capacity by region. Conclusion The wGUI can be used by health policy makers and administrators with little or no formal GIS training to visualize multiple health resource allocation scenarios. The GUI is poised to become a critical decision-making tool especially as evidence is increasingly required for distribution of health services. PMID:18793428

Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

USGS Publications Warehouse

Ji, Lei; Senay, Gabriel B.; Verdin, James P.

2015-01-01

There is a high demand for agrohydrologic models to use gridded near-surface air temperature data as the model input for estimating regional and global water budgets and cycles. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global scale. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, the daily 0.25° resolution GLDAS air temperature data are compared with two reference datasets: 1) 1-km-resolution gridded Daymet data (2002 and 2010) for the conterminous United States and 2) global meteorological observations (2000–11) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets, including 13 511 weather stations, indicates a fairly high accuracy of the GLDAS data for daily temperature. The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accuracy. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. The evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but caution should be taken when the data are used in mountainous areas or places with sparse weather stations.

A Large-Scale, High-Resolution Hydrological Model Parameter Data Set for Climate Change Impact Assessment for the Conterminous US

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oubeidillah, Abdoul A; Kao, Shih-Chieh; Ashfaq, Moetasim

2014-01-01

To extend geographical coverage, refine spatial resolution, and improve modeling efficiency, a computation- and data-intensive effort was conducted to organize a comprehensive hydrologic dataset with post-calibrated model parameters for hydro-climate impact assessment. Several key inputs for hydrologic simulation including meteorologic forcings, soil, land class, vegetation, and elevation were collected from multiple best-available data sources and organized for 2107 hydrologic subbasins (8-digit hydrologic units, HUC8s) in the conterminous United States at refined 1/24 (~4 km) spatial resolution. Using high-performance computing for intensive model calibration, a high-resolution parameter dataset was prepared for the macro-scale Variable Infiltration Capacity (VIC) hydrologic model. The VICmore » simulation was driven by DAYMET daily meteorological forcing and was calibrated against USGS WaterWatch monthly runoff observations for each HUC8. The results showed that this new parameter dataset may help reasonably simulate runoff at most US HUC8 subbasins. Based on this exhaustive calibration effort, it is now possible to accurately estimate the resources required for further model improvement across the entire conterminous United States. We anticipate that through this hydrologic parameter dataset, the repeated effort of fundamental data processing can be lessened, so that research efforts can emphasize the more challenging task of assessing climate change impacts. The pre-organized model parameter dataset will be provided to interested parties to support further hydro-climate impact assessment.« less

Application of crowd-sourced data to multi-scale evolutionary exposure and vulnerability models

NASA Astrophysics Data System (ADS)

Pittore, Massimiliano

2016-04-01

Seismic exposure, defined as the assets (population, buildings, infrastructure) exposed to earthquake hazard and susceptible to damage, is a critical -but often neglected- component of seismic risk assessment. This partly stems from the burden associated with the compilation of a useful and reliable model over wide spatial areas. While detailed engineering data have still to be collected in order to constrain exposure and vulnerability models, the availability of increasingly large crowd-sourced datasets (e. g. OpenStreetMap) opens up the exciting possibility to generate incrementally evolving models. Integrating crowd-sourced and authoritative data using statistical learning methodologies can reduce models uncertainties and also provide additional drive and motivation to volunteered geoinformation collection. A case study in Central Asia will be presented and discussed.

Spatial Analysis of Geohazards using ArcGIS--A web-based Course.

NASA Astrophysics Data System (ADS)

Harbert, W.; Davis, D.

2003-12-01

As part of the Environmental Systems Research Incorporated (ESRI) Virtual Campus program, a course was designed to present the benefits of Geographical Information Systems (GIS) based spatial analysis as applied towards a variety of geohazards. We created this on-line ArcGIS 8.x-based course to aid the motivated student or professional in his or her efforts to use GIS in determining where geohazards are likely to occur and for assessing their potential impact on the human community. Our course is broadly designed for earth scientists, public sector professionals, students, and others who want to apply GIS to the study of geohazards. Participants work with ArcGIS software and diverse datasets to display, visualize and analyze a wide variety of data sets and map a variety of geohazards including earthquakes, volcanoes, landslides, tsunamis, and floods. Following the GIS-based methodology of posing a question, decomposing the question into specific criteria, applying the criteria to spatial or tabular geodatasets and then analyzing feature relationships, from the beginning the course content was designed in order to enable the motivated student to answer questions. For example, to explain the relationship between earth quake location, earthquake depth, and plate boundaries; use a seismic hazard map to identify population and features at risk from an earthquake; import data from an earthquake catalog and visualize these data in 3D; explain the relationship between earthquake damage and local geology; use a flood scenario map to identify features at risk for forecast river discharges; use a tsunami inundation map to identify population and features at risk from tsunami; use a hurricane inundation map to identify the population at risk for any given category hurricane; estimate accumulated precipitation by integrating time-series Doppler radar data; and model a real-life landslide event. The six on-line modules for our course are Earthquakes I, Earthquakes II, Volcanoes, Floods, Coastal Geohazards and Landslides. Earthquake I can be viewed and accessed for no cost at http://campus.esri.com.

Spatial assessment of land degradation through key ecosystem services: The role of globally available data.

PubMed

Cerretelli, Stefania; Poggio, Laura; Gimona, Alessandro; Yakob, Getahun; Boke, Shiferaw; Habte, Mulugeta; Coull, Malcolm; Peressotti, Alessandro; Black, Helaina

2018-07-01

Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making. Copyright © 2018. Published by Elsevier B.V.

National Geospatial Data Asset Lifecycle Baseline Maturity Assessment for the Federal Geographic Data Committee

NASA Astrophysics Data System (ADS)

Peltz-Lewis, L. A.; Blake-Coleman, W.; Johnston, J.; DeLoatch, I. B.

2014-12-01

The Federal Geographic Data Committee (FGDC) is designing a portfolio management process for 193 geospatial datasets contained within the 16 topical National Spatial Data Infrastructure themes managed under OMB Circular A-16 "Coordination of Geographic Information and Related Spatial Data Activities." The 193 datasets are designated as National Geospatial Data Assets (NGDA) because of their significance in implementing to the missions of multiple levels of government, partners and stakeholders. As a starting point, the data managers of these NGDAs will conduct a baseline maturity assessment of the dataset(s) for which they are responsible. The maturity is measured against benchmarks related to each of the seven stages of the data lifecycle management framework promulgated within the OMB Circular A-16 Supplemental Guidance issued by OMB in November 2010. This framework was developed by the interagency Lifecycle Management Work Group (LMWG), consisting of 16 Federal agencies, under the 2004 Presidential Initiative the Geospatial Line of Business,using OMB Circular A-130" Management of Federal Information Resources" as guidance The seven lifecycle stages are: Define, Inventory/Evaluate, Obtain, Access, Maintain, Use/Evaluate, and Archive. This paper will focus on the Lifecycle Baseline Maturity Assessment, and efforts to integration the FGDC approach with other data maturity assessments.

Evaluating the High Asia Reanalysis (HAR) using Gauge-based and Satellite Precipitation Data over High Mountain Asia

NASA Astrophysics Data System (ADS)

Pangaluru, K.; Velicogna, I.; Ciraci, E.; Mohajerani, Y.

