Spatial autocorrelation in growth of undisturbed natural pine stands across Georgia

Treesearch

Raymond L. Czaplewski; Robin M. Reich; William A. Bechtold

1994-01-01

Moran's I statistic measures the spatial autocorrelation in a random variable measured at discrete locations in space. Permutation procedures test the null hypothesis that the observed Moran's I value is no greater than that expected by chance. The spatial autocorrelation of gross basal area increment is analyzed for undisturbed, naturally regenerated stands...

Study of trabecular bone microstructure using spatial autocorrelation analysis

NASA Astrophysics Data System (ADS)

Wald, Michael J.; Vasilic, Branimir; Saha, Punam K.; Wehrli, Felix W.

2005-04-01

The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone"s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods.

PubMed

Duncan, Dustin T; Kawachi, Ichiro; Kum, Susan; Aldstadt, Jared; Piras, Gianfranco; Matthews, Stephen A; Arbia, Giuseppe; Castro, Marcia C; White, Kellee; Williams, David R

2014-04-01

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P =0.001), for tree density (Global Moran's I =0.452, P =0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P <0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (r S =-0.19; conventional P -value=0.016; spatially adjusted P -value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (r S =-0.18; conventional P -value=0.019; spatially adjusted P -value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

PubMed

Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

2017-01-01

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species.

Accounting for spatial effects in land use regression for urban air pollution modeling.

PubMed

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

PubMed Central

2009-01-01

Background Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns. Methods In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender. Results Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships. Conclusions Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services. PMID:20003460

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Negligible influence of spatial autocorrelation in the assessment of fire effects in a mixed conifer forest

USGS Publications Warehouse

van Mantgem, P.J.; Schwilk, D.W.

2009-01-01

Fire is an important feature of many forest ecosystems, although the quantification of its effects is compromised by the large scale at which fire occurs and its inherent unpredictability. A recurring problem is the use of subsamples collected within individual burns, potentially resulting in spatially autocorrelated data. Using subsamples from six different fires (and three unburned control areas) we show little evidence for strong spatial autocorrelation either before or after burning for eight measures of forest conditions (both fuels and vegetation). Additionally, including a term for spatially autocorrelated errors provided little improvement for simple linear models contrasting the effects of early versus late season burning. While the effects of spatial autocorrelation should always be examined, it may not always greatly influence assessments of fire effects. If high patch scale variability is common in Sierra Nevada mixed conifer forests, even following more than a century of fire exclusion, treatments designed to encourage further heterogeneity in forest conditions prior to the reintroduction of fire will likely be unnecessary.

A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

PubMed Central

Duncan, Dustin T.; Kawachi, Ichiro; Kum, Susan; Aldstadt, Jared; Piras, Gianfranco; Matthews, Stephen A.; Arbia, Giuseppe; Castro, Marcia C.; White, Kellee; Williams, David R.

2017-01-01

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran’s I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran’s I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran’s I=0.452, P=0.001), and in the OLS regression residuals (Global Moran’s I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=−0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=−0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed. PMID:29354668

Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies forest: analyses using spatial autocorrelation and the mark correlation function.

PubMed

Suzuki, Satoshi N; Kachi, Naoki; Suzuki, Jun-Ichirou

2008-09-01

During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. In 1980 all trees in an 8 x 8 m plot were mapped and their height growth after the disturbance was estimated. Their mortality and growth were then recorded at 1- to 4-year intervals between 1980 and 2006. Spatial distribution patterns of trees were analysed by the pair correlation function. Spatial correlations between tree heights were analysed with a spatial autocorrelation function and the mark correlation function. The mark correlation function was able to detect a local size hierarchy that could not be detected by the spatial autocorrelation function alone. The small-scale spatial distribution pattern of trees changed from clumped to slightly regular during the 47 years. Mortality occurred in a density-dependent manner, which resulted in regular spacing between trees after 1980. The spatial autocorrelation and mark correlation functions revealed the existence of tree patches consisting of large trees at the initial stage. Development of a local size hierarchy was detected within the first decade after the disturbance, although the spatial autocorrelation was not negative. Local size hierarchies that developed persisted until 2006, and the spatial autocorrelation became negative at later stages (after about 40 years). This is the first study to detect local size hierarchies as a prelude to regular spacing using the mark correlation function. The results confirm that use of the mark correlation function together with the spatial autocorrelation function is an effective tool to analyse the development of a local size hierarchy of trees in a forest.

Effect of site level environmental variables, spatial autocorrelation and sampling intensity on arthropod communities in an ancient temperate lowland woodland area.

PubMed

Horak, Jakub

2013-01-01

The interaction of arthropods with the environment and the management of their populations is a focus of the ecological agenda. Spatial autocorrelation and under-sampling may generate bias and, when they are ignored, it is hard to determine if results can in any way be trusted. Arthropod communities were studied during two seasons and using two methods: window and panel traps, in an area of ancient temperate lowland woodland of Zebracka (Czech Republic). The composition of arthropod communities was studied focusing on four site level variables (canopy openness, diameter in the breast height and height of tree, and water distance) and finally analysed using two approaches: with and without effects of spatial autocorrelation. I found that the proportion of variance explained by space cannot be ignored (≈20% in both years). Potential bias in analyses of the response of arthropods to site level variables without including spatial co-variables is well illustrated by redundancy analyses. Inclusion of space led to more accurate results, as water distance and tree diameter were significant, showing approximately the same ratio of explained variance and direction in both seasons. Results without spatial co-variables were much more disordered and were difficult to explain. This study showed that neglecting the effects of spatial autocorrelation could lead to wrong conclusions in site level studies and, furthermore, that inclusion of space may lead to more accurate and unambiguous outcomes. Rarefactions showed that lower sampling intensity, when appropriately designed, can produce sufficient results without exploitation of the environment.

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb

2003-01-01

Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

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.

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas.

PubMed

Broms, Kristin M; Johnson, Devin S; Altwegg, Res; Conquest, Loveday L

2014-03-01

Determining the range of a species and exploring species--habitat associations are central questions in ecology and can be answered by analyzing presence--absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence--absence data set using a newly introduced R package. The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order, of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.

An investigation of turbulent transport in the extreme lower atmosphere

NASA Technical Reports Server (NTRS)

Koper, C. A., Jr.; Sadeh, W. Z.

1975-01-01

A model in which the Lagrangian autocorrelation is expressed by a domain integral over a set of usual Eulerian autocorrelations acquired concurrently at all points within a turbulence box is proposed along with a method for ascertaining the statistical stationarity of turbulent velocity by creating an equivalent ensemble to investigate the flow in the extreme lower atmosphere. Simultaneous measurements of turbulent velocity on a turbulence line along the wake axis were carried out utilizing a longitudinal array of five hot-wire anemometers remotely operated. The stationarity test revealed that the turbulent velocity is approximated as a realization of a weakly self-stationary random process. Based on the Lagrangian autocorrelation it is found that: (1) large diffusion time predominated; (2) ratios of Lagrangian to Eulerian time and spatial scales were smaller than unity; and, (3) short and long diffusion time scales and diffusion spatial scales were constrained within their Eulerian counterparts.

A geostatistical state-space model of animal densities for stream networks.

PubMed

Hocking, Daniel J; Thorson, James T; O'Neil, Kyle; Letcher, Benjamin H

2018-06-21

Population dynamics are often correlated in space and time due to correlations in environmental drivers as well as synchrony induced by individual dispersal. Many statistical analyses of populations ignore potential autocorrelations and assume that survey methods (distance and time between samples) eliminate these correlations, allowing samples to be treated independently. If these assumptions are incorrect, results and therefore inference may be biased and uncertainty under-estimated. We developed a novel statistical method to account for spatio-temporal correlations within dendritic stream networks, while accounting for imperfect detection in the surveys. Through simulations, we found this model decreased predictive error relative to standard statistical methods when data were spatially correlated based on stream distance and performed similarly when data were not correlated. We found that increasing the number of years surveyed substantially improved the model accuracy when estimating spatial and temporal correlation coefficients, especially from 10 to 15 years. Increasing the number of survey sites within the network improved the performance of the non-spatial model but only marginally improved the density estimates in the spatio-temporal model. We applied this model to Brook Trout data from the West Susquehanna Watershed in Pennsylvania collected over 34 years from 1981 - 2014. We found the model including temporal and spatio-temporal autocorrelation best described young-of-the-year (YOY) and adult density patterns. YOY densities were positively related to forest cover and negatively related to spring temperatures with low temporal autocorrelation and moderately-high spatio-temporal correlation. Adult densities were less strongly affected by climatic conditions and less temporally variable than YOY but with similar spatio-temporal correlation and higher temporal autocorrelation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Correction of Spatial Bias in Oligonucleotide Array Data

PubMed Central

Lemieux, Sébastien

2013-01-01

Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias. PMID:23573083

Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran

NASA Astrophysics Data System (ADS)

Rousta, Iman; Doostkamian, Mehdi; Haghighi, Esmaeil; Ghafarian Malamiri, Hamid Reza; Yarahmadi, Parvane

2017-09-01

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord G i statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

An Evaluation of Fractal Surface Measurement Methods for Characterizing Landscape Complexity from Remote-Sensing Imagery

NASA Technical Reports Server (NTRS)

Lam, Nina Siu-Ngan; Qiu, Hong-Lie; Quattrochi, Dale A.; Emerson, Charles W.; Arnold, James E. (Technical Monitor)

2001-01-01

The rapid increase in digital data volumes from new and existing sensors necessitates the need for efficient analytical tools for extracting information. We developed an integrated software package called ICAMS (Image Characterization and Modeling System) to provide specialized spatial analytical functions for interpreting remote sensing data. This paper evaluates the three fractal dimension measurement methods: isarithm, variogram, and triangular prism, along with the spatial autocorrelation measurement methods Moran's I and Geary's C, that have been implemented in ICAMS. A modified triangular prism method was proposed and implemented. Results from analyzing 25 simulated surfaces having known fractal dimensions show that both the isarithm and triangular prism methods can accurately measure a range of fractal surfaces. The triangular prism method is most accurate at estimating the fractal dimension of higher spatial complexity, but it is sensitive to contrast stretching. The variogram method is a comparatively poor estimator for all of the surfaces, particularly those with higher fractal dimensions. Similar to the fractal techniques, the spatial autocorrelation techniques are found to be useful to measure complex images but not images with low dimensionality. These fractal measurement methods can be applied directly to unclassified images and could serve as a tool for change detection and data mining.

Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2018-02-01

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial-temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The autocorrelation structures are characterized by spatial anisotropy, correlation distances 1.5-2.8 km and rarely exceeding 5 km, and time-correlation distances 1.8-6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques.

[Characteristics of temporal-spatial differentiation in landscape pattern vulnerability in Nansihu Lake wetland, China.

PubMed

Liang, Jia Xin; Li, Xin Ju

2018-02-01

With remote sensing images from 1985, 2000 Lantsat 5 TM and 2015 Lantsat 8 OLI as data sources, we tried to select the suitable research scale and examine the temporal-spatial diffe-rentiation with such scale in the Nansihu Lake wetland by using landscape pattern vulnerability index constructed by sensitivity index and adaptability index, and combined with space statistics such as semivariogram and spatial autocorrelation. The results showed that 1 km × 1 km equidistant grid was the suitable research scale, which could eliminate the influence of spatial heterogeneity induced by random factors. From 1985 to 2015, the landscape pattern vulnerability in the Nansihu Lake wetland deteriorated gradually. The high-risk area of landscape pattern vulnerability dramatically expanded with time. The spatial heterogeneity of landscape pattern vulnerability increased, and the influence of non-structural factors on landscape pattern vulnerability strengthened. Spatial variability affected by spatial autocorrelation slightly weakened. Landscape pattern vulnerability had strong general spatial positive correlation, with the significant form of spatial agglomeration. The positive spatial autocorrelation continued to increase and the phenomenon of spatial concentration was more and more obvious over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighboring space units. The high-high accumulation areas showed the strongest level of significance, and the significant level of low-low accumulation zone increased with time. Natural factors, such as temperature and precipitation, affected water-level and landscape distribution, and thus changed the landscape patterns vulnerability of Nansihu Lake wetland. The dominant driver for the deterioration of landscape patterns vulnerability was human activities, including social economy activity and policy system.

Environmental drivers of spatial variation in whole-tree transpiration in an aspen-dominated upland-to-wetland forest gradient

NASA Astrophysics Data System (ADS)

Loranty, Michael M.; Mackay, D. Scott; Ewers, Brent E.; Adelman, Jonathan D.; Kruger, Eric L.

2008-02-01

Assumed representative center-of-stand measurements are typical inputs to models that scale forest transpiration to stand and regional extents. These inputs do not consider gradients in transpiration at stand boundaries or along moisture gradients and therefore potentially bias the large-scale estimates. We measured half-hourly sap flux (JS) for 173 trees in a spatially explicit cyclic sampling design across a topographically controlled gradient between a forested wetland and upland forest in northern Wisconsin. Our analyses focused on three dominant species in the site: quaking aspen (Populus tremuloides Michx), speckled alder (Alnus incana (DuRoi) Spreng), and white cedar (Thuja occidentalis L.). Sapwood area (AS) was used to scale JS to whole tree transpiration (EC). Because spatial patterns imply underlying processes, geostatistical analyses were employed to quantify patterns of spatial autocorrelation across the site. A simple Jarvis type model parameterized using a Monte Carlo sampling approach was used to simulate EC (EC-SIM). EC-SIM was compared with observed EC(EC-OBS) and found to reproduce both the temporal trends and spatial variance of canopy transpiration. EC-SIM was then used to examine spatial autocorrelation as a function of environmental drivers. We found no spatial autocorrelation in JS across the gradient from forested wetland to forested upland. EC was spatially autocorrelated and this was attributed to spatial variation in AS which suggests species spatial patterns are important for understanding spatial estimates of transpiration. However, the range of autocorrelation in EC-SIM decreased linearly with increasing vapor pressure deficit, implying that consideration of spatial variation in the sensitivity of canopy stomatal conductance to D is also key to accurately scaling up transpiration in space.

[Temporal and spatial characteristics of ecological risk in Shunyi, Beijing, China based on landscape structure.

PubMed

Qing, Feng Ting; Peng, Yu

2016-05-01

Based on the remote sensing data in 1997, 2001, 2005, 2009 and 2013, this article classified the landscape types of Shunyi, and the ecological risk index was built based on landscape disturbance index and landscape fragility. The spatial auto-correlation and geostatistical analysis by GS + and ArcGIS was used to study temporal and spatial changes of ecological risk. The results showed that eco-risk degree in the study region had positive spatial correlation which decreased with the increasing grain size. Within a certain grain range (<12 km), the spatial auto-correlation had an obvious dependence on scale. The random variation of spatial heterogeneity was less than spatial auto-correlation variation from 1997 to 2013, which meant the auto-correlation had a dominant role in spatial heterogeneity. The ecological risk of Shunyi was mainly at moderate level during the study period. The area of the district with higher and lower ecological risk increased, while that of mode-rate ecological risk decreased. The area with low ecological risk was mainly located in the airport region and forest of southeast Shunyi, while that with high ecological risk was mainly concentrated in the water landscape, such as the banks of Chaobai River.

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Treesearch

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

PubMed Central

Aldstadt, Jared; Whalen, John; White, Kellee; Castro, Marcia C.; Williams, David R.