2017-12-01

The Indus, Ganges and Brahmaputra (IGB) basins supply water for both domestic and agricultural demands, the latter of which is the mainstay of Indian economy. Here, we use high-resolution Asia Refined Analysis (HAR) rainfall datasets to study the spatial and temporal behavior of rainfall over the mountainous areas of the Indus, Ganges and Brahmaputra (IGB) over the period from 2001 to 2014. The validation of High Asia Refined Analysis (HAR) precipitation data is carried out with observational (GPCP, CRU and CPC) and satellite (TRMM_3B43) datasets for the period. We find that the relative differences between the HAR model and the satellite and gauge-based datasets varies between -9% and 67% for the seasonal mean and between 1% and 26% for the annual mean for all basins. The correlation between the HAR model and the observational datasets lies between 0.5 and 0.9 for all seasons. Spatial variations and monthly magnitudes of gridded precipitation trends are calculated by using the Mann-Kendall (MK) test and the Thei-Sen approach (TSA) respectively. We found significant positive trends precipitation grids over the IGB basins in the annual and monsoon season time frames, as opposed to winter and falls seasons.

Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents

USGS Publications Warehouse

Caritat, Patrice de; Reimann, Clemens; Smith, David; Wang, Xueqiu

2017-01-01

During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality-controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents.

Internet use and health: Connecting secondary data through spatial microsimulation

PubMed Central

Deetjen, Ulrike; Powell, John A

2016-01-01

Objective Internet use may affect health and health service use, and is seen as a potential lever for empowering patients, levelling inequalities and managing costs in the health system. However, supporting evidence is scant, partially due to a lack of data to investigate the relationship on a larger scale. This paper presents an approach for connecting existing datasets to generate new insights. Methods Spatial microsimulation offers a way to combine a random sample survey on Internet use with aggregate census data and other routine data from the health system based on small geographic areas to examine the relationship between Internet use, perceived health and health service use. While health research has primarily used spatial microsimulation to estimate the geographic distribution of a certain phenomenon, this research highlights this simulation technique as a way to link datasets for joint analysis, with location as the connecting element. Results Internet use is associated with higher perceived health and lower health service use independently of whether Internet use was conceptualised in terms of access, support or usage, and controlling for sociodemographic covariates. Internal validation confirms that differences between actual and simulated data are small; external validation shows that the simulated dataset is a good reflection of the real world. Conclusion Spatial microsimulation helps to generate new insights through linking secondary data in a privacy-preserving and cost-effective way. This allows for better understanding the relationship between Internet use and health, enabling theoretical insights and practical implications for policy with insights down to the local level. PMID:29942566

Spatially Referenced Educational Achievement Data Exploration: A Web-Based Interactive System Integration of GIS, PHP, and MySQL Technologies

ERIC Educational Resources Information Center

Mulvenon, Sean W.; Wang, Kening; Mckenzie, Sarah; Anderson, Travis

2006-01-01

Effective exploration of spatially referenced educational achievement data can help educational researchers and policy analysts speed up gaining valuable insight into datasets. This article illustrates a demo system developed in the National Office for Research on Measurement and Evaluation Systems (NORMES) for supporting Web-based interactive…

76 FR 34656 - Fisheries of the Caribbean; Southeast Data, Assessment, and Review (SEDAR); Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-14

... Workshop. The product of the Data Workshop is a data report which compiles and evaluates potential datasets and recommends which datasets are appropriate for assessment analyses. The product of the Stock....m. Using datasets provided by the Data Workshop, participants will develop population models to...

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

DOE PAGES

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.; ...

2017-09-14

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.

Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. Nevertheless, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse-spatial-resolution datasets used in land surface modeling pose a new challenge in simulating the spatially distributed and basin-integrated processes since these datasets do not adequately represent the small-scale hydrological, thermal, and ecological heterogeneity. The goal of this study is to improve the prediction capacity of mesoscale to large-scale hydrological models by introducing a small-scale parameterization scheme, which bettermore » represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and a sub-grid parameterization method in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) sub-basin and one permafrost-dominated (HighP) sub-basin. The sub-grid parameterization method used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the sub-grid parameterization method than the coarse-resolution datasets. Parameters derived from the coarse-resolution datasets and from the sub-grid parameterization method are implemented into the variable infiltration capacity (VIC) mesoscale hydrological model to simulate runoff, evapotranspiration (ET), and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows, with similar accuracy in both sub-basins, compared to simulated hydrographs based on the coarse-resolution datasets. On average, the small-scale parameterization scheme improves the total runoff simulation by up to 50 % in the LowP sub-basin and by up to 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed sub-grid parameterization method can be used to improve the performance of mesoscale hydrological models in the Alaskan sub-arctic watersheds.« less

A downscaled 1 km dataset of daily Greenland ice sheet surface mass balance components (1958-2014)

NASA Astrophysics Data System (ADS)

Noel, B.; Van De Berg, W. J.; Fettweis, X.; Machguth, H.; Howat, I. M.; van den Broeke, M. R.

2015-12-01

The current spatial resolution in regional climate models (RCMs), typically around 5 to 20 km, remains too coarse to accurately reproduce the spatial variability in surface mass balance (SMB) components over the narrow ablation zones, marginal outlet glaciers and neighbouring ice caps of the Greenland ice sheet (GrIS). In these topographically rough terrains, the SMB components are highly dependent on local variations in topography. However, the relatively low-resolution elevation and ice mask prescribed in RCMs contribute to significantly underestimate melt and runoff in these regions due to unresolved valley glaciers and fjords. Therefore, near-km resolution topography is essential to better capture SMB variability in these spatially restricted regions. We present a 1 km resolution dataset of daily GrIS SMB covering the period 1958-2014, which is statistically downscaled from data of the polar regional climate model RACMO2.3 at 11 km, using an elevation dependence. The dataset includes all individual SMB components projected on the elevation and ice mask from the GIMP DEM, down-sampled to 1 km. Daily runoff and sublimation are interpolated to the 1 km topography using a local regression to elevation valid for each day specifically; daily precipitation is bi-linearly downscaled without elevation corrections. The daily SMB dataset is then reconstructed by summing downscaled precipitation, sublimation and runoff. High-resolution elevation and ice mask allow for properly resolving the narrow ablation zones and valley glaciers at the GrIS margins, leading to significant increase in runoff estimate. In these regions, and especially over narrow glaciers tongues, the downscaled products improve on the original RACMO2.3 outputs by better representing local SMB patterns through a gradual ablation increase towards the GrIS margins. We discuss the impact of downscaling on the SMB components in a case study for a spatially restricted region, where large elevation discrepancies are observed between both resolutions. Owing to generally enhanced runoff in the GrIS ablation zone, the evaluation of daily downscaled SMB against ablation measurements, collected at in-situ measuring sites derived from a newly compiled ablation dataset, shows a better agreement with observations relative to native RACMO2.3 SMB at 11 km.

Comparison of Radiative Energy Flows in Observational Datasets and Climate Modeling

NASA Technical Reports Server (NTRS)

Raschke, Ehrhard; Kinne, Stefan; Rossow, William B.; Stackhouse, Paul W. Jr.; Wild, Martin

2016-01-01

This study examines radiative flux distributions and local spread of values from three major observational datasets (CERES, ISCCP, and SRB) and compares them with results from climate modeling (CMIP3). Examinations of the spread and differences also differentiate among contributions from cloudy and clear-sky conditions. The spread among observational datasets is in large part caused by noncloud ancillary data. Average differences of at least 10Wm(exp -2) each for clear-sky downward solar, upward solar, and upward infrared fluxes at the surface demonstrate via spatial difference patterns major differences in assumptions for atmospheric aerosol, solar surface albedo and surface temperature, and/or emittance in observational datasets. At the top of the atmosphere (TOA), observational datasets are less influenced by the ancillary data errors than at the surface. Comparisons of spatial radiative flux distributions at the TOA between observations and climate modeling indicate large deficiencies in the strength and distribution of model-simulated cloud radiative effects. Differences are largest for lower-altitude clouds over low-latitude oceans. Global modeling simulates stronger cloud radiative effects (CRE) by +30Wmexp -2) over trade wind cumulus regions, yet smaller CRE by about -30Wm(exp -2) over (smaller in area) stratocumulus regions. At the surface, climate modeling simulates on average about 15Wm(exp -2) smaller radiative net flux imbalances, as if climate modeling underestimates latent heat release (and precipitation). Relative to observational datasets, simulated surface net fluxes are particularly lower over oceanic trade wind regions (where global modeling tends to overestimate the radiative impact of clouds). Still, with the uncertainty in noncloud ancillary data, observational data do not establish a reliable reference.