2017-01-01

BACKGROUND Multiple and varied benefits have been suggested for increased neighborhood walkability. However, spatial inequalities in neighborhood walkability likely exist and may be attributable, in part, to residential segregation. OBJECTIVE Utilizing a spatial demographic perspective, we evaluated potential spatial inequalities in walkable neighborhood amenities across census tracts in Boston, MA (US). METHODS The independent variables included minority racial/ethnic population percentages and percent of families in poverty. Walkable neighborhood amenities were assessed with a composite measure. Spatial autocorrelation in key study variables were first calculated with the Global Moran’s I statistic. Then, Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were calculated as well as Spearman correlations accounting for spatial autocorrelation. We fit ordinary least squares (OLS) regression and spatial autoregressive models, when appropriate, as a final step. RESULTS Significant positive spatial autocorrelation was found in neighborhood socio-demographic characteristics (e.g. census tract percent Black), but not walkable neighborhood amenities or in the OLS regression residuals. Spearman correlations between neighborhood socio-demographic characteristics and walkable neighborhood amenities were not statistically significant, nor were neighborhood socio-demographic characteristics significantly associated with walkable neighborhood amenities in OLS regression models. CONCLUSIONS Our results suggest that there is residential segregation in Boston and that spatial inequalities do not necessarily show up using a composite measure. COMMENTS Future research in other geographic areas (including international contexts) and using different definitions of neighborhoods (including small-area definitions) should evaluate if spatial inequalities are found using composite measures but also should use measures of specific neighborhood amenities. PMID:29046612

Using geographical semi-variogram method to quantify the difference between NO2 and PM2.5 spatial distribution characteristics in urban areas.

PubMed

Song, Weize; Jia, Haifeng; Li, Zhilin; Tang, Deliang

2018-08-01

Urban air pollutant distribution is a concern in environmental and health studies. Particularly, the spatial distribution of NO 2 and PM 2.5 , which represent photochemical smog and haze pollution in urban areas, is of concern. This paper presents a study quantifying the seasonal differences between urban NO 2 and PM 2.5 distributions in Foshan, China. A geographical semi-variogram analysis was conducted to delineate the spatial variation in daily NO 2 and PM 2.5 concentrations. The data were collected from 38 sites in the government-operated monitoring network. The results showed that the total spatial variance of NO 2 is 38.5% higher than that of PM 2.5 . The random spatial variance of NO 2 was 1.6 times than that of PM 2.5 . The nugget effect (i.e., random to total spatial variance ratio) values of NO 2 and PM 2.5 were 29.7 and 20.9%, respectively. This indicates that urban NO 2 distribution was affected by both local and regional influencing factors, while urban PM 2.5 distribution was dominated by regional influencing factors. NO 2 had a larger seasonally averaged spatial autocorrelation distance (48km) than that of PM 2.5 (33km). The spatial range of NO 2 autocorrelation was larger in winter than the other seasons, and PM 2.5 has a smaller range of spatial autocorrelation in winter than the other seasons. Overall, the geographical semi-variogram analysis is a very effective method to enrich the understanding of NO 2 and PM 2.5 distributions. It can provide scientific evidences for the buffering radius selection of spatial predictors for land use regression models. It will also be beneficial for developing the targeted policies and measures to reduce NO 2 and PM 2.5 pollution levels. Copyright © 2018 Elsevier B.V. All rights reserved.

Disentangling the effects of forage, social rank, and risk on movement autocorrelation of elephants using Fourier and wavelet analyses

PubMed Central

Wittemyer, George; Polansky, Leo; Douglas-Hamilton, Iain; Getz, Wayne M.

2008-01-01

The internal state of an individual—as it relates to thirst, hunger, fear, or reproductive drive—can be inferred by referencing points on its movement path to external environmental and sociological variables. Using time-series approaches to characterize autocorrelative properties of step-length movements collated every 3 h for seven free-ranging African elephants, we examined the influence of social rank, predation risk, and seasonal variation in resource abundance on periodic properties of movement. The frequency domain methods of Fourier and wavelet analyses provide compact summaries of temporal autocorrelation and show both strong diurnal and seasonal based periodicities in the step-length time series. This autocorrelation is weaker during the wet season, indicating random movements are more common when ecological conditions are good. Periodograms of socially dominant individuals are consistent across seasons, whereas subordinate individuals show distinct differences diverging from that of dominants during the dry season. We link temporally localized statistical properties of movement to landscape features and find that diurnal movement correlation is more common within protected wildlife areas, and multiday movement correlations found among lower ranked individuals are typically outside of protected areas where predation risks are greatest. A frequency-related spatial analysis of movement-step lengths reveal that rest cycles related to the spatial distribution of critical resources (i.e., forage and water) are responsible for creating the observed patterns. Our approach generates unique information regarding the spatial-temporal interplay between environmental and individual characteristics, providing an original approach for understanding the movement ecology of individual animals and the spatial organization of animal populations. PMID:19060207

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

NASA Technical Reports Server (NTRS)

Mcgwire, K.; Friedl, M.; Estes, J. E.

1993-01-01

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Effect of the spatial autocorrelation of empty sites on the evolution of cooperation

NASA Astrophysics Data System (ADS)

Zhang, Hui; Wang, Li; Hou, Dongshuang

2016-02-01

An evolutionary game model is constructed to investigate the spatial autocorrelation of empty sites on the evolution of cooperation. Each individual is assumed to imitate the strategy of the one who scores the highest in its neighborhood including itself. Simulation results illustrate that the evolutionary dynamics based on the Prisoner's Dilemma game (PD) depends severely on the initial conditions, while the Snowdrift game (SD) is hardly affected by that. A high degree of autocorrelation of empty sites is beneficial for the evolution of cooperation in the PD, whereas it shows diversification effects depending on the parameter of temptation to defect in the SD. Moreover, for the repeated game with three strategies, 'always defect' (ALLD), 'tit-for-tat' (TFT), and 'always cooperate' (ALLC), simulations reveal that an amazing evolutionary diversity appears for varying of parameters of the temptation to defect and the probability of playing in the next round of the game. The spatial autocorrelation of empty sites can have profound effects on evolutionary dynamics (equilibrium and oscillation) and spatial distribution.

Extension of the spatial autocorrelation (SPAC) method to mixed-component correlations of surface waves

USGS Publications Warehouse

Haney, Matthew M.; Mikesell, T. Dylan; van Wijk, Kasper; Nakahara, Hisashi

2012-01-01

Using ambient seismic noise for imaging subsurface structure dates back to the development of the spatial autocorrelation (SPAC) method in the 1950s. We present a theoretical analysis of the SPAC method for multicomponent recordings of surface waves to determine the complete 3 × 3 matrix of correlations between all pairs of three-component motions, called the correlation matrix. In the case of isotropic incidence, when either Rayleigh or Love waves arrive from all directions with equal power, the only non-zero off-diagonal terms in the matrix are the vertical–radial (ZR) and radial–vertical (RZ) correlations in the presence of Rayleigh waves. Such combinations were not considered in the development of the SPAC method. The method originally addressed the vertical–vertical (ZZ), RR and TT correlations, hence the name spatial autocorrelation. The theoretical expressions we derive for the ZR and RZ correlations offer additional ways to measure Rayleigh wave dispersion within the SPAC framework. Expanding on the results for isotropic incidence, we derive the complete correlation matrix in the case of generally anisotropic incidence. We show that the ZR and RZ correlations have advantageous properties in the presence of an out-of-plane directional wavefield compared to ZZ and RR correlations. We apply the results for mixed-component correlations to a data set from Akutan Volcano, Alaska and find consistent estimates of Rayleigh wave phase velocity from ZR compared to ZZ correlations. This work together with the recently discovered connections between the SPAC method and time-domain correlations of ambient noise provide further insights into the retrieval of surface wave Green’s functions from seismic noise.

ASSESSMENT OF SPATIAL AUTOCORRELATION IN EMPIRICAL MODELS IN ECOLOGY

EPA Science Inventory

Statistically assessing ecological models is inherently difficult because data are autocorrelated and this autocorrelation varies in an unknown fashion. At a simple level, the linking of a single species to a habitat type is a straightforward analysis. With some investigation int...

Spatial regression methods capture prediction uncertainty in species distribution model projections through time

Treesearch

Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz

2013-01-01

The uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to...

[Spatial distribution of occupational disease prevalence in Guangzhou and Foshan city by geographic information system].

PubMed

Tan, Q; Tu, H W; Gu, C H; Li, X D; Li, R Z; Wang, M; Chen, S G; Cheng, Y J; Liu, Y M

2017-11-20

Objective: To explore the occupational disease spatial distribution characteristics in Guangzhou and Foshan city in 2006-2013 with Geographic Information System and to provide evidence for making control strategy. Methods: The data on occupational disease diagnosis in Guangzhou and Foshan city from 2006 through 2013 were collected and linked to the digital map at administrative county level with Arc GIS12.0 software for spatial analysis. Results: The maps of occupational disease and Moran's spatial autocor-relation analysis showed that the spatial aggregation existed in Shunde and Nanhai region with Moran's index 1.727, -0.003. Local Moran's I spatial autocorrelation analysis pointed out the "positive high incidence re-gion" and the "negative high incidence region" during 2006~2013. Trend analysis showed that the diagnosis case increased slightly then declined from west to east, increase obviously from north to south, declined from? southwest to northeast, high in the middle and low on both sides in northwest-southeast direction. Conclusions: The occupational disease is obviously geographical distribution in Guangzhou and Foshan city. The corresponding prevention measures should be made according to the geographical distribution.

Global Autocorrelation Scales of the Partial Pressure of Oceanic CO2

NASA Technical Reports Server (NTRS)

Li, Zhen; Adamec, David; Takahashi, Taro; Sutherland, Stewart C.

2004-01-01

A global database of approximately 1.7 million observations of the partial pressure of carbon dioxide in surface ocean waters (pCO2) collected between 1970 and 2003 is used to estimate its spatial autocorrelation structure. The patterns of the lag distance where the autocorrelation exceeds 0.8 is similar to patterns in the spatial distribution of the first baroclinic Rossby radius of deformation indicating that ocean circulation processes play a significant role in determining the spatial variability of pCO2. For example, the global maximum of the distance at which autocorrelations exceed 0.8 averages about 140 km in the equatorial Pacific. Also, the lag distance at which the autocorrelation exceed 0.8 is greater in the vicinity of the Gulf Stream than it is near the Kuroshio, approximately 50 km near the Gulf Stream as opposed to 20 km near the Kuroshio. Separate calculations for times when the sun is north and south of the equator revealed no obvious seasonal dependence of the spatial autocorrelation scales. The pCO2 measurements at Ocean Weather Station (OWS) 'P', in the eastern subarctic Pacific (50 N, 145 W) is the only fixed location where an uninterrupted time series of sufficient length exists to calculate a meaningful temporal autocorrelation function for lags greater than a few days. The estimated temporal autocorrelation function at OWS 'P', is highly variable. A spectral analysis of the longest four pCO2 time series indicates a high level of variability occurring over periods from the atmospheric synoptic to the maximum length of the time series, in this case 42 days. It is likely that a relative peak in variability with a period of 3-6 days is related to atmospheric synoptic period variability and ocean mixing events due to wind stirring. However, the short length of available time series makes identifying temporal relationships between pCO2 and atmospheric or ocean processes problematic.

Spatial Autocorrelation Can Generate Stronger Correlations between Range Size and Climatic Niches Than the Biological Signal - A Demonstration Using Bird and Mammal Range Maps.

PubMed

Boucher-Lalonde, Véronique; Currie, David J

2016-01-01

Species' geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species' ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species' climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species' ranges, are correlations between species' range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species' realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated.

Spatial Autocorrelation Can Generate Stronger Correlations between Range Size and Climatic Niches Than the Biological Signal — A Demonstration Using Bird and Mammal Range Maps

PubMed Central

Boucher-Lalonde, Véronique; Currie, David J.

2016-01-01

Species’ geographic ranges could primarily be physiological tolerances drawn in space. Alternatively, geographic ranges could be only broadly constrained by physiological climatic tolerances: there could generally be much more proximate constraints on species’ ranges (dispersal limitation, biotic interactions, etc.) such that species often occupy a small and unpredictable subset of tolerable climates. In the literature, species’ climatic tolerances are typically estimated from the set of conditions observed within their geographic range. Using this method, studies have concluded that broader climatic niches permit larger ranges. Similarly, other studies have investigated the biological causes of incomplete range filling. But, when climatic constraints are measured directly from species’ ranges, are correlations between species’ range size and climate necessarily consistent with a causal link? We evaluated the extent to which variation in range size among 3277 bird and 1659 mammal species occurring in the Americas is statistically related to characteristics of species’ realized climatic niches. We then compared how these relationships differed from the ones expected in the absence of a causal link. We used a null model that randomizes the predictor variables (climate), while retaining their broad spatial autocorrelation structure, thereby removing any causal relationship between range size and climate. We found that, although range size is strongly positively related to climatic niche breadth, range filling and, to a lesser extent, niche position in nature, the observed relationships are not always stronger than expected from spatial autocorrelation alone. Thus, we conclude that equally strong relationships between range size and climate would result from any processes causing ranges to be highly spatially autocorrelated. PMID:27855201

Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

NASA Astrophysics Data System (ADS)

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

2018-04-01

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

NASA Astrophysics Data System (ADS)

Smith, Tony E.; Lee, Ka Lok

2012-01-01

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.

Characterizing groundwater quality ranks for drinking purposes in Sylhet district, Bangladesh, using entropy method, spatial autocorrelation index, and geostatistics.

PubMed

Islam, Abu Reza Md Towfiqul; Ahmed, Nasir; Bodrud-Doza, Md; Chu, Ronghao

2017-12-01

Drinking water is susceptible to the poor quality of contaminated water affecting the health of humans. Thus, it is an essential study to investigate factors affecting groundwater quality and its suitability for drinking uses. In this paper, the entropy theory, multivariate statistics, spatial autocorrelation index, and geostatistics are applied to characterize groundwater quality and its spatial variability in the Sylhet district of Bangladesh. A total of 91samples have been collected from wells (e.g., shallow, intermediate, and deep tube wells at 15-300-m depth) from the study area. The results show that NO 3 - , then SO 4 2- , and As are the most contributed parameters influencing the groundwater quality according to the entropy theory. The principal component analysis (PCA) and correlation coefficient also confirm the results of the entropy theory. However, Na + has the highest spatial autocorrelation and the most entropy, thus affecting the groundwater quality. Based on the entropy-weighted water quality index (EWQI) and groundwater quality index (GWQI) classifications, it is observed that 60.45 and 53.86% of water samples are classified as having an excellent to good qualities, while the remaining samples vary from medium to extremely poor quality domains for drinking purposes. Furthermore, the EWQI classification provides the more reasonable results than GWQIs due to its simplicity, accuracy, and ignoring of artificial weight. A Gaussian semivariogram model has been chosen to the best fit model, and groundwater quality indices have a weak spatial dependence, suggesting that both geogenic and anthropogenic factors play a pivotal role in spatial heterogeneity of groundwater quality oscillations.

Decorrelation scales for Arctic Ocean hydrography - Part I: Amerasian Basin

NASA Astrophysics Data System (ADS)

Sumata, Hiroshi; Kauker, Frank; Karcher, Michael; Rabe, Benjamin; Timmermans, Mary-Louise; Behrendt, Axel; Gerdes, Rüdiger; Schauer, Ursula; Shimada, Koji; Cho, Kyoung-Ho; Kikuchi, Takashi

2018-03-01

Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Amerasian Basin of the Arctic Ocean. For this purpose, we compile historical measurements from 1980 to 2015. Assuming spatial and temporal homogeneity of the decorrelation scale in the basin interior (abyssal plain area), we calculate autocorrelations as a function of spatial distance and temporal lag. The examination of the functional form of autocorrelation in each depth range reveals that the autocorrelation is well described by a Gaussian function in space and time. We derive decorrelation scales of 150-200 km in space and 100-300 days in time. These scales are directly applicable to quantify the representation error, which is essential for use of ocean in situ measurements in data assimilation. We also describe how the estimated autocorrelation function and decorrelation scale should be applied for cost function calculation in a data assimilation system.

Field test comparison of an autocorrelation technique for determining grain size using a digital 'beachball' camera versus traditional methods

USGS Publications Warehouse

Barnard, P.L.; Rubin, D.M.; Harney, J.; Mustain, N.

2007-01-01

This extensive field test of an autocorrelation technique for determining grain size from digital images was conducted using a digital bed-sediment camera, or 'beachball' camera. Using 205 sediment samples and >1200 images from a variety of beaches on the west coast of the US, grain size ranging from sand to granules was measured from field samples using both the autocorrelation technique developed by Rubin [Rubin, D.M., 2004. A simple autocorrelation algorithm for determining grain size from digital images of sediment. Journal of Sedimentary Research, 74(1): 160-165.] and traditional methods (i.e. settling tube analysis, sieving, and point counts). To test the accuracy of the digital-image grain size algorithm, we compared results with manual point counts of an extensive image data set in the Santa Barbara littoral cell. Grain sizes calculated using the autocorrelation algorithm were highly correlated with the point counts of the same images (r2 = 0.93; n = 79) and had an error of only 1%. Comparisons of calculated grain sizes and grain sizes measured from grab samples demonstrated that the autocorrelation technique works well on high-energy dissipative beaches with well-sorted sediment such as in the Pacific Northwest (r2 ??? 0.92; n = 115). On less dissipative, more poorly sorted beaches such as Ocean Beach in San Francisco, results were not as good (r2 ??? 0.70; n = 67; within 3% accuracy). Because the algorithm works well compared with point counts of the same image, the poorer correlation with grab samples must be a result of actual spatial and vertical variability of sediment in the field; closer agreement between grain size in the images and grain size of grab samples can be achieved by increasing the sampling volume of the images (taking more images, distributed over a volume comparable to that of a grab sample). In all field tests the autocorrelation method was able to predict the mean and median grain size with ???96% accuracy, which is more than adequate for the majority of sedimentological applications, especially considering that the autocorrelation technique is estimated to be at least 100 times faster than traditional methods.

The spatial structure of chronic morbidity: evidence from UK census returns.

PubMed

Dutey-Magni, Peter F; Moon, Graham

2016-08-24

Disease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity. 2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power. The odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions. Systematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.

SPATIALLY AUTOCORRELATED DEMOGRAPHY AND INTERPOND MIGRATION IN THE CALIFORNIA TIGER SALAMANDER (AMBYSTOME CALIFORNIENSE)

EPA Science Inventory

We investigated the metapopulation structure of the California tiger salamander (Ambystoma californiense) using a combination of indirect and direct methods to evaluate two key requirements of modern metapopulation models: 1) that patches support somewhat independent populations ...

The Spatial Distribution of Adult Obesity Prevalence in Denver County, Colorado: An Empirical Bayes Approach to Adjust EHR-Derived Small Area Estimates.

PubMed

Tabano, David C; Bol, Kirk; Newcomer, Sophia R; Barrow, Jennifer C; Daley, Matthew F

2017-12-06

Measuring obesity prevalence across geographic areas should account for environmental and socioeconomic factors that contribute to spatial autocorrelation, the dependency of values in estimates across neighboring areas, to mitigate the bias in measures and risk of type I errors in hypothesis testing. Dependency among observations across geographic areas violates statistical independence assumptions and may result in biased estimates. Empirical Bayes (EB) estimators reduce the variability of estimates with spatial autocorrelation, which limits the overall mean square-error and controls for sample bias. Using the Colorado Body Mass Index (BMI) Monitoring System, we modeled the spatial autocorrelation of adult (≥ 18 years old) obesity (BMI ≥ 30 kg m 2 ) measurements using patient-level electronic health record data from encounters between January 1, 2009, and December 31, 2011. Obesity prevalence was estimated among census tracts with >=10 observations in Denver County census tracts during the study period. We calculated the Moran's I statistic to test for spatial autocorrelation across census tracts, and mapped crude and EB obesity prevalence across geographic areas. In Denver County, there were 143 census tracts with 10 or more observations, representing a total of 97,710 adults with a valid BMI. The crude obesity prevalence for adults in Denver County was 29.8 percent (95% CI 28.4-31.1%) and ranged from 12.8 to 45.2 percent across individual census tracts. EB obesity prevalence was 30.2 percent (95% CI 28.9-31.5%) and ranged from 15.3 to 44.3 percent across census tracts. Statistical tests using the Moran's I statistic suggest adult obesity prevalence in Denver County was distributed in a non-random pattern. Clusters of EB obesity estimates were highly significant (alpha=0.05) in neighboring census tracts. Concentrations of obesity estimates were primarily in the west and north in Denver County. Statistical tests reveal adult obesity prevalence exhibit spatial autocorrelation in Denver County at the census tract level. EB estimates for obesity prevalence can be used to control for spatial autocorrelation between neighboring census tracts and may produce less biased estimates of obesity prevalence.

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

PubMed

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.

Spatial design and strength of spatial signal: Effects on covariance estimation

USGS Publications Warehouse

Irvine, Kathryn M.; Gitelman, Alix I.; Hoeting, Jennifer A.

2007-01-01

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.

A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014

PubMed Central

Dong, Wen; Yang, Kun; Xu, Quan-Li; Yang, Yu-Lian

2015-01-01

This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. PMID:26633446

Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

PubMed

Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

2017-08-07

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.

Land cover mapping at sub-pixel scales

NASA Astrophysics Data System (ADS)

Makido, Yasuyo Kato

One of the biggest drawbacks of land cover mapping from remotely sensed images relates to spatial resolution, which determines the level of spatial details depicted in an image. Fine spatial resolution images from satellite sensors such as IKONOS and QuickBird are now available. However, these images are not suitable for large-area studies, since a single image is very small and therefore it is costly for large area studies. Much research has focused on attempting to extract land cover types at sub-pixel scale, and little research has been conducted concerning the spatial allocation of land cover types within a pixel. This study is devoted to the development of new algorithms for predicting land cover distribution using remote sensory imagery at sub-pixel level. The "pixel-swapping" optimization algorithm, which was proposed by Atkinson for predicting sub-pixel land cover distribution, is investigated in this study. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation, are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. Two different simulation models were employed to develop spatially autocorrelated binary class maps. In all tested models, Gaussian, Exponential, and IDW, the pixel swapping method improved classification accuracy compared with the initial random allocation of sub-pixels. However the results suggested that equal weight could be used to increase accuracy and sub-pixel spatial autocorrelation instead of using these more complex models of spatial structure. New algorithms for modeling the spatial distribution of multiple land cover classes at sub-pixel scales are developed and evaluated. Three methods are examined: sequential categorical swapping, simultaneous categorical swapping, and simulated annealing. These three methods are applied to classified Landsat ETM+ data that has been resampled to 210 meters. The result suggested that the simultaneous method can be considered as the optimum method in terms of accuracy performance and computation time. The case study employs remote sensing imagery at the following sites: tropical forests in Brazil and temperate multiple land mosaic in East China. Sub-areas for both sites are used to examine how the characteristics of the landscape affect the ability of the optimum technique. Three types of measurement: Moran's I, mean patch size (MPS), and patch size standard deviation (STDEV), are used to characterize the landscape. All results suggested that this technique could increase the classification accuracy more than traditional hard classification. The methods developed in this study can benefit researchers who employ coarse remote sensing imagery but are interested in detailed landscape information. In many cases, the satellite sensor that provides large spatial coverage has insufficient spatial detail to identify landscape patterns. Application of the super-resolution technique described in this dissertation could potentially solve this problem by providing detailed land cover predictions from the coarse resolution satellite sensor imagery.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts.

PubMed

Crase, Beth; Liedloff, Adam; Vesk, Peter A; Fukuda, Yusuke; Wintle, Brendan A

2014-08-01

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions. © 2014 John Wiley & Sons Ltd.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in chinook salmon

USGS Publications Warehouse

Neville, Helen; Isaak, Daniel; Dunham, J.B.; Thurow, Russel; Rieman, B.

2006-01-01

Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of fine-scale genetic structuring due to the spatial clustering of related individuals on spawning grounds. Thus, we explored the spatial resolution of natal homing using genetic associations among individual Chinook salmon (Oncorhynchus tshawytscha) in an interconnected stream network. We also investigated the relationship between genetic patterns and two factors hypothesized to influence natal homing and localized movements at finer scales in this species, localized patterns in the distribution of spawning gravels and sex. Spatial autocorrelation analyses showed that spawning locations in both sub-basins of our study site were spatially clumped, but the upper sub-basin generally had a larger spatial extent and continuity of redd locations than the lower sub-basin, where the distribution of redds and associated habitat conditions were more patchy. Male genotypes were not autocorrelated at any spatial scale in either sub-basin. Female genotypes showed significant spatial autocorrelation and genetic patterns for females varied in the direction predicted between the two sub-basins, with much stronger autocorrelation in the sub-basin with less continuity in spawning gravels. The patterns observed here support predictions about differential constraints and breeding tactics between the two sexes and the potential for fine-scale habitat structure to influence the precision of natal homing and localized movements of individual Chinook salmon on their breeding grounds.

GIS-based regionalized life cycle assessment: how big is small enough? Methodology and case study of electricity generation.

PubMed

Mutel, Christopher L; Pfister, Stephan; Hellweg, Stefanie

2012-01-17

We describe a new methodology for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. We extend standard matrix-based calculations to include matrices that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. We demonstrate these techniques on electricity production in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calculations, and provide specific guidance for future improvements of inventory data sets and impact assessment methods.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

PubMed

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

PubMed Central

Cao, Chunxiang; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases. PMID:27597972

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

PubMed Central

Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J

2009-01-01

Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. Results By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. Conclusion An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity. PMID:19772590

The geography of recreational open space: influence of neighborhood racial composition and neighborhood poverty.

PubMed

Duncan, Dustin T; Kawachi, Ichiro; White, Kellee; Williams, David R

2013-08-01

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood socio-demographic characteristics (all p value = 0.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I = 0.082; p value = 0.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S = -0.22; conventional p value = 0.005; spatially adjusted p value = 0.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (>60 % non-Hispanic Black in a census tract) and recreational open space density (r S = -0.23; conventional p value = 0.003; spatially adjusted p value = 0.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (p value < 0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults

PubMed Central

Gartner, Danielle R.; Taber, Daniel R.; Hirsch, Jana A.; Robinson, Whitney R.

2016-01-01

Purpose While obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race/ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence. Methods Data from the Centers for Disease Control and Prevention’s 2013 Behavioral Risk Factor Surveillance System (BRFSS) were used to calculate state-specific obesity prevalence and gender inequality measures. Global and Local Moran’s Indices were calculated to determine spatial autocorrelation. Results Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score=4.89, p-value <0.001). Local Moran’s Indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the U.S. Conclusion Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality. PMID:27039046

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage

USGS Publications Warehouse

Mattsson, Brady J.; Zipkin, Elise F.; Gardner, Beth; Blank, Peter J.; Sauer, John R.; Royle, J. Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Explaining local-scale species distributions: relative contributions of spatial autocorrelation and landscape heterogeneity for an avian assemblage.

PubMed

Mattsson, Brady J; Zipkin, Elise F; Gardner, Beth; Blank, Peter J; Sauer, John R; Royle, J Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition.

Explaining Local-Scale Species Distributions: Relative Contributions of Spatial Autocorrelation and Landscape Heterogeneity for an Avian Assemblage

PubMed Central

Mattsson, Brady J.; Zipkin, Elise F.; Gardner, Beth; Blank, Peter J.; Sauer, John R.; Royle, J. Andrew

2013-01-01

Understanding interactions between mobile species distributions and landcover characteristics remains an outstanding challenge in ecology. Multiple factors could explain species distributions including endogenous evolutionary traits leading to conspecific clustering and endogenous habitat features that support life history requirements. Birds are a useful taxon for examining hypotheses about the relative importance of these factors among species in a community. We developed a hierarchical Bayes approach to model the relationships between bird species occupancy and local landcover variables accounting for spatial autocorrelation, species similarities, and partial observability. We fit alternative occupancy models to detections of 90 bird species observed during repeat visits to 316 point-counts forming a 400-m grid throughout the Patuxent Wildlife Research Refuge in Maryland, USA. Models with landcover variables performed significantly better than our autologistic and null models, supporting the hypothesis that local landcover heterogeneity is important as an exogenous driver for species distributions. Conspecific clustering alone was a comparatively poor descriptor of local community composition, but there was evidence for spatial autocorrelation in all species. Considerable uncertainty remains whether landcover combined with spatial autocorrelation is most parsimonious for describing bird species distributions at a local scale. Spatial structuring may be weaker at intermediate scales within which dispersal is less frequent, information flows are localized, and landcover types become spatially diversified and therefore exhibit little aggregation. Examining such hypotheses across species assemblages contributes to our understanding of community-level associations with conspecifics and landscape composition. PMID:23393564

Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression

PubMed Central

Chen, Yanguang

2016-01-01

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. PMID:26800271

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

PubMed

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

PubMed

Jacob, Benjamin J; Krapp, Fiorella; Ponce, Mario; Gottuzzo, Eduardo; Griffith, Daniel A; Novak, Robert J

2010-05-01

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle.

PubMed

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-02-26

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions.

Computationally Efficient 2D DOA Estimation with Uniform Rectangular Array in Low-Grazing Angle

PubMed Central

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Sun, Fenggang; Xiao, Yu

2017-01-01

In this paper, we propose a computationally efficient spatial differencing matrix set (SDMS) method for two-dimensional direction of arrival (2D DOA) estimation with uniform rectangular arrays (URAs) in a low-grazing angle (LGA) condition. By rearranging the auto-correlation and cross-correlation matrices in turn among different subarrays, the SDMS method can estimate the two parameters independently with one-dimensional (1D) subspace-based estimation techniques, where we only perform difference for auto-correlation matrices and the cross-correlation matrices are kept completely. Then, the pair-matching of two parameters is achieved by extracting the diagonal elements of URA. Thus, the proposed method can decrease the computational complexity, suppress the effect of additive noise and also have little information loss. Simulation results show that, in LGA, compared to other methods, the proposed methods can achieve performance improvement in the white or colored noise conditions. PMID:28245634

Measurement and data processing approach for detecting anisotropic spatial statistics of the turbulence-induced index of refraction fluctuations in the upper atmosphere.