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

PubMed Central

Wang, Yaping; Nie, Jingxin; Yap, Pew-Thian; Li, Gang; Shi, Feng; Geng, Xiujuan; Guo, Lei; Shen, Dinggang

2014-01-01

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness. PMID:24489639

Identifying public water facilities with low spatial variability of disinfection by-products for epidemiological investigations

PubMed Central

Hinckley, A; Bachand, A; Nuckols, J; Reif, J

2005-01-01

Background and Aims: Epidemiological studies of disinfection by-products (DBPs) and reproductive outcomes have been hampered by misclassification of exposure. In most epidemiological studies conducted to date, all persons living within the boundaries of a water distribution system have been assigned a common exposure value based on facility-wide averages of trihalomethane (THM) concentrations. Since THMs do not develop uniformly throughout a distribution system, assignment of facility-wide averages may be inappropriate. One approach to mitigate this potential for misclassification is to select communities for epidemiological investigations that are served by distribution systems with consistently low spatial variability of THMs. Methods and Results: A feasibility study was conducted to develop methods for community selection using the Information Collection Rule (ICR) database, assembled by the US Environmental Protection Agency. The ICR database contains quarterly DBP concentrations collected between 1997 and 1998 from the distribution systems of 198 public water facilities with minimum service populations of 100 000 persons. Facilities with low spatial variation of THMs were identified using two methods; 33 facilities were found with low spatial variability based on one or both methods. Because brominated THMs may be important predictors of risk for adverse reproductive outcomes, sites were categorised into three exposure profiles according to proportion of brominated THM species and average TTHM concentration. The correlation between THMs and haloacetic acids (HAAs) in these facilities was evaluated to see whether selection by total trihalomethanes (TTHMs) corresponds to low spatial variability for HAAs. TTHMs were only moderately correlated with HAAs (r = 0.623). Conclusions: Results provide a simple method for a priori selection of sites with low spatial variability from state or national public water facility datasets as a means to reduce exposure misclassification in epidemiological studies of DBPs. PMID:15961627

A Multihazard Regional Level Impact Assessment for South Asia

NASA Astrophysics Data System (ADS)

Amarnath, Giriraj; Alahacoon, Niranga; Aggarwal, Pramod; Smakhtin, Vladimir

2016-04-01

To prioritize climate adaptation strategies, there is a need for quantitative and systematic regional-level assessments which are comparable across multiple climatic hazard regimes. Assessing which countries in a region are most vulnerable to climate change requires analysis of multiple climatic hazards including: droughts, floods, extreme temperature as well as rainfall and sea-level rise. These five climatic hazards, along with population densities were modelled using GIS which enabled a summary of associated human exposure and agriculture losses. A combined index based on hazard, exposure and adaptive capacity is introduced to identify areas of extreme risks. The analysis results in population climate hazard exposure defined as the relative likelihood that a person in a given location was exposed to a given climate-hazard event in a given period of time. The study presents a detailed and coherent approach to fine-scale climate hazard mapping and identification of risks areas for the regions of South Asia that, for the first time, combines the following unique features: (a) methodological consistency across different climate-related hazards, (b) assessment of total exposure on population and agricultural losses, (c) regional-level spatial coverage, and (d) development of customized tools using ArcGIS toolbox that allow assessment of changes in exposure over time and easy replacement of existing datasets with a newly released or superior datasets. The resulting maps enable comparison of the most vulnerable regions in South Asia to climate-related hazards and is among the most urgent of policy needs. Subnational areas (regions/districts/provinces) most vulnerable to climate change impacts in South Asia are documented. The approach involves overlaying climate hazard maps, sensitivity maps, and adaptive capacity maps following the vulnerability assessment framework of the United Nations' Intergovernmental Panel on Climate Change (IPCC). The study used data on the spatial distribution of various climate-related hazards in 1,398 subnational areas of Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka. An analysis of country-level population exposure showed that approximately 750 million people are affected from combined climate-hazards. Of the affected population 72% are in India, followed by 12% each from Bangladesh and Pakistan. Due in part to the economic importance of agriculture, it was found to be most vulnerable and exposed to climate extremes. An analysis of individual hazards indicates that floods and droughts) are the dominant hazards impacting agricultural areas followed by extreme rainfall, extreme temperature and sea-level rise. Based on this vulnerability assessment, all the regions of Bangladesh and the Indian States in Andhra Pradesh, Bihar, Maharashtra, Karnataka and Orissa; Ampara, Puttalam, Trincomalee, Mannar and Batticaloa in Sri Lanka; Sind and Baluchistan in Pakistan; Central and East Nepal; and the transboundary river basins of Indus, Ganges and Brahmaputra are among the most vulnerable regions in South Asia.

Population Density, Climate Variables and Poverty Synergistically Structure Spatial Risk in Urban Malaria in India

PubMed Central

Santos-Vega, Mauricio; Bouma, Menno J; Kohli, Vijay; Pascual, Mercedes

2016-01-01

Background The world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria. Methodology/principal findings Statistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors. Conclusion/significance Climate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission ‘hotspots’. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a “radical cure”. PMID:27906962

Population Density, Climate Variables and Poverty Synergistically Structure Spatial Risk in Urban Malaria in India.

PubMed

Santos-Vega, Mauricio; Bouma, Menno J; Kohli, Vijay; Pascual, Mercedes

2016-12-01

The world is rapidly becoming urban with the global population living in cities projected to double by 2050. This increase in urbanization poses new challenges for the spread and control of communicable diseases such as malaria. In particular, urban environments create highly heterogeneous socio-economic and environmental conditions that can affect the transmission of vector-borne diseases dependent on human water storage and waste water management. Interestingly India, as opposed to Africa, harbors a mosquito vector, Anopheles stephensi, which thrives in the man-made environments of cities and acts as the vector for both Plasmodium vivax and Plasmodium falciparum, making the malaria problem a truly urban phenomenon. Here we address the role and determinants of within-city spatial heterogeneity in the incidence patterns of vivax malaria, and then draw comparisons with results for falciparum malaria. Statistical analyses and a phenomenological transmission model are applied to an extensive spatio-temporal dataset on cases of Plasmodium vivax in the city of Ahmedabad (Gujarat, India) that spans 12 years monthly at the level of wards. A spatial pattern in malaria incidence is described that is largely stationary in time for this parasite. Malaria risk is then shown to be associated with socioeconomic indicators and environmental parameters, temperature and humidity. In a more dynamical perspective, an Inhomogeneous Markov Chain Model is used to predict vivax malaria risk. Models that account for climate factors, socioeconomic level and population size show the highest predictive skill. A comparison to the transmission dynamics of falciparum malaria reinforces the conclusion that the spatio-temporal patterns of risk are strongly driven by extrinsic factors. Climate forcing and socio-economic heterogeneity act synergistically at local scales on the population dynamics of urban malaria in this city. The stationarity of malaria risk patterns provides a basis for more targeted intervention, such as vector control, based on transmission 'hotspots'. This is especially relevant for P. vivax, a more resilient parasite than P. falciparum, due to its ability to relapse and the operational shortcomings of delivering a "radical cure".