PubMed

Havens, Timothy C; Roggemann, Michael C; Schulz, Timothy J; Brown, Wade W; Beyer, Jeff T; Otten, L John

2002-05-20

We discuss a method of data reduction and analysis that has been developed for a novel experiment to detect anisotropic turbulence in the tropopause and to measure the spatial statistics of these flows. The experimental concept is to make measurements of temperature at 15 points on a hexagonal grid for altitudes from 12,000 to 18,000 m while suspended from a balloon performing a controlled descent. From the temperature data, we estimate the index of refraction and study the spatial statistics of the turbulence-induced index of refraction fluctuations. We present and evaluate the performance of a processing approach to estimate the parameters of an anisotropic model for the spatial power spectrum of the turbulence-induced index of refraction fluctuations. A Gaussian correlation model and a least-squares optimization routine are used to estimate the parameters of the model from the measurements. In addition, we implemented a quick-look algorithm to have a computationally nonintensive way of viewing the autocorrelation function of the index fluctuations. The autocorrelation of the index of refraction fluctuations is binned and interpolated onto a uniform grid from the sparse points that exist in our experiment. This allows the autocorrelation to be viewed with a three-dimensional plot to determine whether anisotropy exists in a specific data slab. Simulation results presented here show that, in the presence of the anticipated levels of measurement noise, the least-squares estimation technique allows turbulence parameters to be estimated with low rms error.

Spatial Distribution of Oak Mistletoe as It Relates to Habits of Oak Woodland Frugivores

PubMed Central

Wilson, Ethan A.; Sullivan, Patrick J.; Dickinson, Janis L.

2014-01-01

This study addresses the underlying spatial distribution of oak mistletoe, Phoradendron villosum, a hemi-parasitic plant that provides a continuous supply of berries for frugivorous birds overwintering the oak savanna habitat of California's outer coast range. As the winter community of birds consuming oak mistletoe varies from group-living territorial species to birds that roam in flocks, we asked if mistletoe volume was spatially autocorrelated at the scale of persistent territories or whether the patterns predicted by long-term territory use by western bluebirds are overcome by seed dispersal by more mobile bird species. The abundance of mistletoe was mapped on trees within a 700 ha study site in Carmel Valley, California. Spatial autocorrelation of mistletoe volume was analyzed using the variogram method and spatial distribution of oak mistletoe trees was analyzed using Ripley's K and O-ring statistics. On a separate set of 45 trees, mistletoe volume was highly correlated with the volume of female, fruit-bearing plants, indicating that overall mistletoe volume is a good predictor of fruit availability. Variogram analysis showed that mistletoe volume was spatially autocorrelated up to approximately 250 m, a distance consistent with persistent territoriality of western bluebirds and philopatry of sons, which often breed next door to their parents and are more likely to remain home when their parents have abundant mistletoe. Using Ripley's K and O-ring analyses, we showed that mistletoe trees were aggregated for distances up to 558 m, but for distances between 558 to 724 m the O-ring analysis deviated from Ripley's K in showing repulsion rather than aggregation. While trees with mistletoe were aggregated at larger distances, mistletoe was spatially correlated at a smaller distance, consistent with what is expected based on persistent group territoriality of western bluebirds in winter and the extreme philopatry of their sons. PMID:25389971

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

A spatial analysis of social and economic determinants of tuberculosis in Brazil.

PubMed

Harling, Guy; Castro, Marcia C

2014-01-01

We investigated the spatial distribution, and social and economic correlates, of tuberculosis in Brazil between 2002 and 2009 using municipality-level age/sex-standardized tuberculosis notification data. Rates were very strongly spatially autocorrelated, being notably high in urban areas on the eastern seaboard and in the west of the country. Non-spatial ecological regression analyses found higher rates associated with urbanicity, population density, poor economic conditions, household crowding, non-white population and worse health and healthcare indicators. These associations remained in spatial conditional autoregressive models, although the effect of poverty appeared partially confounded by urbanicity, race and spatial autocorrelation, and partially mediated by household crowding. Our analysis highlights both the multiple relationships between socioeconomic factors and tuberculosis in Brazil, and the importance of accounting for spatial factors in analysing socioeconomic determinants of tuberculosis. © 2013 Published by Elsevier Ltd.

Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop-raiding.

PubMed

Songhurst, Anna; Coulson, Tim

2014-03-01

Few universal trends in spatial patterns of wildlife crop-raiding have been found. Variations in wildlife ecology and movements, and human spatial use have been identified as causes of this apparent unpredictability. However, varying spatial patterns of spatial autocorrelation (SA) in human-wildlife conflict (HWC) data could also contribute. We explicitly explore the effects of SA on wildlife crop-raiding data in order to facilitate the design of future HWC studies. We conducted a comparative survey of raided and nonraided fields to determine key drivers of crop-raiding. Data were subsampled at different spatial scales to select independent raiding data points. The model derived from all data was fitted to subsample data sets. Model parameters from these models were compared to determine the effect of SA. Most methods used to account for SA in data attempt to correct for the change in P-values; yet, by subsampling data at broader spatial scales, we identified changes in regression estimates. We consequently advocate reporting both model parameters across a range of spatial scales to help biological interpretation. Patterns of SA vary spatially in our crop-raiding data. Spatial distribution of fields should therefore be considered when choosing the spatial scale for analyses of HWC studies. Robust key drivers of elephant crop-raiding included raiding history of a field and distance of field to a main elephant pathway. Understanding spatial patterns and determining reliable socio-ecological drivers of wildlife crop-raiding is paramount for designing mitigation and land-use planning strategies to reduce HWC. Spatial patterns of HWC are complex, determined by multiple factors acting at more than one scale; therefore, studies need to be designed with an understanding of the effects of SA. Our methods are accessible to a variety of practitioners to assess the effects of SA, thereby improving the reliability of conservation management actions.

Spatial distribution of vehicle emission inventories in the Federal District, Brazil

NASA Astrophysics Data System (ADS)

Réquia, Weeberb João; Koutrakis, Petros; Roig, Henrique Llacer

2015-07-01

Air pollution poses an important public health risk, especially in large urban areas. Information about the spatial distribution of air pollutants can be used as a tool for developing public policies to reduce source emissions. Air pollution monitoring networks provide information about pollutant concentrations; however, they are not available in every urban area. Among the 5570 cities in Brazil, for example, only 1.7% of them have air pollution monitoring networks. In this study we assess vehicle emissions for main traffic routes of the Federal District (state of Brazil) and characterize their spatial patterns. Toward this end, we used a bottom-up method to predict emissions and to characterize their spatial patterns using Global Moran's (Spatial autocorrelation analysis) and Getis-Ord General G (High/Low cluster analysis). Our findings suggested that light duty vehicles are primarily responsible for the vehicular emissions of CO (68.9%), CH4 (93.6%), and CO2 (57.9%), whereas heavy duty vehicles are primarily responsible for the vehicular emissions of NMHC (92.9%), NOx (90.7%), and PM (97.4%). Furthermore, CO2 is the pollutant with the highest emissions, over 30 million tons/year. In the spatial autocorrelation analysis was identified cluster (p < 0.01) for all types of vehicles and for all pollutants. However, we identified high cluster only for the light vehicles.

Geographical and Temporal Body Size Variation in a Reptile: Roles of Sex, Ecology, Phylogeny and Ecology Structured in Phylogeny

PubMed Central

Aragón, Pedro; Fitze, Patrick S.

2014-01-01

Geographical body size variation has long interested evolutionary biologists, and a range of mechanisms have been proposed to explain the observed patterns. It is considered to be more puzzling in ectotherms than in endotherms, and integrative approaches are necessary for testing non-exclusive alternative mechanisms. Using lacertid lizards as a model, we adopted an integrative approach, testing different hypotheses for both sexes while incorporating temporal, spatial, and phylogenetic autocorrelation at the individual level. We used data on the Spanish Sand Racer species group from a field survey to disentangle different sources of body size variation through environmental and individual genetic data, while accounting for temporal and spatial autocorrelation. A variation partitioning method was applied to separate independent and shared components of ecology and phylogeny, and estimated their significance. Then, we fed-back our models by controlling for relevant independent components. The pattern was consistent with the geographical Bergmann's cline and the experimental temperature-size rule: adults were larger at lower temperatures (and/or higher elevations). This result was confirmed with additional multi-year independent data-set derived from the literature. Variation partitioning showed no sex differences in phylogenetic inertia but showed sex differences in the independent component of ecology; primarily due to growth differences. Interestingly, only after controlling for independent components did primary productivity also emerge as an important predictor explaining size variation in both sexes. This study highlights the importance of integrating individual-based genetic information, relevant ecological parameters, and temporal and spatial autocorrelation in sex-specific models to detect potentially important hidden effects. Our individual-based approach devoted to extract and control for independent components was useful to reveal hidden effects linked with alternative non-exclusive hypothesis, such as those of primary productivity. Also, including measurement date allowed disentangling and controlling for short-term temporal autocorrelation reflecting sex-specific growth plasticity. PMID:25090025

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

PubMed Central

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID:26402522

Spatial and spatiotemporal pattern analysis of coconut lethal yellowing in Mozambique.

PubMed

Bonnot, F; de Franqueville, H; Lourenço, E

2010-04-01

Coconut lethal yellowing (LY) is caused by a phytoplasma and is a major threat for coconut production throughout its growing area. Incidence of LY was monitored visually on every coconut tree in six fields in Mozambique for 34 months. Disease progress curves were plotted and average monthly disease incidence was estimated. Spatial patterns of disease incidence were analyzed at six assessment times. Aggregation was tested by the coefficient of spatial autocorrelation of the beta-binomial distribution of diseased trees in quadrats. The binary power law was used as an assessment of overdispersion across the six fields. Spatial autocorrelation between symptomatic trees was measured by the BB join count statistic based on the number of pairs of diseased trees separated by a specific distance and orientation, and tested using permutation methods. Aggregation of symptomatic trees was detected in every field in both cumulative and new cases. Spatiotemporal patterns were analyzed with two methods. The proximity of symptomatic trees at two assessment times was investigated using the spatiotemporal BB join count statistic based on the number of pairs of trees separated by a specific distance and orientation and exhibiting the first symptoms of LY at the two times. The semivariogram of times of appearance of LY was calculated to characterize how the lag between times of appearance of LY was related to the distance between symptomatic trees. Both statistics were tested using permutation methods. A tendency for new cases to appear in the proximity of previously diseased trees and a spatially structured pattern of times of appearance of LY within clusters of diseased trees were detected, suggesting secondary spread of the disease.

Human Cortical θ during Free Exploration Encodes Space and Predicts Subsequent Memory

PubMed Central

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric

2013-01-01

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration. PMID:24048836

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach.

PubMed

Domnich, Alexander; Arata, Lucia; Amicizia, Daniela; Signori, Alessio; Gasparini, Roberto; Panatto, Donatella

2016-11-16

Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km2, and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita (I=0.082) and per km2 (I=0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population, mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km2. The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.

Human cortical θ during free exploration encodes space and predicts subsequent memory.

PubMed

Snider, Joseph; Plank, Markus; Lynch, Gary; Halgren, Eric; Poizner, Howard

2013-09-18

Spatial representations and walking speed in rodents are consistently related to the phase, frequency, and/or amplitude of θ rhythms in hippocampal local field potentials. However, neuropsychological studies in humans have emphasized the importance of parietal cortex for spatial navigation, and efforts to identify the electrophysiological signs of spatial navigation in humans have been stymied by the difficulty of recording during free exploration of complex environments. We resolved the recording problem and experimentally probed brain activity of human participants who were fully ambulant. On each of 2 d, electroencephalography was synchronized with head and body movement in 13 subjects freely navigating an extended virtual environment containing numerous unique objects. θ phase and amplitude recorded over parietal cortex were consistent when subjects walked through a particular spatial separation at widely separated times. This spatial displacement θ autocorrelation (STAcc) was quantified and found to be significant from 2 to 8 Hz within the environment. Similar autocorrelation analyses performed on an electrooculographic channel, used to measure eye movements, showed no significant spatial autocorrelations, ruling out eye movements as the source of STAcc. Strikingly, the strength of an individual's STAcc maps from day 1 significantly predicted object location recall success on day 2. θ was also significantly correlated with walking speed; however, this correlation appeared unrelated to STAcc and did not predict memory performance. This is the first demonstration of memory-related, spatial maps in humans generated during active spatial exploration.

Spatial occupancy models for large data sets

USGS Publications Warehouse

Johnson, Devin S.; Conn, Paul B.; Hooten, Mevin B.; Ray, Justina C.; Pond, Bruce A.

2013-01-01

Since its development, occupancy modeling has become a popular and useful tool for ecologists wishing to learn about the dynamics of species occurrence over time and space. Such models require presence–absence data to be collected at spatially indexed survey units. However, only recently have researchers recognized the need to correct for spatially induced overdisperison by explicitly accounting for spatial autocorrelation in occupancy probability. Previous efforts to incorporate such autocorrelation have largely focused on logit-normal formulations for occupancy, with spatial autocorrelation induced by a random effect within a hierarchical modeling framework. Although useful, computational time generally limits such an approach to relatively small data sets, and there are often problems with algorithm instability, yielding unsatisfactory results. Further, recent research has revealed a hidden form of multicollinearity in such applications, which may lead to parameter bias if not explicitly addressed. Combining several techniques, we present a unifying hierarchical spatial occupancy model specification that is particularly effective over large spatial extents. This approach employs a probit mixture framework for occupancy and can easily accommodate a reduced-dimensional spatial process to resolve issues with multicollinearity and spatial confounding while improving algorithm convergence. Using open-source software, we demonstrate this new model specification using a case study involving occupancy of caribou (Rangifer tarandus) over a set of 1080 survey units spanning a large contiguous region (108 000 km2) in northern Ontario, Canada. Overall, the combination of a more efficient specification and open-source software allows for a facile and stable implementation of spatial occupancy models for large data sets.

[Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model].

PubMed

Yuan, Yu Yun; Wahap, Halik; Guan, Jing Yun; Lu, Long Hui; Zhang, Qin Qin

2016-10-01

In this paper, topsoil salinity data gathered from 24 sampling sites in the Yutian Oasis were used, nine different kinds of environmental variables closely related to soil salinity were selec-ted as influencing factors, then, the spatial distribution characteristics of topsoil salinity and spatial heterogeneity of influencing factors were analyzed by combining the spatial autocorrelation with traditional regression analysis and geographically weighted regression model. Results showed that the topsoil salinity in Yutian Oasis was not of random distribution but had strong spatial dependence, and the spatial autocorrelation index for topsoil salinity was 0.479. Groundwater salinity, groundwater depth, elevation and temperature were the main factors influencing topsoil salt accumulation in arid land oases and they were spatially heterogeneous. The nine selected environmental variables except soil pH had significant influences on topsoil salinity with spatial disparity. GWR model was superior to the OLS model on interpretation and estimation of spatial non-stationary data, also had a remarkable advantage in visualization of modeling parameters.

Dispersal leads to spatial autocorrelation in species distributions: A simulation model

USGS Publications Warehouse

Bahn, V.; Krohn, W.B.; O'Connor, R.J.

2008-01-01

Compared to population growth regulated by local conditions, dispersal has been underappreciated as a central process shaping the spatial distribution of populations. This paper asks: (a) which conditions increase the importance of dispersers relative to local recruits in determining population sizes? and (b) how does dispersal influence the spatial distribution patterns of abundances among connected populations? We approached these questions with a simulation model of populations on a coupled lattice with cells of continuously varying habitat quality expressed as carrying capacities. Each cell contained a population with the basic dynamics of density-regulated growth, and was connected to other populations by immigration and emigration. The degree to which dispersal influenced the distribution of population sizes depended most strongly on the absolute amount of dispersal, and then on the potential population growth rate. Dispersal decaying in intensity with distance left close neighbours more alike in population size than distant populations, leading to an increase in spatial autocorrelation. The spatial distribution of species with low potential growth rates is more dependent on dispersal than that of species with high growth rates; therefore, distribution modelling for species with low growth rates requires particular attention to autocorrelation, and conservation management of these species requires attention to factors curtailing dispersal, such as fragmentation and dispersal barriers. ?? 2007 Elsevier B.V. All rights reserved.

Spatial variation in mandibular bone elastic modulus and its effect on structural bending stiffness: A test case using the Taï Forest monkeys.

PubMed

Le, Kim N; Marsik, Matthew; Daegling, David J; Duque, Ana; McGraw, William Scott

2017-03-01

We investigated how heterogeneity in material stiffness affects structural stiffness in the cercopithecid mandibular cortical bone. We assessed (1) whether this effect changes the interpretation of interspecific structural stiffness variation across four primate species, (2) whether the heterogeneity is random, and (3) whether heterogeneity mitigates bending stress in the jaw associated with food processing. The sample consisted of Taï Forest, Cote d'Ivoire, monkeys: Cercocebus atys, Piliocolobus badius, Colobus polykomos, and Cercopithecus diana. Vickers indentation hardness samples estimated elastic moduli throughout the cortical bone area of each coronal section of postcanine corpus. For each section, we calculated maximum area moment of inertia, I max (structural mechanical property), under three models of material heterogeneity, as well as spatial autocorrelation statistics (Moran's I, I MORAN ). When the model considered material stiffness variation and spatial patterning, I max decreased and individual ranks based on structural stiffness changed. Rank changes were not significant across models. All specimens showed positive (nonrandom) spatial autocorrelation. Differences in I MORAN were not significant among species, and there were no discernable patterns of autocorrelation within species. Across species, significant local I MORAN was often attributed to proximity of low moduli in the alveolar process and high moduli in the basal process. While our sample did not demonstrate species differences in the degree of spatial autocorrelation of elastic moduli, there may be mechanical effects of heterogeneity (relative strength and rigidity) that do distinguish at the species or subfamilial level (i.e., colobines vs. cercopithecines). The potential connections of heterogeneity to diet and/or taxonomy remain to be discovered. © 2016 Wiley Periodicals, Inc.

A spatial analysis of the association between restaurant density and body mass index in Canadian adults.

PubMed

Hollands, Simon; Campbell, M Karen; Gilliland, Jason; Sarma, Sisira

2013-10-01

To investigate the association between fast-food restaurant density and adult body mass index (BMI) in Canada. Individual-level BMI and confounding variables were obtained from the 2007-2008 Canadian Community Health Survey master file. Locations of the fast-food and full-service chain restaurants and other non-chain restaurants were obtained from the 2008 Infogroup Canada business database. Food outlet density (fast-food, full-service and other) per 10,000 population was calculated for each Forward Sortation Area (FSA). Global (Moran's I) and local indicators of spatial autocorrelation of BMI were assessed. Ordinary least squares (OLS) and spatial auto-regressive error (SARE) methods were used to assess the association between local food environment and adult BMI in Canada. Global and local spatial autocorrelation of BMI were found in our univariate analysis. We found that OLS and SARE estimates were very similar in our multivariate models. An additional fast-food restaurant per 10,000 people at the FSA-level is associated with a 0.022kg/m(2) increase in BMI. On the other hand, other restaurant density is negatively related to BMI. Fast-food restaurant density is positively associated with BMI in Canada. Results suggest that restricting availability of fast-food in local neighborhoods may play a role in obesity prevention. © 2013.

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia.

PubMed

Moriguchi, Sachiko; Tominaga, Atsushi; Irwin, Kelly J; Freake, Michael J; Suzuki, Kazutaka; Goka, Koichi

2015-04-08

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

Analysis of data from NASA B-57B gust gradient program

NASA Technical Reports Server (NTRS)

Frost, W.; Lin, M. C.; Chang, H. P.; Ringnes, E.

1985-01-01

Statistical analysis of the turbulence measured in flight 6 of the NASA B-57B over Denver, Colorado, from July 7 to July 23, 1982 included the calculations of average turbulence parameters, integral length scales, probability density functions, single point autocorrelation coefficients, two point autocorrelation coefficients, normalized autospectra, normalized two point autospectra, and two point cross sectra for gust velocities. The single point autocorrelation coefficients were compared with the theoretical model developed by von Karman. Theoretical analyses were developed which address the effects spanwise gust distributions, using two point spatial turbulence correlations.

[Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

PubMed

Zhang, Ling Yu; Liu, Zhao Gang

2017-12-01

Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

Spatial early warning signals in a lake manipulation

USGS Publications Warehouse

Butitta, Vince L.; Carpenter, Stephen R.; Loken, Luke; Pace, Michael L.; Stanley, Emily H.

2017-01-01

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or early warning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.

Power quality analysis based on spatial correlation

NASA Astrophysics Data System (ADS)

Li, Jiangtao; Zhao, Gang; Liu, Haibo; Li, Fenghou; Liu, Xiaoli

2018-03-01

With the industrialization and urbanization, the status of electricity in the production and life is getting higher and higher. So the prediction of power quality is the more potential significance. Traditional power quality analysis methods include: power quality data compression, disturbance event pattern classification, disturbance parameter calculation. Under certain conditions, these methods can predict power quality. This paper analyses the temporal variation of power quality of one provincial power grid in China from time angle. The distribution of power quality was analyzed based on spatial autocorrelation. This paper tries to prove that the research idea of geography is effective for mining the potential information of power quality.

The use of spatio-temporal correlation to forecast critical transitions

NASA Astrophysics Data System (ADS)

Karssenberg, Derek; Bierkens, Marc F. P.

2010-05-01

Complex dynamical systems may have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been observed in systems ranging from the human body system to financial markets and the Earth system. Forecasting the timing of critical transitions before they are reached is of paramount importance because critical transitions are associated with a large shift in dynamical regime of the system under consideration. However, it is hard to forecast critical transitions, because the state of the system shows relatively little change before the threshold is reached. Recently, it was shown that increased spatio-temporal autocorrelation and variance can serve as alternative early warning signal for critical transitions. However, thus far these second order statistics have not been used for forecasting in a data assimilation framework. Here we show that the use of spatio-temporal autocorrelation and variance in the state of the system reduces the uncertainty in the predicted timing of critical transitions compared to classical approaches that use the value of the system state only. This is shown by assimilating observed spatio-temporal autocorrelation and variance into a dynamical system model using a Particle Filter. We adapt a well-studied distributed model of a logistically growing resource with a fixed grazing rate. The model describes the transition from an underexploited system with high resource biomass to overexploitation as grazing pressure crosses the critical threshold, which is a fold bifurcation. To represent limited prior information, we use a large variance in the prior probability distributions of model parameters and the system driver (grazing rate). First, we show that the rate of increase in spatio-temporal autocorrelation and variance prior to reaching the critical threshold is relatively consistent across the uncertainty range of the driver and parameter values used. This indicates that an increase in spatio-temporal autocorrelation and variance are consistent predictors of a critical transition, even under the condition of a poorly defined system. Second, we perform data assimilation experiments using an artificial exhaustive data set generated by one realization of the model. To mimic real-world sampling, an observational data set is created from this exhaustive data set. This is done by sampling on a regular spatio-temporal grid, supplemented by sampling locations at a short distance. Spatial and temporal autocorrelation in this observational data set is calculated for different spatial and temporal separation (lag) distances. To assign appropriate weights to observations (here, autocorrelation values and variance) in the Particle Filter, the covariance matrix of the error in these observations is required. This covariance matrix is estimated using Monte Carlo sampling, selecting a different random position of the sampling network relative to the exhaustive data set for each realization. At each update moment in the Particle Filter, observed autocorrelation values are assimilated into the model and the state of the model is updated. Using this approach, it is shown that the use of autocorrelation reduces the uncertainty in the forecasted timing of a critical transition compared to runs without data assimilation. The performance of the use of spatial autocorrelation versus temporal autocorrelation depends on the timing and number of observational data. This study is restricted to a single model only. However, it is becoming increasingly clear that spatio-temporal autocorrelation and variance can be used as early warning signals for a large number of systems. Thus, it is expected that spatio-temporal autocorrelation and variance are valuable in data assimilation frameworks in a large number of dynamical systems.

Spatial Autocorrelation of Cancer Incidence in Saudi Arabia

PubMed Central

Al-Ahmadi, Khalid; Al-Zahrani, Ali

2013-01-01

Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran’s I and Anselin’s local Moran’s I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran’s I index values, indicating a tendency toward clustering. The Anselin’s local Moran’s I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin’s disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin’s disease (r² = 0.49–0.67 using OLS and r² = 0.52–0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations. PMID:24351742

Predicting thermal regimes of stream networks across the northeast United States: Natural and anthropogenic influences

EPA Science Inventory

We used STARS (Spatial Tools for the Analysis of River Systems), an ArcGIS geoprocessing toolbox, to create spatial stream networks. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance...

Dynamic Assessment on the Landscape Patterns and Spatio-temporal Change in the mainstream of Tarim River

NASA Astrophysics Data System (ADS)

Zhang, Hui; Xue, Lianqing; Yang, Changbing; Chen, Xinfang; Zhang, Luochen; Wei, Guanghui

2018-01-01

The Tarim River (TR), as the longest inland river at an arid area in China, is a typical regions of vegetation variation research and plays a crucial role in the sustainable development of regional ecological environment. In this paper, the newest dataset of MODND1M NDVI, at a resolution of 500m, were applied to calculate vegetation index in growing season during the period 2000-2015. Using a vegetation coverage index, a trend line analysis, and the local spatial autocorrelation analysis, this paper investigated the landscape patterns and spatio-temporal variation of vegetation coverage at regional and pixel scales over mainstream of the Tarim River, Xinjiang. The results showed that (1) The bare land area on both sides of Tarim River appeared to have a fluctuated downward trend and there were two obvious valley values in 2005 and 2012. (2) Spatially, the vegetation coverage improved areas is mostly distributed in upstream and the degraded areas is mainly distributed in the left bank of midstream and the end of Tarim River during 2000-2005. (3) The local spatial auto-correlation analysis revealed that vegetation coverage was spatially positive autocorrelated and spatial concentrated. The high-high self-related areas are mainly distributed in upstream, where vegetation cover are relatively good, and the low-low self-related areas are mostly with lower vegetation cover in the lower reaches of Tarim River.

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping

PubMed Central

2011-01-01

Background Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. Results In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Conclusions Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset. PMID:21978359

Adjusting for sampling variability in sparse data: geostatistical approaches to disease mapping.

PubMed

Hampton, Kristen H; Serre, Marc L; Gesink, Dionne C; Pilcher, Christopher D; Miller, William C

2011-10-06

Disease maps of crude rates from routinely collected health data indexed at a small geographical resolution pose specific statistical problems due to the sparse nature of the data. Spatial smoothers allow areas to borrow strength from neighboring regions to produce a more stable estimate of the areal value. Geostatistical smoothers are able to quantify the uncertainty in smoothed rate estimates without a high computational burden. In this paper, we introduce a uniform model extension of Bayesian Maximum Entropy (UMBME) and compare its performance to that of Poisson kriging in measures of smoothing strength and estimation accuracy as applied to simulated data and the real data example of HIV infection in North Carolina. The aim is to produce more reliable maps of disease rates in small areas to improve identification of spatial trends at the local level. In all data environments, Poisson kriging exhibited greater smoothing strength than UMBME. With the simulated data where the true latent rate of infection was known, Poisson kriging resulted in greater estimation accuracy with data that displayed low spatial autocorrelation, while UMBME provided more accurate estimators with data that displayed higher spatial autocorrelation. With the HIV data, UMBME performed slightly better than Poisson kriging in cross-validatory predictive checks, with both models performing better than the observed data model with no smoothing. Smoothing methods have different advantages depending upon both internal model assumptions that affect smoothing strength and external data environments, such as spatial correlation of the observed data. Further model comparisons in different data environments are required to provide public health practitioners with guidelines needed in choosing the most appropriate smoothing method for their particular health dataset.

Spatial Dependency and Contextual Effects on Academic Achievement

ERIC Educational Resources Information Center

Matlock, Ki; Song, Joon Jin; Goering, Christian Z.

2014-01-01

This study investigated the influences of district-related variables on a district's academic performance. Arkansas augmented benchmark examination scores were used to measure a district's scholastic achievement. Spatial analysis fit each district's performance to its geographical location; spatial autocorrelation measured the amount of influence…

Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach

NASA Astrophysics Data System (ADS)

Salleh, R. M.; Zawawi, N. I.; Gan, Z. F.; Nor, M. E.

2018-04-01

Existing of autocorrelation will bring a significant effect on the performance and accuracy of process control if the problem does not handle carefully. When dealing with autocorrelated process, Box-Jenkins method will be preferred because of the popularity. However, the computation of Box-Jenkins method is too complicated and challenging which cause of time-consuming. Therefore, an alternative method which known as Geometric Brownian Motion (GBM) is introduced to monitor the autocorrelated process. One real case of furnace temperature data is conducted to compare the performance of Box-Jenkins and GBM methods in monitoring autocorrelation process. Both methods give the same results in terms of model accuracy and monitoring process control. Yet, GBM is superior compared to Box-Jenkins method due to its simplicity and practically with shorter computational time.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas.

PubMed

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-09-02

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

PubMed Central

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-01-01

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST. PMID:27598186

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

USGS Publications Warehouse

Wilson, T.L.; Odei, J.B.; Hooten, M.B.; Edwards, T.C.

2010-01-01

Conservationists routinely use species distribution models to plan conservation, restoration and development actions, while ecologists use them to infer process from pattern. These models tend to work well for common or easily observable species, but are of limited utility for rare and cryptic species. This may be because honest accounting of known observation bias and spatial autocorrelation are rarely included, thereby limiting statistical inference of resulting distribution maps. We specified and implemented a spatially explicit Bayesian hierarchical model for a cryptic mammal species (pygmy rabbit Brachylagus idahoensis). Our approach used two levels of indirect sign that are naturally hierarchical (burrows and faecal pellets) to build a model that allows for inference on regression coefficients as well as spatially explicit model parameters. We also produced maps of rabbit distribution (occupied burrows) and relative abundance (number of burrows expected to be occupied by pygmy rabbits). The model demonstrated statistically rigorous spatial prediction by including spatial autocorrelation and measurement uncertainty. We demonstrated flexibility of our modelling framework by depicting probabilistic distribution predictions using different assumptions of pygmy rabbit habitat requirements. Spatial representations of the variance of posterior predictive distributions were obtained to evaluate heterogeneity in model fit across the spatial domain. Leave-one-out cross-validation was conducted to evaluate the overall model fit. Synthesis and applications. Our method draws on the strengths of previous work, thereby bridging and extending two active areas of ecological research: species distribution models and multi-state occupancy modelling. Our framework can be extended to encompass both larger extents and other species for which direct estimation of abundance is difficult. ?? 2010 The Authors. Journal compilation ?? 2010 British Ecological Society.

Heteroskedasticity as a leading indicator of desertification in spatially explicit data.