Surrogate taxa and fossils as reliable proxies of spatial biodiversity patterns in marine benthic communities.

PubMed

Tyler, Carrie L; Kowalewski, Michał

2017-03-15

Rigorous documentation of spatial heterogeneity (β-diversity) in present-day and preindustrial ecosystems is required to assess how marine communities respond to environmental and anthropogenic drivers. However, the overwhelming majority of contemporary and palaeontological assessments have centred on single higher taxa. To evaluate the validity of single taxa as community surrogates and palaeontological proxies, we compared macrobenthic communities and sympatric death assemblages at 52 localities in Onslow Bay (NC, USA). Compositional heterogeneity did not differ significantly across datasets based on live molluscs, live non-molluscs, and all live organisms. Death assemblages were less heterogeneous spatially, likely reflecting homogenization by time-averaging. Nevertheless, live and dead datasets were greater than 80% congruent in pairwise comparisons to the literature estimates of β-diversity in other marine ecosystems, yielded concordant bathymetric gradients, and produced nearly identical ordinations consistently delineating habitats. Congruent estimates from molluscs and non-molluscs suggest that single groups can serve as reliable community proxies. High spatial fidelity of death assemblages supports the emerging paradigm of Conservation Palaeobiology. Integrated analyses of ecological and palaeontological data based on surrogate taxa can quantify anthropogenic changes in marine ecosystems and advance our understanding of spatial and temporal aspects of biodiversity. © 2017 The Author(s).

Spatial decorrelation stretch of annual (2003-2014) Daymet precipitation summaries on a 1-km grid for California, Nevada, Arizona, and Utah.

PubMed

Ch Miliaresis, George

2016-06-01

A method is presented for elevation (H) and spatial position (X, Y) decorrelation stretch of annual precipitation summaries on a 1-km grid for SW USA for the period 2003 to 2014. Multiple linear regression analysis of the first and second principal component (PC) quantifies the variance in the multi-temporal precipitation imagery that is explained by X, Y, and elevation (h). The multi-temporal dataset is reconstructed from the PC1 and PC2 residual images and the later PCs by taking into account the variance that is not related to X, Y, and h. Clustering of the reconstructed precipitation dataset allowed the definition of positive (for example, in Sierra Nevada, Salt Lake City) and negative (for example, in San Joaquin Valley, Nevada, Colorado Plateau) precipitation anomalies. The temporal and spatial patterns defined from the spatially standardized multi-temporal precipitation imagery provide a tool of comparison for regions in different geographic environments according to the deviation from the precipitation amount that they are expected to receive as function of X, Y, and h. Such a standardization allows the definition of less or more sensitive to climatic change regions and gives an insight in the spatial impact of atmospheric circulation that causes the annual precipitation.

Digital hydrologic networks supporting applications related to spatially referenced regression modeling

USGS Publications Warehouse

Brakebill, John W.; Wolock, David M.; Terziotti, Silvia

2011-01-01

Digital hydrologic networks depicting surface-water pathways and their associated drainage catchments provide a key component to hydrologic analysis and modeling. Collectively, they form common spatial units that can be used to frame the descriptions of aquatic and watershed processes. In addition, they provide the ability to simulate and route the movement of water and associated constituents throughout the landscape. Digital hydrologic networks have evolved from derivatives of mapping products to detailed, interconnected, spatially referenced networks of water pathways, drainage areas, and stream and watershed characteristics. These properties are important because they enhance the ability to spatially evaluate factors that affect the sources and transport of water-quality constituents at various scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW), a process-based ⁄ statistical model, relies on a digital hydrologic network in order to establish relations between quantities of monitored contaminant flux, contaminant sources, and the associated physical characteristics affecting contaminant transport. Digital hydrologic networks modified from the River Reach File (RF1) and National Hydrography Dataset (NHD) geospatial datasets provided frameworks for SPARROW in six regions of the conterminous United States. In addition, characteristics of the modified RF1 were used to update estimates of mean-annual streamflow. This produced more current flow estimates for use in SPARROW modeling.

Octree-based indexing for 3D pointclouds within an Oracle Spatial DBMS

NASA Astrophysics Data System (ADS)

Schön, Bianca; Mosa, Abu Saleh Mohammad; Laefer, Debra F.; Bertolotto, Michela

2013-02-01

A large proportion of today's digital datasets have a spatial component. The effective storage and management of which poses particular challenges, especially with light detection and ranging (LiDAR), where datasets of even small geographic areas may contain several hundred million points. While in the last decade 2.5-dimensional data were prevalent, true 3-dimensional data are increasingly commonplace via LiDAR. They have gained particular popularity for urban applications including generation of city-scale maps, baseline data disaster management, and utility planning. Additionally, LiDAR is commonly used for flood plane identification, coastal-erosion tracking, and forest biomass mapping. Despite growing data availability, current spatial information systems do not provide suitable full support for the data's true 3D nature. Consequently, one system is needed to store the data and another for its processing, thereby necessitating format transformations. The work presented herein aims at a more cost-effective way for managing 3D LiDAR data that allows for storage and manipulation within a single system by enabling a new index within existing spatial database management technology. Implementation of an octree index for 3D LiDAR data atop Oracle Spatial 11g is presented, along with an evaluation showing up to an eight-fold improvement compared to the native Oracle R-tree index.

Spatially-explicit models of global tree density.

PubMed

Glick, Henry B; Bettigole, Charlie; Maynard, Daniel S; Covey, Kristofer R; Smith, Jeffrey R; Crowther, Thomas W

2016-08-16

Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution.

PubMed

Gangnon, Ronald E

2012-03-01

The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, whereas rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. © 2011, The International Biometric Society.

Local multiplicity adjustment for the spatial scan statistic using the Gumbel distribution

PubMed Central

Gangnon, Ronald E.

2011-01-01

Summary The spatial scan statistic is an important and widely used tool for cluster detection. It is based on the simultaneous evaluation of the statistical significance of the maximum likelihood ratio test statistic over a large collection of potential clusters. In most cluster detection problems, there is variation in the extent of local multiplicity across the study region. For example, using a fixed maximum geographic radius for clusters, urban areas typically have many overlapping potential clusters, while rural areas have relatively few. The spatial scan statistic does not account for local multiplicity variation. We describe a previously proposed local multiplicity adjustment based on a nested Bonferroni correction and propose a novel adjustment based on a Gumbel distribution approximation to the distribution of a local scan statistic. We compare the performance of all three statistics in terms of power and a novel unbiased cluster detection criterion. These methods are then applied to the well-known New York leukemia dataset and a Wisconsin breast cancer incidence dataset. PMID:21762118

Global climate shocks to agriculture from 1950 - 2015

NASA Astrophysics Data System (ADS)

Jackson, N. D.; Konar, M.; Debaere, P.; Sheffield, J.

2016-12-01

Climate shocks represent a major disruption to crop yields and agricultural production, yet a consistent and comprehensive database of agriculturally relevant climate shocks does not exist. To this end, we conduct a spatially and temporally disaggregated analysis of climate shocks to agriculture from 1950-2015 using a new gridded dataset. We quantify the occurrence and magnitude of climate shocks for all global agricultural areas during the growing season using a 0.25-degree spatial grid and daily time scale. We include all major crops and both temperature and precipitation extremes in our analysis. Critically, we evaluate climate shocks to all potential agricultural areas to improve projections within our time series. To do this, we use Global Agro-Ecological Zones maps from the Food and Agricultural Organization, the Princeton Global Meteorological Forcing dataset, and crop calendars from Sacks et al. (2010). We trace the dynamic evolution of climate shocks to agriculture, evaluate the spatial heterogeneity in agriculturally relevant climate shocks, and identify the crops and regions that are most prone to climate shocks.