PubMed

Seekell, David A; Dakos, Vasilis

2015-06-01

Regime shifts are abrupt transitions between alternate ecosystem states including desertification in arid regions due to drought or overgrazing. Regime shifts may be preceded by statistical anomalies such as increased autocorrelation, indicating declining resilience and warning of an impending shift. Tests for conditional heteroskedasticity, a type of clustered variance, have proven powerful leading indicators for regime shifts in time series data, but an analogous indicator for spatial data has not been evaluated. A spatial analog for conditional heteroskedasticity might be especially useful in arid environments where spatial interactions are critical in structuring ecosystem pattern and process. We tested the efficacy of a test for spatial heteroskedasticity as a leading indicator of regime shifts with simulated data from spatially extended vegetation models with regular and scale-free patterning. These models simulate shifts from extensive vegetative cover to bare, desert-like conditions. The magnitude of spatial heteroskedasticity increased consistently as the modeled systems approached a regime shift from vegetated to desert state. Relative spatial autocorrelation, spatial heteroskedasticity increased earlier and more consistently. We conclude that tests for spatial heteroskedasticity can contribute to the growing toolbox of early warning indicators for regime shifts analyzed with spatially explicit data.

Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

PubMed Central

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P. A.; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

PubMed

Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P A; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

2014-01-01

Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Decorrelation Times of Photospheric Fields and Flows

NASA Technical Reports Server (NTRS)

Welsch, B. T.; Kusano, K.; Yamamoto, T. T.; Muglach, K.

2012-01-01

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.

Cross-comparison and evaluation of air pollution field estimation methods

NASA Astrophysics Data System (ADS)

Yu, Haofei; Russell, Armistead; Mulholland, James; Odman, Talat; Hu, Yongtao; Chang, Howard H.; Kumar, Naresh

2018-04-01

Accurate estimates of human exposure is critical for air pollution health studies and a variety of methods are currently being used to assign pollutant concentrations to populations. Results from these methods may differ substantially, which can affect the outcomes of health impact assessments. Here, we applied 14 methods for developing spatiotemporal air pollutant concentration fields of eight pollutants to the Atlanta, Georgia region. These methods include eight methods relying mostly on air quality observations (CM: central monitor; SA: spatial average; IDW: inverse distance weighting; KRIG: kriging; TESS-D: discontinuous tessellation; TESS-NN: natural neighbor tessellation with interpolation; LUR: land use regression; AOD: downscaled satellite-derived aerosol optical depth), one using the RLINE dispersion model, and five methods using a chemical transport model (CMAQ), with and without using observational data to constrain results. The derived fields were evaluated and compared. Overall, all methods generally perform better at urban than rural area, and for secondary than primary pollutants. We found the CM and SA methods may be appropriate only for small domains, and for secondary pollutants, though the SA method lead to large negative spatial correlations when using data withholding for PM2.5 (spatial correlation coefficient R = -0.81). The TESS-D method was found to have major limitations. Results of the IDW, KRIG and TESS-NN methods are similar. They are found to be better suited for secondary pollutants because of their satisfactory temporal performance (e.g. average temporal R2 > 0.85 for PM2.5 but less than 0.35 for primary pollutant NO2). In addition, they are suitable for areas with relatively dense monitoring networks due to their inability to capture spatial concentration variabilities, as indicated by the negative spatial R (lower than -0.2 for PM2.5 when assessed using data withholding). The performance of LUR and AOD methods were similar to kriging. Using RLINE and CMAQ fields without fusing observational data led to substantial errors and biases, though the CMAQ model captured spatial gradients reasonably well (spatial R = 0.45 for PM2.5). Two unique tests conducted here included quantifying autocorrelation of method biases (which can be important in time series analyses) and how well the methods capture the observed interspecies correlations (which would be of particular importance in multipollutant health assessments). Autocorrelation of method biases lasted longest and interspecies correlations of primary pollutants was higher than observations when air quality models were used without data fusing. Use of hybrid methods that combine air quality model outputs with observational data overcome some of these limitations and is better suited for health studies. Results from this study contribute to better understanding the strengths and weaknesses of different methods for estimating human exposures.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Spatial Dependence and Heterogeneity in Bayesian Factor Analysis: A Cross-National Investigation of Schwartz Values

ERIC Educational Resources Information Center

Stakhovych, Stanislav; Bijmolt, Tammo H. A.; Wedel, Michel

2012-01-01

In this article, we present a Bayesian spatial factor analysis model. We extend previous work on confirmatory factor analysis by including geographically distributed latent variables and accounting for heterogeneity and spatial autocorrelation. The simulation study shows excellent recovery of the model parameters and demonstrates the consequences…

Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

2014-10-01

The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

Image correlation microscopy for uniform illumination.

PubMed

Gaborski, T R; Sealander, M N; Ehrenberg, M; Waugh, R E; McGrath, J L

2010-01-01

Image cross-correlation microscopy is a technique that quantifies the motion of fluorescent features in an image by measuring the temporal autocorrelation function decay in a time-lapse image sequence. Image cross-correlation microscopy has traditionally employed laser-scanning microscopes because the technique emerged as an extension of laser-based fluorescence correlation spectroscopy. In this work, we show that image correlation can also be used to measure fluorescence dynamics in uniform illumination or wide-field imaging systems and we call our new approach uniform illumination image correlation microscopy. Wide-field microscopy is not only a simpler, less expensive imaging modality, but it offers the capability of greater temporal resolution over laser-scanning systems. In traditional laser-scanning image cross-correlation microscopy, lateral mobility is calculated from the temporal de-correlation of an image, where the characteristic length is the illuminating laser beam width. In wide-field microscopy, the diffusion length is defined by the feature size using the spatial autocorrelation function. Correlation function decay in time occurs as an object diffuses from its original position. We show that theoretical and simulated comparisons between Gaussian and uniform features indicate the temporal autocorrelation function depends strongly on particle size and not particle shape. In this report, we establish the relationships between the spatial autocorrelation function feature size, temporal autocorrelation function characteristic time and the diffusion coefficient for uniform illumination image correlation microscopy using analytical, Monte Carlo and experimental validation with particle tracking algorithms. Additionally, we demonstrate uniform illumination image correlation microscopy analysis of adhesion molecule domain aggregation and diffusion on the surface of human neutrophils.

Fractal analysis of multiscale spatial autocorrelation among point data

USGS Publications Warehouse

De Cola, L.

1991-01-01

The analysis of spatial autocorrelation among point-data quadrats is a well-developed technique that has made limited but intriguing use of the multiscale aspects of pattern. In this paper are presented theoretical and algorithmic approaches to the analysis of aggregations of quadrats at or above a given density, in which these sets are treated as multifractal regions whose fractal dimension, D, may vary with phenomenon intensity, scale, and location. The technique is illustrated with Matui's quadrat house-count data, which yield measurements consistent with a nonautocorrelated simulated Poisson process but not with an orthogonal unit-step random walk. The paper concludes with a discussion of the implications of such analysis for multiscale geographic analysis systems. -Author

SPATIAL SCALE OF AUTOCORRELATION IN WISCONSIN FROG AND TOAD SURVEY DATA

EPA Science Inventory

The degree to which local population dynamics are correlated with nearby sites has important implications for metapopulation dynamics and landscape management. Spatially extensive monitoring data can be used to evaluate large-scale population dynamic processes. Our goals in this ...

Spatial and Temporal Patterns of Locally-Acquired Dengue Transmission in Northern Queensland, Australia, 1993–2012

PubMed Central

Naish, Suchithra; Dale, Pat; Mackenzie, John S.; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Background Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993–2012. Methods Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Results 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ2 = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Conclusions Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas. PMID:24691549

Small area-level variation in the incidence of psychotic disorders in an urban area in France: an ecological study.

PubMed

Szoke, Andrei; Pignon, Baptiste; Baudin, Grégoire; Tortelli, Andrea; Richard, Jean-Romain; Leboyer, Marion; Schürhoff, Franck

2016-07-01

We sought to determine whether significant variation in the incidence of clinically relevant psychoses existed at an ecological level in an urban French setting, and to examine possible factors associated with this variation. We aimed to advance the literature by testing this hypothesis in a novel population setting and by comparing a variety of spatial models. We sought to identify all first episode cases of non-affective and affective psychotic disorders presenting in a defined urban catchment area over a 4 years period, over more than half a million person-years at-risk. Because data from geographic close neighbourhoods usually show spatial autocorrelation, we used for our analyses Bayesian modelling. We included small area neighbourhood measures of deprivation, migrants' density and social fragmentation as putative explanatory variables in the models. Incidence of broad psychotic disorders shows spatial patterning with the best fit for models that included both strong autocorrelation between neighbouring areas and weak autocorrelation between areas further apart. Affective psychotic disorders showed similar spatial patterning and were associated with the proportion of migrants/foreigners in the area (inverse correlation). In contrast, non-affective psychoses did not show spatial patterning. At ecological level, the variation in the number of cases and the factors that influence this variation are different for non-affective and affective psychotic disorders. Important differences in results-compared with previous studies in different settings-point to the importance of the context and the necessity of further studies to understand these differences.

A new phase correction method in NMR imaging based on autocorrelation and histogram analysis.

PubMed

Ahn, C B; Cho, Z H

1987-01-01

A new statistical approach to phase correction in NMR imaging is proposed. The proposed scheme consists of first-and zero-order phase corrections each by the inverse multiplication of estimated phase error. The first-order error is estimated by the phase of autocorrelation calculated from the complex valued phase distorted image while the zero-order correction factor is extracted from the histogram of phase distribution of the first-order corrected image. Since all the correction procedures are performed on the spatial domain after completion of data acquisition, no prior adjustments or additional measurements are required. The algorithm can be applicable to most of the phase-involved NMR imaging techniques including inversion recovery imaging, quadrature modulated imaging, spectroscopic imaging, and flow imaging, etc. Some experimental results with inversion recovery imaging as well as quadrature spectroscopic imaging are shown to demonstrate the usefulness of the algorithm.

Spatial autocorrelation of radiation measured by the Earth Radiation Budget Experiment: Scene inhomogeneity and reciprocity violation

NASA Technical Reports Server (NTRS)

Davies, Roger

1994-01-01

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles less than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Time-series network analysis of civil aviation in Japan (1985-2005)

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Xu, Bing; Yamada, Ikuho

2008-10-01

Due to the airline deregulation in 1985, a series of new airport developments in the 1990s and 2000s, and the reorganization of airline companies in the 2000s, Japan's air passenger transportation has been dramatically altered in the last two decades in many ways. In this paper, the authors examine how the network and flow structures of domestic air passenger transportation in Japan have geographically changed since 1985. For this purpose, passenger flow data in 1985, 1995, and 2005 were extracted from the Air Transportation Statistical Survey conducted by the Ministry of Land, Infrastructure and Transport, Japan. First, national and regional hub airports are identified via dominant flow and hub function analysis. Then the roles of the hub airports and individual connections over the network are examined with respect to their spatial and network autocorrelations. Spatial and network autocorrelations were evaluated both globally and locally using Moran's I and LISA statistics. The passenger flow data were first examined as a whole and then divided into 3 airline-based categories. Dominant flow and hub function enabled us to detect the hub airports. Structural processes of the hub-and-spoke network were confirmed in each airline through spatial autocorrelation analysis. Network autocorrelation analysis showed that all airlines ingeniously optimized their networks by connecting their routes with large numbers of passengers to other routes with large numbers of passengers, and routes with small numbers of passengers to other routes with small numbers of passengers. The effects of political events and the changes in the strategies of each airline on the whole networks were strongly reflected in the results of this study.

Describing spatial pattern in stream networks: A practical approach

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

A geostatistical approach for describing spatial pattern in stream networks

USGS Publications Warehouse

Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

2005-01-01

The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

A Comparison of Local Variance, Fractal Dimension, and Moran's I as Aids to Multispectral Image Classification

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Sig-NganLam, Nina; Quattrochi, Dale A.

2004-01-01

The accuracy of traditional multispectral maximum-likelihood image classification is limited by the skewed statistical distributions of reflectances from the complex heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery. Tools available in the Image Characterization and Modeling System (ICAMS) were used to analyze Landsat 7 imagery of Atlanta, Georgia. Although segmentation of panchromatic images is possible using indicators of spatial complexity, different land covers often yield similar values of these indices. Better results are obtained when a surface of local fractal dimension or spatial autocorrelation is combined as an additional layer in a supervised maximum-likelihood multispectral classification. The addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per-pixel spectral classification techniques.

An autocorrelation method to detect low frequency earthquakes within tremor

USGS Publications Warehouse

Brown, J.R.; Beroza, G.C.; Shelly, D.R.

2008-01-01

Recent studies have shown that deep tremor in the Nankai Trough under western Shikoku consists of a swarm of low frequency earthquakes (LFEs) that occur as slow shear slip on the down-dip extension of the primary seismogenic zone of the plate interface. The similarity of tremor in other locations suggests a similar mechanism, but the absence of cataloged low frequency earthquakes prevents a similar analysis. In this study, we develop a method for identifying LFEs within tremor. The method employs a matched-filter algorithm, similar to the technique used to infer that tremor in parts of Shikoku is comprised of LFEs; however, in this case we do not assume the origin times or locations of any LFEs a priori. We search for LFEs using the running autocorrelation of tremor waveforms for 6 Hi-Net stations in the vicinity of the tremor source. Time lags showing strong similarity in the autocorrelation represent either repeats, or near repeats, of LFEs within the tremor. We test the method on an hour of Hi-Net recordings of tremor and demonstrates that it extracts both known and previously unidentified LFEs. Once identified, we cross correlate waveforms to measure relative arrival times and locate the LFEs. The results are able to explain most of the tremor as a swarm of LFEs and the locations of newly identified events appear to fill a gap in the spatial distribution of known LFEs. This method should allow us to extend the analysis of Shelly et al. (2007a) to parts of the Nankai Trough in Shikoku that have sparse LFE coverage, and may also allow us to extend our analysis to other regions that experience deep tremor, but where LFEs have not yet been identified. Copyright 2008 by the American Geophysical Union.

Does the edge effect impact on the measure of spatial accessibility to healthcare providers?

PubMed

Gao, Fei; Kihal, Wahida; Le Meur, Nolwenn; Souris, Marc; Deguen, Séverine

2017-12-11

Spatial accessibility indices are increasingly applied when investigating inequalities in health. Although most studies are making mentions of potential errors caused by the edge effect, many acknowledge having neglected to consider this concern by establishing spatial analyses within a finite region, settling for hypothesizing that accessibility to facilities will be under-reported. Our study seeks to assess the effect of edge on the accuracy of defining healthcare provider access by comparing healthcare provider accessibility accounting or not for the edge effect, in a real-world application. This study was carried out in the department of Nord, France. The statistical unit we use is the French census block known as 'IRIS' (Ilot Regroupé pour l'Information Statistique), defined by the National Institute of Statistics and Economic Studies. The geographical accessibility indicator used is the "Index of Spatial Accessibility" (ISA), based on the E2SFCA algorithm. We calculated ISA for the pregnant women population by selecting three types of healthcare providers: general practitioners, gynecologists and midwives. We compared ISA variation when accounting or not edge effect in urban and rural zones. The GIS method was then employed to determine global and local autocorrelation. Lastly, we compared the relationship between socioeconomic distress index and ISA, when accounting or not for the edge effect, to fully evaluate its impact. The results revealed that on average ISA when offer and demand beyond the boundary were included is slightly below ISA when not accounting for the edge effect, and we found that the IRIS value was more likely to deteriorate than improve. Moreover, edge effect impact can vary widely by health provider type. There is greater variability within the rural IRIS group than within the urban IRIS group. We found a positive correlation between socioeconomic distress variables and composite ISA. Spatial analysis results (such as Moran's spatial autocorrelation index and local indicators of spatial autocorrelation) are not really impacted. Our research has revealed minor accessibility variation when edge effect has been considered in a French context. No general statement can be set up because intensity of impact varies according to healthcare provider type, territorial organization and methodology used to measure the accessibility to healthcare. Additional researches are required in order to distinguish what findings are specific to a territory and others common to different countries. It constitute a promising direction to determine more precisely healthcare shortage areas and then to fight against social health inequalities.

Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

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

Warner, Timothy; Steinmaus, Karen L.

2005-02-01

New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

Big assumptions for small samples in crop insurance

Treesearch

Ashley Elaine Hungerford; Barry Goodwin

2014-01-01

The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial autocorrelation and small sample size are not properly accounted for in premium ratings, the premium rates may inaccurately reflect the risk of a loss.

Calling depths of baleen whales from single sensor data: development of an autocorrelation method using multipath localization.

PubMed

Valtierra, Robert D; Glynn Holt, R; Cholewiak, Danielle; Van Parijs, Sofie M

2013-09-01

Multipath localization techniques have not previously been applied to baleen whale vocalizations due to difficulties in application to tonal vocalizations. Here it is shown that an autocorrelation method coupled with the direct reflected time difference of arrival localization technique can successfully resolve location information. A derivation was made to model the autocorrelation of a direct signal and its overlapping reflections to illustrate that an autocorrelation may be used to extract reflection information from longer duration signals containing a frequency sweep, such as some calls produced by baleen whales. An analysis was performed to characterize the difference in behavior of the autocorrelation when applied to call types with varying parameters (sweep rate, call duration). The method's feasibility was tested using data from playback transmissions to localize an acoustic transducer at a known depth and location. The method was then used to estimate the depth and range of a single North Atlantic right whale (Eubalaena glacialis) and humpback whale (Megaptera novaeangliae) from two separate experiments.

Studies of the micromorphology of sputtered TiN thin films by autocorrelation techniques

NASA Astrophysics Data System (ADS)

Smagoń, Kamil; Stach, Sebastian; Ţălu, Ştefan; Arman, Ali; Achour, Amine; Luna, Carlos; Ghobadi, Nader; Mardani, Mohsen; Hafezi, Fatemeh; Ahmadpourian, Azin; Ganji, Mohsen; Grayeli Korpi, Alireza

2017-12-01

Autocorrelation techniques are crucial tools for the study of the micromorphology of surfaces: They provide the description of anisotropic properties and the identification of repeated patterns on the surface, facilitating the comparison of samples. In the present investigation, some fundamental concepts of these techniques including the autocorrelation function and autocorrelation length have been reviewed and applied in the study of titanium nitride thin films by atomic force microscopy (AFM). The studied samples were grown on glass substrates by reactive magnetron sputtering at different substrate temperatures (from 25 {}°C to 400 {}°C , and their micromorphology was studied by AFM. The obtained AFM data were analyzed using MountainsMap Premium software obtaining the correlation function, the structure of isotropy and the spatial parameters according to ISO 25178 and EUR 15178N. These studies indicated that the substrate temperature during the deposition process is an important parameter to modify the micromorphology of sputtered TiN thin films and to find optimized surface properties. For instance, the autocorrelation length exhibited a maximum value for the sample prepared at a substrate temperature of 300 {}°C , and the sample obtained at 400 {}°C presented a maximum angle of the direction of the surface structure.

Comparative analysis of zonal systems for macro-level crash modeling.

PubMed

Cai, Qing; Abdel-Aty, Mohamed; Lee, Jaeyoung; Eluru, Naveen

2017-06-01

Macro-level traffic safety analysis has been undertaken at different spatial configurations. However, clear guidelines for the appropriate zonal system selection for safety analysis are unavailable. In this study, a comparative analysis was conducted to determine the optimal zonal system for macroscopic crash modeling considering census tracts (CTs), state-wide traffic analysis zones (STAZs), and a newly developed traffic-related zone system labeled traffic analysis districts (TADs). Poisson lognormal models for three crash types (i.e., total, severe, and non-motorized mode crashes) are developed based on the three zonal systems without and with consideration of spatial autocorrelation. The study proposes a method to compare the modeling performance of the three types of geographic units at different spatial configurations through a grid based framework. Specifically, the study region is partitioned to grids of various sizes and the model prediction accuracy of the various macro models is considered within these grids of various sizes. These model comparison results for all crash types indicated that the models based on TADs consistently offer a better performance compared to the others. Besides, the models considering spatial autocorrelation outperform the ones that do not consider it. Based on the modeling results and motivation for developing the different zonal systems, it is recommended using CTs for socio-demographic data collection, employing TAZs for transportation demand forecasting, and adopting TADs for transportation safety planning. The findings from this study can help practitioners select appropriate zonal systems for traffic crash modeling, which leads to develop more efficient policies to enhance transportation safety. Copyright © 2017 Elsevier Ltd and National Safety Council. All rights reserved.

Quantitative approaches in climate change ecology

PubMed Central

Brown, Christopher J; Schoeman, David S; Sydeman, William J; Brander, Keith; Buckley, Lauren B; Burrows, Michael; Duarte, Carlos M; Moore, Pippa J; Pandolfi, John M; Poloczanska, Elvira; Venables, William; Richardson, Anthony J

2011-01-01

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer-reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non-climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.

Autoregressive modelling of species richness in the Brazilian Cerrado.

PubMed

Vieira, C M; Blamires, D; Diniz-Filho, J A F; Bini, L M; Rangel, T F L V B

2008-05-01

Spatial autocorrelation is the lack of independence between pairs of observations at given distances within a geographical space, a phenomenon commonly found in ecological data. Taking into account spatial autocorrelation when evaluating problems in geographical ecology, including gradients in species richness, is important to describe both the spatial structure in data and to correct the bias in Type I errors of standard statistical analyses. However, to effectively solve these problems it is necessary to establish the best way to incorporate the spatial structure to be used in the models. In this paper, we applied autoregressive models based on different types of connections and distances between 181 cells covering the Cerrado region of Central Brazil to study the spatial variation in mammal and bird species richness across the biome. Spatial structure was stronger for birds than for mammals, with R(2) values ranging from 0.77 to 0.94 for mammals and from 0.77 to 0.97 for birds, for models based on different definitions of spatial structures. According to the Akaike Information Criterion (AIC), the best autoregressive model was obtained by using the rook connection. In general, these results furnish guidelines for future modelling of species richness patterns in relation to environmental predictors and other variables expressing human occupation in the biome.

A method to detect layover and shadow based on distributed spaceborne single-baseline InSAR

NASA Astrophysics Data System (ADS)

Yun, Ren; Huanxin, Zou; Shilin, Zhou; Hao, Sun; Kefeng, Ji

2014-03-01

Layover and Shadow are inevitable phenomenena in InSAR, which seriously destroy the continuity of interferometric phase images and present difficulties in the follow-up phase unwrapping. Thus, it's significant to detect layover and shadow. This paper presents an approach to detect layover and shadow using the auto-correlation matrix and amplitude of the two images. The method can make full use of the spatial information of neighboring pixels and effectively detect layover and shadow regions in the case of low registration accuracy. Experiment result on the simulated data verifies effectiveness of the algorithm.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

Measuring the value of air quality: application of the spatial hedonic model.

PubMed

Kim, Seung Gyu; Cho, Seong-Hoon; Lambert, Dayton M; Roberts, Roland K

2010-03-01

This study applies a hedonic model to assess the economic benefits of air quality improvement following the 1990 Clean Air Act Amendment at the county level in the lower 48 United States. An instrumental variable approach that combines geographically weighted regression and spatial autoregression methods (GWR-SEM) is adopted to simultaneously account for spatial heterogeneity and spatial autocorrelation. SEM mitigates spatial dependency while GWR addresses spatial heterogeneity by allowing response coefficients to vary across observations. Positive amenity values of improved air quality are found in four major clusters: (1) in East Kentucky and most of Georgia around the Southern Appalachian area; (2) in a few counties in Illinois; (3) on the border of Oklahoma and Kansas, on the border of Kansas and Nebraska, and in east Texas; and (4) in a few counties in Montana. Clusters of significant positive amenity values may exist because of a combination of intense air pollution and consumer awareness of diminishing air quality.

Social inequalities in residential exposure to road traffic noise: an environmental justice analysis based on the RECORD Cohort Study.

PubMed

Havard, Sabrina; Reich, Brian J; Bean, Kathy; Chaix, Basile

2011-05-01

To explore social inequalities in residential exposure to road traffic noise in an urban area. Environmental injustice in road traffic noise exposure was investigated in Paris, France, using the RECORD Cohort Study (n = 2130) and modelled noise data. Associations were assessed by estimating noise exposure within the local area around participants' residence, considering various socioeconomic variables defined at both individual and neighbourhood level, and comparing different regression models attempting or not to control for spatial autocorrelation in noise levels. After individual-level adjustment, participants' noise exposure increased with neighbourhood educational level and dwelling value but also with proportion of non-French citizens, suggesting seemingly contradictory findings. However, when country of citizenship was defined according to its human development level, noise exposure in fact increased and decreased with the proportions of citizens from advantaged and disadvantaged countries, respectively. These findings were consistent with those reported for the other socioeconomic characteristics, suggesting higher road traffic noise exposure in advantaged neighbourhoods. Substantial collinearity between neighbourhood explanatory variables and spatial random effects caused identifiability problems that prevented successful control for spatial autocorrelation. Contrary to previous literature, this study shows that people living in advantaged neighbourhoods were more exposed to road traffic noise in their residential environment than their deprived counterparts. This case study demonstrates the need to systematically perform sensitivity analyses with multiple socioeconomic characteristics to avoid incorrect inferences about an environmental injustice situation and the complexity of effectively controlling for spatial autocorrelation when fixed and random components of the model are correlated.

[Spatial Distribution Characteristics of Different Species Mercury in Water Body of Changshou Lake in Three Gorges Reservoir Region].

PubMed

Bai, Wei-yang; Zhang, Cheng; Zhao, Zheng; Tang, Zhen-ya; Wang, Ding-yong

2015-08-01

An investigation on the concentrations and the spatial distribution characteristics of different species of mercury in the water body of Changshou Lake in Three Gorges Reservoir region was carried out based on the AreGIS statistics module. The results showed that the concentration of the total mercury in Changshou Lake surface water ranged from 0.50 to 3.78 ng x L(-1), with an average of 1.51 ng x L(-1); the concentration of the total MeHg (methylmercury) ranged from 0.10 to 0.75 ng x L(-1), with an average of 0.23 ng x L(-1). The nugget effect value of total mercury in surface water (50.65%), dissolved mercury (49.80%), particulate mercury (29.94%) and the activity mercury (26.95%) were moderate spatial autocorrelation. It indicated that the autocorrelation was impacted by the intrinsic properties of sediments (such as parent materials and rocks, geological mineral and terrain), and on the other hand it was also disturbed by the exogenous input factors (such as aquaculture, industrial activities, farming etc). The nugget effect value of dissolved methylmercury (DMeHg) in Changshou lake surface water (3.49%) was less than 25%, showing significant strong spatial autocorrelation. The distribution was mainly controlled by environmental factors in water. The proportion of total MeHg in total Hg in Changshou Lake water reached 30% which was the maximum ratio of the total MeHg to total Hg in freshwater lakes and rivers. It implied that mercury was easily methylated in the environment of Chanashou Lake.

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.

Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003-2015): Implications for Prevention and Control Policies.

PubMed

Zhu, Bin; Liu, Jinlin; Fu, Yang; Zhang, Bo; Mao, Ying

2018-04-02

Viral hepatitis, as one of the most serious notifiable infectious diseases in China, takes heavy tolls from the infected and causes a severe economic burden to society, yet few studies have systematically explored the spatio-temporal epidemiology of viral hepatitis in China. This study aims to explore, visualize and compare the epidemiologic trends and spatial changing patterns of different types of viral hepatitis (A, B, C, E and unspecified, based on the classification of CDC) at the provincial level in China. The growth rates of incidence are used and converted to box plots to visualize the epidemiologic trends, with the linear trend being tested by chi-square linear by linear association test. Two complementary spatial cluster methods are used to explore the overall agglomeration level and identify spatial clusters: spatial autocorrelation analysis (measured by global and local Moran's I) and space-time scan analysis. Based on the spatial autocorrelation analysis, the hotspots of hepatitis A remain relatively stable and gradually shrunk, with Yunnan and Sichuan successively moving out the high-high (HH) cluster area. The HH clustering feature of hepatitis B in China gradually disappeared with time. However, the HH cluster area of hepatitis C has gradually moved towards the west, while for hepatitis E, the provincial units around the Yangtze River Delta region have been revealing HH cluster features since 2005. The space-time scan analysis also indicates the distinct spatial changing patterns of different types of viral hepatitis in China. It is easy to conclude that there is no one-size-fits-all plan for the prevention and control of viral hepatitis in all the provincial units. An effective response requires a package of coordinated actions, which should vary across localities regarding the spatial-temporal epidemic dynamics of each type of virus and the specific conditions of each provincial unit.

Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics

PubMed Central

Wong, Man Sing; Peng, Fen; Zou, Bin; Shi, Wen Zhong; Wilson, Gaines J.

2016-01-01

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity “hotspots” were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect. PMID:26985899

Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics.

PubMed

Wong, Man Sing; Peng, Fen; Zou, Bin; Shi, Wen Zhong; Wilson, Gaines J

2016-03-12

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity "hotspots" were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect.

Spatial Assessment of Model Errors from Four Regression Techniques

Treesearch

Lianjun Zhang; Jeffrey H. Gove; Jeffrey H. Gove

2005-01-01

Fomst modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographicalIy weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study...

Periodicity in spatial data and geostatistical models: autocorrelation between patches

Treesearch

Volker C. Radeloff; Todd F. Miller; Hong S. He; David J. Mladenoff

2000-01-01

Several recent studies in landscape ecology have found periodicity in correlograms or semi-variograms calculated, for instance, from spatial data of soils, forests, or animal populations. Some of the studies interpreted this as an indication of regular or periodic landscape patterns. This interpretation is in disagreement with other studies that doubt whether such...

Comparing spatial regression to random forests for large ...

EPA Pesticide Factsheets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Spatial variability of organic layer thickness and carbon stocks in mature boreal forest stands--implications and suggestions for sampling designs.

PubMed

Kristensen, Terje; Ohlson, Mikael; Bolstad, Paul; Nagy, Zoltan

2015-08-01

Accurate field measurements from inventories across fine spatial scales are critical to improve sampling designs and to increase the precision of forest C cycling modeling. By studying soils undisturbed from active forest management, this paper gives a unique insight in the naturally occurring variability of organic layer C and provides valuable references against which subsequent and future sampling schemes can be evaluated. We found that the organic layer C stocks displayed great short-range variability with spatial autocorrelation distances ranging from 0.86 up to 2.85 m. When spatial autocorrelations are known, we show that a minimum of 20 inventory samples separated by ∼5 m is needed to determine the organic layer C stock with a precision of ±0.5 kg C m(-2). Our data also demonstrates a strong relationship between the organic layer C stock and horizon thickness (R (2) ranging from 0.58 to 0.82). This relationship suggests that relatively inexpensive measurements of horizon thickness can supplement soil C sampling, by reducing the number of soil samples collected, or to enhance the spatial resolution of organic layer C mapping.