A comparison of spatial interpolation methods for soil temperature over a complex topographical region

NASA Astrophysics Data System (ADS)

Wu, Wei; Tang, Xiao-Ping; Ma, Xue-Qing; Liu, Hong-Bin

2016-08-01

Soil temperature variability data provide valuable information on understanding land-surface ecosystem processes and climate change. This study developed and analyzed a spatial dataset of monthly mean soil temperature at a depth of 10 cm over a complex topographical region in southwestern China. The records were measured at 83 stations during the period of 1961-2000. Nine approaches were compared for interpolating soil temperature. The accuracy indicators were root mean square error (RMSE), modelling efficiency (ME), and coefficient of residual mass (CRM). The results indicated that thin plate spline with latitude, longitude, and elevation gave the best performance with RMSE varying between 0.425 and 0.592 °C, ME between 0.895 and 0.947, and CRM between -0.007 and 0.001. A spatial database was developed based on the best model. The dataset showed that larger seasonal changes of soil temperature were from autumn to winter over the region. The northern and eastern areas with hilly and low-middle mountains experienced larger seasonal changes.

Spatial Variation of Selenium in Appalachian Coal Seams

NASA Astrophysics Data System (ADS)

Le, L.; Tyner, J. S.; Perfect, E.; Yoder, D. C.

2013-12-01

The potential environmental impacts from coal extraction have led to many investigations of the geochemistry of coal. Previous studies have shown that selenium (Se) is an environmental contaminant due to its mutagenic effects on sensitive macro-organisms as a result of bioaccumulation in affected waters. Some regulatory authorities have responded by requiring the sampling of coal seams and adjacent rock for Se prior to authorizing a given coal mining permit. In at least one case, a single continuous rock core was sampled for Se to determine the threshold of Se across a 2.2 square kilometer proposed surface coal mine. To examine the adequacy of such an approach, we investigated the spatial variability and correlation of a West Virginia Geological and Economic Survey (WVGES) dataset of Se concentrations from coal seams collected within Appalachia (1088 samples). We conducted semi-variogram and Kriging cross-validation analyses on six coal seams from the dataset. Our findings suggest no significant spatial correlation of Se within a given coal seam.

VLUIS, a land use data product for Victoria, Australia, covering 2006 to 2013

PubMed Central

Morse-McNabb, Elizabeth; Sheffield, Kathryn; Clark, Rob; Lewis, Hayden; Robson, Susan; Cherry, Don; Williams, Steve

2015-01-01

Land Use Information is a key dataset required to enable an understanding of the changing nature of our landscapes and the associated influences on natural resources and regional communities. The Victorian Land Use Information System (VLUIS) data product has been created within the State Government of Victoria to support land use assessments. The project began in 2007 using stakeholder engagement to establish product requirements such as format, classification, frequency and spatial resolution. Its genesis is significantly different to traditional methods, incorporating data from a range of jurisdictions to develop land use information designed for regular on-going creation and consistency. Covering the entire landmass of Victoria, the dataset separately describes land tenure, land use and land cover. These variables are co-registered to a common spatial base (cadastral parcels) across the state for the period 2006 to 2013; biennially for land tenure and land use, and annually for land cover. Data is produced as a spatial GIS feature class. PMID:26602150

VLUIS, a land use data product for Victoria, Australia, covering 2006 to 2013.

PubMed

Morse-McNabb, Elizabeth; Sheffield, Kathryn; Clark, Rob; Lewis, Hayden; Robson, Susan; Cherry, Don; Williams, Steve

2015-11-24

Land Use Information is a key dataset required to enable an understanding of the changing nature of our landscapes and the associated influences on natural resources and regional communities. The Victorian Land Use Information System (VLUIS) data product has been created within the State Government of Victoria to support land use assessments. The project began in 2007 using stakeholder engagement to establish product requirements such as format, classification, frequency and spatial resolution. Its genesis is significantly different to traditional methods, incorporating data from a range of jurisdictions to develop land use information designed for regular on-going creation and consistency. Covering the entire landmass of Victoria, the dataset separately describes land tenure, land use and land cover. These variables are co-registered to a common spatial base (cadastral parcels) across the state for the period 2006 to 2013; biennially for land tenure and land use, and annually for land cover. Data is produced as a spatial GIS feature class.

Recommended GIS Analysis Methods for Global Gridded Population Data

NASA Astrophysics Data System (ADS)

Frye, C. E.; Sorichetta, A.; Rose, A.

2017-12-01

When using geographic information systems (GIS) to analyze gridded, i.e., raster, population data, analysts need a detailed understanding of several factors that affect raster data processing, and thus, the accuracy of the results. Global raster data is most often provided in an unprojected state, usually in the WGS 1984 geographic coordinate system. Most GIS functions and tools evaluate data based on overlay relationships (area) or proximity (distance). Area and distance for global raster data can be either calculated directly using the various earth ellipsoids or after transforming the data to equal-area/equidistant projected coordinate systems to analyze all locations equally. However, unlike when projecting vector data, not all projected coordinate systems can support such analyses equally, and the process of transforming raster data from one coordinate space to another often results unmanaged loss of data through a process called resampling. Resampling determines which values to use in the result dataset given an imperfect locational match in the input dataset(s). Cell size or resolution, registration, resampling method, statistical type, and whether the raster represents continuous or discreet information potentially influence the quality of the result. Gridded population data represent estimates of population in each raster cell, and this presentation will provide guidelines for accurately transforming population rasters for analysis in GIS. Resampling impacts the display of high resolution global gridded population data, and we will discuss how to properly handle pyramid creation using the Aggregate tool with the sum option to create overviews for mosaic datasets.

Climate and population density drive changes in cod body size throughout a century on the Norwegian coast

PubMed Central

Rogers, Lauren A.; Stige, Leif C.; Olsen, Esben M.; Knutsen, Halvor; Chan, Kung-Sik; Stenseth, Nils Chr.

2011-01-01

Understanding how populations respond to changes in climate requires long-term, high-quality datasets, which are rare for marine systems. We estimated the effects of climate warming on cod lengths and length variability using a unique 91-y time series of more than 100,000 individual juvenile cod lengths from surveys that began in 1919 along the Norwegian Skagerrak coast. Using linear mixed-effects models, we accounted for spatial population structure and the nested structure of the survey data to reveal opposite effects of spring and summer warming on juvenile cod lengths. Warm summer temperatures in the coastal Skagerrak have limited juvenile growth. In contrast, warmer springs have resulted in larger juvenile cod, with less variation in lengths within a cohort, possibly because of a temperature-driven contraction in the spring spawning period. A density-dependent reduction in length was evident only at the highest population densities in the time series, which have rarely been observed in the last decade. If temperatures rise because of global warming, nonlinearities in the opposing temperature effects suggest that negative effects of warmer summers will increasingly outweigh positive effects of warmer springs, and the coastal Skagerrak will become ill-suited for Atlantic cod. PMID:21245301

Economic development and coastal ecosystem change in China

PubMed Central

He, Qiang; Bertness, Mark D.; Bruno, John F.; Li, Bo; Chen, Guoqian; Coverdale, Tyler C.; Altieri, Andrew H.; Bai, Junhong; Sun, Tao; Pennings, Steven C.; Liu, Jianguo; Ehrlich, Paul R.; Cui, Baoshan

2014-01-01

Despite their value, coastal ecosystems are globally threatened by anthropogenic impacts, yet how these impacts are driven by economic development is not well understood. We compiled a multifaceted dataset to quantify coastal trends and examine the role of economic growth in China's coastal degradation since the 1950s. Although China's coastal population growth did not change following the 1978 economic reforms, its coastal economy increased by orders of magnitude. All 15 coastal human impacts examined increased over time, especially after the reforms. Econometric analysis revealed positive relationships between most impacts and GDP across temporal and spatial scales, often lacking dropping thresholds. These relationships generally held when influences of population growth were addressed by analyzing per capita impacts, and when population density was included as explanatory variables. Historical trends in physical and biotic indicators showed that China's coastal ecosystems changed little or slowly between the 1950s and 1978, but have degraded at accelerated rates since 1978. Thus economic growth has been the cause of accelerating human damage to China's coastal ecosystems. China's GDP per capita remains very low. Without strict conservation efforts, continuing economic growth will further degrade China's coastal ecosystems. PMID:25104138

Epidemic Spread of Symbiotic and Non-Symbiotic Bradyrhizobium Genotypes Across California.

PubMed

Hollowell, A C; Regus, J U; Gano, K A; Bantay, R; Centeno, D; Pham, J; Lyu, J Y; Moore, D; Bernardo, A; Lopez, G; Patil, A; Patel, S; Lii, Y; Sachs, J L

2016-04-01

The patterns and drivers of bacterial strain dominance remain poorly understood in natural populations. Here, we cultured 1292 Bradyrhizobium isolates from symbiotic root nodules and the soil root interface of the host plant Acmispon strigosus across a >840-km transect in California. To investigate epidemiology and the potential role of accessory loci as epidemic drivers, isolates were genotyped at two chromosomal loci and were assayed for presence or absence of accessory "symbiosis island" loci that encode capacity to form nodules on hosts. We found that Bradyrhizobium populations were very diverse but dominated by few haplotypes-with a single "epidemic" haplotype constituting nearly 30 % of collected isolates and spreading nearly statewide. In many Bradyrhizobium lineages, we inferred presence and absence of the symbiosis island suggesting recurrent evolutionary gain and or loss of symbiotic capacity. We did not find statistical phylogenetic evidence that the symbiosis island acquisition promotes strain dominance and both symbiotic and non-symbiotic strains exhibited population dominance and spatial spread. Our dataset reveals that a strikingly few Bradyrhizobium genotypes can rapidly spread to dominate a landscape and suggests that these epidemics are not driven by the acquisition of accessory loci as occurs in key human pathogens.

Automated Topographic Change Detection via Dem Differencing at Large Scales Using The Arcticdem Database

NASA Astrophysics Data System (ADS)

Candela, S. G.; Howat, I.; Noh, M. J.; Porter, C. C.; Morin, P. J.

2016-12-01

In the last decade, high resolution satellite imagery has become an increasingly accessible tool for geoscientists to quantify changes in the Arctic land surface due to geophysical, ecological and anthropomorphic processes. However, the trade off between spatial coverage and spatial-temporal resolution has limited detailed, process-level change detection over large (i.e. continental) scales. The ArcticDEM project utilized over 300,000 Worldview image pairs to produce a nearly 100% coverage elevation model (above 60°N) offering the first polar, high spatial - high resolution (2-8m by region) dataset, often with multiple repeats in areas of particular interest to geo-scientists. A dataset of this size (nearly 250 TB) offers endless new avenues of scientific inquiry, but quickly becomes unmanageable computationally and logistically for the computing resources available to the average scientist. Here we present TopoDiff, a framework for a generalized. automated workflow that requires minimal input from the end user about a study site, and utilizes cloud computing resources to provide a temporally sorted and differenced dataset, ready for geostatistical analysis. This hands-off approach allows the end user to focus on the science, without having to manage thousands of files, or petabytes of data. At the same time, TopoDiff provides a consistent and accurate workflow for image sorting, selection, and co-registration enabling cross-comparisons between research projects.

Detecting and modelling structures on the micro and the macro scales: Assessing their effects on solute transport behaviour

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.; Sudicky, E. A.

2017-09-01

This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated. The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully.

High-resolution spatial databases of monthly climate variables (1961-2010) over a complex terrain region in southwestern China

NASA Astrophysics Data System (ADS)

Wu, Wei; Xu, An-Ding; Liu, Hong-Bin

2015-01-01

Climate data in gridded format are critical for understanding climate change and its impact on eco-environment. The aim of the current study is to develop spatial databases for three climate variables (maximum, minimum temperatures, and relative humidity) over a large region with complex topography in southwestern China. Five widely used approaches including inverse distance weighting, ordinary kriging, universal kriging, co-kriging, and thin-plate smoothing spline were tested. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) showed that thin-plate smoothing spline with latitude, longitude, and elevation outperformed other models. Average RMSE, MAE, and MAPE of the best models were 1.16 °C, 0.74 °C, and 7.38 % for maximum temperature; 0.826 °C, 0.58 °C, and 6.41 % for minimum temperature; and 3.44, 2.28, and 3.21 % for relative humidity, respectively. Spatial datasets of annual and monthly climate variables with 1-km resolution covering the period 1961-2010 were then obtained using the best performance methods. Comparative study showed that the current outcomes were in well agreement with public datasets. Based on the gridded datasets, changes in temperature variables were investigated across the study area. Future study might be needed to capture the uncertainty induced by environmental conditions through remote sensing and knowledge-based methods.

Application Perspective of 2D+SCALE Dimension

NASA Astrophysics Data System (ADS)

Karim, H.; Rahman, A. Abdul

2016-09-01

Different applications or users need different abstraction of spatial models, dimensionalities and specification of their datasets due to variations of required analysis and output. Various approaches, data models and data structures are now available to support most current application models in Geographic Information System (GIS). One of the focuses trend in GIS multi-dimensional research community is the implementation of scale dimension with spatial datasets to suit various scale application needs. In this paper, 2D spatial datasets that been scaled up as the third dimension are addressed as 2D+scale (or 3D-scale) dimension. Nowadays, various data structures, data models, approaches, schemas, and formats have been proposed as the best approaches to support variety of applications and dimensionality in 3D topology. However, only a few of them considers the element of scale as their targeted dimension. As the scale dimension is concerned, the implementation approach can be either multi-scale or vario-scale (with any available data structures and formats) depending on application requirements (topology, semantic and function). This paper attempts to discuss on the current and new potential applications which positively could be integrated upon 3D-scale dimension approach. The previous and current works on scale dimension as well as the requirements to be preserved for any given applications, implementation issues and future potential applications forms the major discussion of this paper.

Temperature suitability for malaria climbing the Ethiopian Highlands

NASA Astrophysics Data System (ADS)

Lyon, Bradfield; Dinku, Tufa; Raman, Anita; Thomson, Madeleine C.

2017-06-01

While the effect of climate change on the prevalence of malaria in the highlands of Eastern Africa has been the topic of protracted debate, temperature is widely accepted as a fundamentally important environmental factor constraining its transmission. Air temperatures below approximately 18 °C and 15 °C, respectively, prohibit the development of the Plasmodium falciparum and P. vivax parasites responsible for the majority of malaria cases in Ethiopia. Low temperatures also impede the development rates of the Anopheles mosquito vectors. While locations of sufficiently high elevation have temperatures below these transmission thresholds, a fundamental question is how such temperature ‘threshold elevations’ are changing with time. A lack of high quality, high spatial resolution climate data has previously prohibited a rigorous investigation. Using a newly developed national temperature dataset for Ethiopia that combines numerous in-situ surface observations with downscaled reanalysis data, we here identify statistically significant increases in elevation for both the 18 °C and 15 °C thresholds in highland areas between 1981-2014. Substantial interannual and spatial variations in threshold elevations are identified, the former associated with the El Niño Southern-Oscillation and the latter with the complex climate of the region. The estimated population in locations with an upward trend in the 15 °C threshold elevation is approximately 6.5 million people (2.2 million for 18 °C). While not a direct prediction of the additional population made vulnerable to malaria through a shift to higher temperature, our results underscore a newly acquired ability to investigate climate variability and trends at fine spatial scales across Ethiopia, including changes in a fundamental constraint on malaria transmission in the Ethiopian Highlands.

Spatial patterns of the frog Oophaga pumilio in a plantation system are consistent with conspecific attraction.

PubMed

Folt, Brian; Donnelly, Maureen A; Guyer, Craig

2018-03-01

The conspecific attraction hypothesis predicts that individuals are attracted to conspecifics because conspecifics may be cues to quality habitat and/or colonists may benefit from living in aggregations. Poison frogs (Dendrobatidae) are aposematic, territorial, and visually oriented-three characteristics which make dendrobatids an appropriate model to test for conspecific attraction. In this study, we tested this hypothesis using an extensive mark-recapture dataset of the strawberry poison frog ( Oophaga pumilio ) from La Selva Biological Station, Costa Rica. Data were collected from replicate populations in a relatively homogenous Theobroma cacao plantation, which provided a unique opportunity to test how conspecifics influence the spatial ecology of migrants in a controlled habitat with homogenous structure. We predicted that (1) individuals entering a population would aggregate with resident adults, (2) migrants would share sites with residents at a greater frequency than expected by chance, and (3) migrant home ranges would have shorter nearest-neighbor distances (NND) to residents than expected by chance. The results were consistent with these three predictions: Relative to random simulations, we observed significant aggregation, home-range overlap, and NND distribution functions in four, five, and six, respectively, of the six migrant-resident groups analyzed. Conspecific attraction may benefit migrant O. pumilio by providing cues to suitable home sites and/or increasing the potential for social interactions with conspecifics; if true, these benefits should outweigh the negative effects of other factors associated with aggregation. The observed aggregation between migrant and resident O. pumilio is consistent with conspecific attraction in dendrobatid frogs, and our study provides rare support from a field setting that conspecific attraction may be a relevant mechanism for models of anuran spatial ecology.

Spatial heterogeneity of type I error for local cluster detection tests

PubMed Central

2014-01-01

Background Just as power, type I error of cluster detection tests (CDTs) should be spatially assessed. Indeed, CDTs’ type I error and power have both a spatial component as CDTs both detect and locate clusters. In the case of type I error, the spatial distribution of wrongly detected clusters (WDCs) can be particularly affected by edge effect. This simulation study aims to describe the spatial distribution of WDCs and to confirm and quantify the presence of edge effect. Methods A simulation of 40 000 datasets has been performed under the null hypothesis of risk homogeneity. The simulation design used realistic parameters from survey data on birth defects, and in particular, two baseline risks. The simulated datasets were analyzed using the Kulldorff’s spatial scan as a commonly used test whose behavior is otherwise well known. To describe the spatial distribution of type I error, we defined the participation rate for each spatial unit of the region. We used this indicator in a new statistical test proposed to confirm, as well as quantify, the edge effect. Results The predefined type I error of 5% was respected for both baseline risks. Results showed strong edge effect in participation rates, with a descending gradient from center to edge, and WDCs more often centrally situated. Conclusions In routine analysis of real data, clusters on the edge of the region should be carefully considered as they rarely occur when there is no cluster. Further work is needed to combine results from power studies with this work in order to optimize CDTs performance. PMID:24885343

Mapping genetic diversity of cherimoya (Annona cherimola Mill.): application of spatial analysis for conservation and use of plant genetic resources.

PubMed

Zonneveld, Maarten van; Scheldeman, Xavier; Escribano, Pilar; Viruel, María A; Van Damme, Patrick; Garcia, Willman; Tapia, César; Romero, José; Sigueñas, Manuel; Hormaza, José I

2012-01-01

There is a growing call for inventories that evaluate geographic patterns in diversity of plant genetic resources maintained on farm and in species' natural populations in order to enhance their use and conservation. Such evaluations are relevant for useful tropical and subtropical tree species, as many of these species are still undomesticated, or in incipient stages of domestication and local populations can offer yet-unknown traits of high value to further domestication. For many outcrossing species, such as most trees, inbreeding depression can be an issue, and genetic diversity is important to sustain local production. Diversity is also crucial for species to adapt to environmental changes. This paper explores the possibilities of incorporating molecular marker data into Geographic Information Systems (GIS) to allow visualization and better understanding of spatial patterns of genetic diversity as a key input to optimize conservation and use of plant genetic resources, based on a case study of cherimoya (Annona cherimola Mill.), a Neotropical fruit tree species. We present spatial analyses to (1) improve the understanding of spatial distribution of genetic diversity of cherimoya natural stands and cultivated trees in Ecuador, Bolivia and Peru based on microsatellite molecular markers (SSRs); and (2) formulate optimal conservation strategies by revealing priority areas for in situ conservation, and identifying existing diversity gaps in ex situ collections. We found high levels of allelic richness, locally common alleles and expected heterozygosity in cherimoya's putative centre of origin, southern Ecuador and northern Peru, whereas levels of diversity in southern Peru and especially in Bolivia were significantly lower. The application of GIS on a large microsatellite dataset allows a more detailed prioritization of areas for in situ conservation and targeted collection across the Andean distribution range of cherimoya than previous studies could do, i.e. at province and department level in Ecuador and Peru, respectively.

Mapping Genetic Diversity of Cherimoya (Annona cherimola Mill.): Application of Spatial Analysis for Conservation and Use of Plant Genetic Resources

PubMed Central

van Zonneveld, Maarten; Scheldeman, Xavier; Escribano, Pilar; Viruel, María A.; Van Damme, Patrick; Garcia, Willman; Tapia, César; Romero, José; Sigueñas, Manuel; Hormaza, José I.

2012-01-01

There is a growing call for inventories that evaluate geographic patterns in diversity of plant genetic resources maintained on farm and in species' natural populations in order to enhance their use and conservation. Such evaluations are relevant for useful tropical and subtropical tree species, as many of these species are still undomesticated, or in incipient stages of domestication and local populations can offer yet-unknown traits of high value to further domestication. For many outcrossing species, such as most trees, inbreeding depression can be an issue, and genetic diversity is important to sustain local production. Diversity is also crucial for species to adapt to environmental changes. This paper explores the possibilities of incorporating molecular marker data into Geographic Information Systems (GIS) to allow visualization and better understanding of spatial patterns of genetic diversity as a key input to optimize conservation and use of plant genetic resources, based on a case study of cherimoya (Annona cherimola Mill.), a Neotropical fruit tree species. We present spatial analyses to (1) improve the understanding of spatial distribution of genetic diversity of cherimoya natural stands and cultivated trees in Ecuador, Bolivia and Peru based on microsatellite molecular markers (SSRs); and (2) formulate optimal conservation strategies by revealing priority areas for in situ conservation, and identifying existing diversity gaps in ex situ collections. We found high levels of allelic richness, locally common alleles and expected heterozygosity in cherimoya's putative centre of origin, southern Ecuador and northern Peru, whereas levels of diversity in southern Peru and especially in Bolivia were significantly lower. The application of GIS on a large microsatellite dataset allows a more detailed prioritization of areas for in situ conservation and targeted collection across the Andean distribution range of cherimoya than previous studies could do, i.e. at province and department level in Ecuador and Peru, respectively. PMID:22253801

DAPAGLOCO - A global daily precipitation dataset from satellite and rain-gauge measurements

NASA Astrophysics Data System (ADS)

Spangehl, T.; Danielczok, A.; Dietzsch, F.; Andersson, A.; Schroeder, M.; Fennig, K.; Ziese, M.; Becker, A.

2017-12-01

The BMBF funded project framework MiKlip(Mittelfristige Klimaprognosen) develops a global climate forecast system on decadal time scales for operational applications. Herein, the DAPAGLOCO project (Daily Precipitation Analysis for the validation of Global medium-range Climate predictions Operationalized) provides a global precipitation dataset as a combination of microwave-based satellite measurements over ocean and rain gauge measurements over land on daily scale. The DAPAGLOCO dataset is created for the evaluation of the MiKlip forecast system in the first place. The HOAPS dataset (Hamburg Ocean Atmosphere Parameter and Fluxes from Satellite data) is used for the derivation of precipitation rates over ocean and is extended by the use of measurements from TMI, GMI, and AMSR-E, in addition to measurements from SSM/I and SSMIS. A 1D-Var retrieval scheme is developed to retrieve rain rates from microwave imager data, which also allows for the determination of uncertainty estimates. Over land, the GPCC (Global Precipitation Climatology Center) Full Data Daily product is used. It consists of rain gauge measurements that are interpolated on a regular grid by ordinary Kriging. The currently available dataset is based on a neuronal network approach, consists of 21 years of data from 1988 to 2008 and is currently extended until 2015 using the 1D-Var scheme and with improved sampling. Three different spatial resolved dataset versions are available with 1° and 2.5° global, and 0.5° for Europe. The evaluation of the MiKlip forecast system by DAPAGLOCO is based on ETCCDI (Expert Team on Climate Change and Detection Indices). Hindcasts are used for the index-based comparison between model and observations. These indices allow for the evaluation of precipitation extremes, their spatial and temporal distribution as well as for the duration of dry and wet spells, average precipitation amounts and percentiles on global scale. Besides, an ETCCDI-based climatology of the DAPAGLOCO precipitation dataset has been derived.

Discovering New Global Climate Patterns: Curating a 21-Year High Temporal (Hourly) and Spatial (40km) Resolution Reanalysis Dataset

NASA Astrophysics Data System (ADS)

Hou, C. Y.; Dattore, R.; Peng, G. S.

2014-12-01

The National Center for Atmospheric Research's Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40km Reanalysis dataset is a dynamically downscaled dataset with high temporal and spatial resolution. The dataset contains three-dimensional hourly analyses in netCDF format for the global atmospheric state from 1985 to 2005 on a 40km horizontal grid (0.4°grid increment) with 28 vertical levels, providing good representation of local forcing and diurnal variation of processes in the planetary boundary layer. This project aimed to make the dataset publicly available, accessible, and usable in order to provide a unique resource to allow and promote studies of new climate characteristics. When the curation project started, it had been five years since the data files were generated. Also, although the Principal Investigator (PI) had generated a user document at the end of the project in 2009, the document had not been maintained. Furthermore, the PI had moved to a new institution, and the remaining team members were reassigned to other projects. These factors made data curation in the areas of verifying data quality, harvest metadata descriptions, documenting provenance information especially challenging. As a result, the project's curation process found that: Data curator's skill and knowledge helped make decisions, such as file format and structure and workflow documentation, that had significant, positive impact on the ease of the dataset's management and long term preservation. Use of data curation tools, such as the Data Curation Profiles Toolkit's guidelines, revealed important information for promoting the data's usability and enhancing preservation planning. Involving data curators during each stage of the data curation life cycle instead of at the end could improve the curation process' efficiency. Overall, the project showed that proper resources invested in the curation process would give datasets the best chance to fulfill their potential to help with new climate pattern discovery.

Annotating spatio-temporal datasets for meaningful analysis in the Web

NASA Astrophysics Data System (ADS)

Stasch, Christoph; Pebesma, Edzer; Scheider, Simon

2014-05-01

More and more environmental datasets that vary in space and time are available in the Web. This comes along with an advantage of using the data for other purposes than originally foreseen, but also with the danger that users may apply inappropriate analysis procedures due to lack of important assumptions made during the data collection process. In order to guide towards a meaningful (statistical) analysis of spatio-temporal datasets available in the Web, we have developed a Higher-Order-Logic formalism that captures some relevant assumptions in our previous work [1]. It allows to proof on meaningful spatial prediction and aggregation in a semi-automated fashion. In this poster presentation, we will present a concept for annotating spatio-temporal datasets available in the Web with concepts defined in our formalism. Therefore, we have defined a subset of the formalism as a Web Ontology Language (OWL) pattern. It allows capturing the distinction between the different spatio-temporal variable types, i.e. point patterns, fields, lattices and trajectories, that in turn determine whether a particular dataset can be interpolated or aggregated in a meaningful way using a certain procedure. The actual annotations that link spatio-temporal datasets with the concepts in the ontology pattern are provided as Linked Data. In order to allow data producers to add the annotations to their datasets, we have implemented a Web portal that uses a triple store at the backend to store the annotations and to make them available in the Linked Data cloud. Furthermore, we have implemented functions in the statistical environment R to retrieve the RDF annotations and, based on these annotations, to support a stronger typing of spatio-temporal datatypes guiding towards a meaningful analysis in R. [1] Stasch, C., Scheider, S., Pebesma, E., Kuhn, W. (2014): "Meaningful spatial prediction and aggregation", Environmental Modelling & Software, 51, 149-165.

MSWEP V2 global 3-hourly 0.1° precipitation: methodology and quantitative appraisal

NASA Astrophysics Data System (ADS)

Beck, H.; Yang, L.; Pan, M.; Wood, E. F.; William, L.

2017-12-01

Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2, the first fully global gridded precipitation (P) dataset with a 0.1° spatial resolution. The dataset covers the period 1979-2016, has a 3-hourly temporal resolution, and was derived by optimally merging a wide range of data sources based on gauges (WorldClim, GHCN-D, GSOD, and others), satellites (CMORPH, GridSat, GSMaP, and TMPA 3B42RT), and reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR). MSWEP V2 implements some major improvements over V1, such as (i) the correction of distributional P biases using cumulative distribution function matching, (ii) increasing the spatial resolution from 0.25° to 0.1°, (iii) the inclusion of ocean areas, (iv) the addition of NCEP-CFSR P estimates, (v) the addition of thermal infrared-based P estimates for the pre-TRMM era, (vi) the addition of 0.1° daily interpolated gauge data, (vii) the use of a daily gauge correction scheme that accounts for regional differences in the 24-hour accumulation period of gauges, and (viii) extension of the data record to 2016. The gauge-based assessment of the reanalysis and satellite P datasets, necessary for establishing the merging weights, revealed that the reanalysis datasets strongly overestimate the P frequency for the entire globe, and that the satellite (resp. reanalysis) datasets consistently performed better at low (high) latitudes. Compared to other state-of-the-art P datasets, MSWEP V2 exhibits more plausible global patterns in mean annual P, percentiles, and annual number of dry days, and better resolves the small-scale variability over topographically complex terrain. Other P datasets appear to consistently underestimate P amounts over mountainous regions. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 959, 796, and 1026 mm/yr, respectively, in close agreement with the best previous published estimates.

National Transportation Atlas Databases : 2002

DOT National Transportation Integrated Search

2002-01-01

The National Transportation Atlas Databases 2002 (NTAD2002) is a set of nationwide geographic databases of transportation facilities, transportation networks, and associated infrastructure. These datasets include spatial information for transportatio...