Effects of multiple founder populations on spatial genetic structure of reintroduced American martens.

PubMed

Williams, Bronwyn W; Scribner, Kim T

2010-01-01

Reintroductions and translocations are increasingly used to repatriate or increase probabilities of persistence for animal and plant species. Genetic and demographic characteristics of founding individuals and suitability of habitat at release sites are commonly believed to affect the success of these conservation programs. Genetic divergence among multiple source populations of American martens (Martes americana) and well documented introduction histories permitted analyses of post-introduction dispersion from release sites and development of genetic clusters in the Upper Peninsula (UP) of Michigan <50 years following release. Location and size of spatial genetic clusters and measures of individual-based autocorrelation were inferred using 11 microsatellite loci. We identified three genetic clusters in geographic proximity to original release locations. Estimated distances of effective gene flow based on spatial autocorrelation varied greatly among genetic clusters (30-90 km). Spatial contiguity of genetic clusters has been largely maintained with evidence for admixture primarily in localized regions, suggesting recent contact or locally retarded rates of gene flow. Data provide guidance for future studies of the effects of permeabilities of different land-cover and land-use features to dispersal and of other biotic and environmental factors that may contribute to the colonization process and development of spatial genetic associations.

Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

NASA Astrophysics Data System (ADS)

Cui, Binge; Ma, Xiudan; Xie, Xiaoyun; Ren, Guangbo; Ma, Yi

2017-03-01

The classification of hyperspectral images with a few labeled samples is a major challenge which is difficult to meet unless some spatial characteristics can be exploited. In this study, we proposed a novel spectral-spatial hyperspectral image classification method that exploited spatial autocorrelation of hyperspectral images. First, image segmentation is performed on the hyperspectral image to assign each pixel to a homogeneous region. Second, the visible and infrared bands of hyperspectral image are partitioned into multiple subsets of adjacent bands, and each subset is merged into one band. Recursive edge-preserving filtering is performed on each merged band which utilizes the spectral information of neighborhood pixels. Third, the resulting spectral and spatial feature band set is classified using the SVM classifier. Finally, bilateral filtering is performed to remove "salt-and-pepper" noise in the classification result. To preserve the spatial structure of hyperspectral image, edge-preserving filtering is applied independently before and after the classification process. Experimental results on different hyperspectral images prove that the proposed spectral-spatial classification approach is robust and offers more classification accuracy than state-of-the-art methods when the number of labeled samples is small.

Information content exploitation of imaging spectrometer's images for lossless compression

NASA Astrophysics Data System (ADS)

Wang, Jianyu; Zhu, Zhenyu; Lin, Kan

1996-11-01

Imaging spectrometer, such as MAIS produces a tremendous volume of image data with up to 5.12 Mbps raw data rate, which needs urgently a real-time, efficient and reversible compression implementation. Between the lossy scheme with high compression ratio and the lossless scheme with high fidelity, we must make our choice based on the particular information content analysis of each imaging spectrometer's image data. In this paper, we present a careful analysis of information-preserving compression of imaging spectrometer MAIS with an entropy and autocorrelation study on the hyperspectral images. First, the statistical information in an actual MAIS image, captured in Marble Bar Australia, is measured with its entropy, conditional entropy, mutual information and autocorrelation coefficients on both spatial dimensions and spectral dimension. With these careful analyses, it is shown that there is high redundancy existing in the spatial dimensions, but the correlation in spectral dimension of the raw images is smaller than expected. The main reason of the nonstationarity on spectral dimension is attributed to the instruments's discrepancy on detector's response and channel's amplification in different spectral bands. To restore its natural correlation, we preprocess the signal in advance. There are two methods to accomplish this requirement: onboard radiation calibration and normalization. A better result can be achieved by the former one. After preprocessing, the spectral correlation increases so high that it contributes much redundancy in addition to spatial correlation. At last, an on-board hardware implementation for the lossless compression is presented with an ideal result.

Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China.

PubMed

Li, Yifan; Wang, Juanle; Gao, Mengxu; Fang, Liqun; Liu, Changhua; Lyu, Xin; Bai, Yongqing; Zhao, Qiang; Li, Hairong; Yu, Hongjie; Cao, Wuchun; Feng, Liqiang; Wang, Yanjun; Zhang, Bin

2017-05-26

Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The impact degree of these factors on TBE risks has the following descending order: temperature, relative humidity, vegetation coverage, precipitation and topography. A high-risk area with a triangle shape was determined in the central part of Hulunbuir; the low-risk area is located in the two belts next to the outside edge of the central triangle. The TBE risk distribution revealed that the impact of the geographic factors changed depending on the heterogeneity.

Correlation time and diffusion coefficient imaging: application to a granular flow system.

PubMed

Caprihan, A; Seymour, J D

2000-05-01

A parametric method for spatially resolved measurements for velocity autocorrelation functions, R(u)(tau) = , expressed as a sum of exponentials, is presented. The method is applied to a granular flow system of 2-mm oil-filled spheres rotated in a half-filled horizontal cylinder, which is an Ornstein-Uhlenbeck process with velocity autocorrelation function R(u)(tau) = e(- ||tau ||/tau(c)), where tau(c) is the correlation time and D = tau(c) is the diffusion coefficient. The pulsed-field-gradient NMR method consists of applying three different gradient pulse sequences of varying motion sensitivity to distinguish the range of correlation times present for particle motion. Time-dependent apparent diffusion coefficients are measured for these three sequences and tau(c) and D are then calculated from the apparent diffusion coefficient images. For the cylinder rotation rate of 2.3 rad/s, the axial diffusion coefficient at the top center of the free surface was 5.5 x 10(-6) m(2)/s, the correlation time was 3 ms, and the velocity fluctuation or granular temperature was 1.8 x 10(-3) m(2)/s(2). This method is also applicable to study transport in systems involving turbulence and porous media flows. Copyright 2000 Academic Press.

Fine-scale natal homing and localized movement as shaped by sex and spawning habitat in Chinook salmon: Insights from spatial autocorrelation analysis of individual genotypes

Treesearch

H. M. Neville; D. J. Isaak; J. B. Dunham; R. F. Thurow; B. E. Rieman

2006-01-01

Natal homing is a hallmark of the life history of salmonid fishes, but the spatial scale of homing within local, naturally reproducing salmon populations is still poorly understood. Accurate homing (paired with restricted movement) should lead to the existence of finescale genetic structuring due to the spatial clustering of related individuals on spawning grounds....

Cross-scale analysis of cluster correspondence using different operational neighborhoods

NASA Astrophysics Data System (ADS)

Lu, Yongmei; Thill, Jean-Claude

2008-09-01

Cluster correspondence analysis examines the spatial autocorrelation of multi-location events at the local scale. This paper argues that patterns of cluster correspondence are highly sensitive to the definition of operational neighborhoods that form the spatial units of analysis. A subset of multi-location events is examined for cluster correspondence if they are associated with the same operational neighborhood. This paper discusses the construction of operational neighborhoods for cluster correspondence analysis based on the spatial properties of the underlying zoning system and the scales at which the zones are aggregated into neighborhoods. Impacts of this construction on the degree of cluster correspondence are also analyzed. Empirical analyses of cluster correspondence between paired vehicle theft and recovery locations are conducted on different zoning methods and across a series of geographic scales and the dynamics of cluster correspondence patterns are discussed.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

PubMed Central

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-01-01

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention. PMID:27827946

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

PubMed

Mahara, Gehendra; Wang, Chao; Yang, Kun; Chen, Sipeng; Guo, Jin; Gao, Qi; Wang, Wei; Wang, Quanyi; Guo, Xiuhua

2016-11-04

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran's I statistic and Anselin's local Moran's I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R² = 0.0741, log likelihood = -1819.69, AIC = 3665.38), SLM (R² = 0.0786, log likelihood = -1819.04, AIC = 3665.08) and SEM (R² = 0.0743, log likelihood = -1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide ( p = 0.027), rainfall ( p = 0.036) and sunshine hour ( p = 0.048), while the relative humidity ( p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

Landscape patterns from mathematical morphology on maps with contagion

Treesearch

Kurt Riitters; Peter Vogt; Pierre Soille; Christine Estreguil

2009-01-01

The perceived realism of simulated maps with contagion (spatial autocorrelation) has led to their use for comparing landscape pattern metrics and as habitat maps for modeling organism movement across landscapes. The objective of this study was to conduct a neutral model analysis of pattern metrics defined by morphological spatial pattern analysis (MSPA) on maps with...

a Geographic Weighted Regression for Rural Highways Crashes Modelling Using the Gaussian and Tricube Kernels: a Case Study of USA Rural Highways

NASA Astrophysics Data System (ADS)

Aghayari, M.; Pahlavani, P.; Bigdeli, B.

2017-09-01

Based on world health organization (WHO) report, driving incidents are counted as one of the eight initial reasons for death in the world. The purpose of this paper is to develop a method for regression on effective parameters of highway crashes. In the traditional methods, it was assumed that the data are completely independent and environment is homogenous while the crashes are spatial events which are occurring in geographic space and crashes have spatial data. Spatial data have spatial features such as spatial autocorrelation and spatial non-stationarity in a way working with them is going to be a bit difficult. The proposed method has implemented on a set of records of fatal crashes that have been occurred in highways connecting eight east states of US. This data have been recorded between the years 2007 and 2009. In this study, we have used GWR method with two Gaussian and Tricube kernels. The Number of casualties has been considered as dependent variable and number of persons in crash, road alignment, number of lanes, pavement type, surface condition, road fence, light condition, vehicle type, weather, drunk driver, speed limitation, harmful event, road profile, and junction type have been considered as explanatory variables according to previous studies in using GWR method. We have compered the results of implementation with OLS method. Results showed that R2 for OLS method is 0.0654 and for the proposed method is 0.9196 that implies the proposed GWR is better method for regression in rural highway crashes.

Spatial autocorrelation of West Nile virus vector mosquito abundance in a seasonally wet suburban environment

NASA Astrophysics Data System (ADS)

Trawinski, P. R.; Mackay, D. S.

2009-03-01

The objective of this study is to quantify and model spatial dependence in mosquito vector populations and develop predictions for unsampled locations using geostatistics. Mosquito control program trap sites are often located too far apart to detect spatial dependence but the results show that integration of spatial data over time for Cx. pipiens-restuans and according to meteorological conditions for Ae. vexans enables spatial analysis of sparse sample data. This study shows that mosquito abundance is spatially correlated and that spatial dependence differs between Cx. pipiens-restuans and Ae. vexans mosquitoes.

Porous media flux sensitivity to pore-scale geostatistics: A bottom-up approach

NASA Astrophysics Data System (ADS)

Di Palma, P. R.; Guyennon, N.; Heße, F.; Romano, E.

2017-04-01

Macroscopic properties of flow through porous media can be directly computed by solving the Navier-Stokes equations at the scales related to the actual flow processes, while considering the porous structures in an explicit way. The aim of this paper is to investigate the effects of the pore-scale spatial distribution on seepage velocity through numerical simulations of 3D fluid flow performed by the lattice Boltzmann method. To this end, we generate multiple random Gaussian fields whose spatial correlation follows an assigned semi-variogram function. The Exponential and Gaussian semi-variograms are chosen as extreme-cases of correlation for short distances and statistical properties of the resulting porous media (indicator field) are described using the Matèrn covariance model, with characteristic lengths of spatial autocorrelation (pore size) varying from 2% to 13% of the linear domain. To consider the sensitivity of the modeling results to the geostatistical representativeness of the domain as well as to the adopted resolution, porous media have been generated repetitively with re-initialized random seeds and three different resolutions have been tested for each resulting realization. The main difference among results is observed between the two adopted semi-variograms, indicating that the roughness (short distances autocorrelation) is the property mainly affecting the flux. However, computed seepage velocities show additionally a wide variability (about three orders of magnitude) for each semi-variogram model in relation to the assigned correlation length, corresponding to pore sizes. The spatial resolution affects more the results for short correlation lengths (i.e., small pore sizes), resulting in an increasing underestimation of the seepage velocity with the decreasing correlation length. On the other hand, results show an increasing uncertainty as the correlation length approaches the domain size.

The spatial distribution of gender differences in obesity prevalence differs from overall obesity prevalence among US adults.

PubMed

Gartner, Danielle R; Taber, Daniel R; Hirsch, Jana A; Robinson, Whitney R

2016-04-01

Although obesity disparities between racial and socioeconomic groups have been well characterized, those based on gender and geography have not been as thoroughly documented. This study describes obesity prevalence by state, gender, and race and/or ethnicity to (1) characterize obesity gender inequality, (2) determine if the geographic distribution of inequality is spatially clustered, and (3) contrast the spatial clustering patterns of obesity gender inequality with overall obesity prevalence. Data from the Centers for Disease Control and Prevention's 2013 Behavioral Risk Factor Surveillance System were used to calculate state-specific obesity prevalence and gender inequality measures. Global and local Moran's indices were calculated to determine spatial autocorrelation. Age-adjusted, state-specific obesity prevalence difference and ratio measures show spatial autocorrelation (z-score = 4.89, P-value < .001). Local Moran's indices indicate the spatial distributions of obesity prevalence and obesity gender inequalities are not the same. High and low values of obesity prevalence and gender inequalities cluster in different areas of the United States. Clustering of gender inequality suggests that spatial processes operating at the state level, such as occupational or physical activity policies or social norms, are involved in the etiology of the inequality and necessitate further attention to the determinates of obesity gender inequality. Copyright © 2016 Elsevier Inc. All rights reserved.

Spatial pattern characteristics of water footprint for maize production in Northeast China.

PubMed

Duan, Peili; Qin, Lijie; Wang, Yeqiao; He, Hongshi

2016-01-30

Water footprint (WF) methodology is essential for quantifying total water consumption of crop production and making efficient water management policies. This study calculated the green, blue, grey and total WFs of maize production in Northeast China from 1998 to 2012 and compared the values of the provinces. This study also analyzed the spatial variation and structure characteristics of the WFs at the prefecture level. The annual average WF of maize production was 1029 m(3) per ton, which was 51% green, 21% blue and 28% grey. The WF of maize production was highest in Liaoning Province, moderate in Heilongjiang Province and lowest in Jilin Province. The spatial differences of the WFs calculated for the 36 major maize production prefectures were significant in Northeast China. There was a moderate positive spatial autocorrelation among prefectures that had similar WFs. Local indicator of spatial autocorrelation index (LISA) analysis identified prefectures with higher WFs in the southeast region of Liaoning Province and the southwest region of Heilongjiang Province and prefectures with lower WFs in the middle of Jilin Province. Spatial differences in the WF of maize production were caused mainly by variations in climate conditions, soil quality, irrigation facilities and maize yield. The spatial distribution of WFs can help provide a scientific basis for optimizing maize production distribution and then formulate strategies to reduce the WF of maize production. © 2015 Society of Chemical Industry.

The Uncertainties on the GIS Based Land Suitability Assessment for Urban and Rural Planning

NASA Astrophysics Data System (ADS)

Liu, H.; Zhan, Q.; Zhan, M.

2017-09-01

The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

Integrating Eddy Covariance, Penman-Monteith and METRIC based Evapotranspiration estimates to generate high resolution space-time ET over the Brazos River Basin

NASA Astrophysics Data System (ADS)

Mbabazi, D.; Mohanty, B.; Gaur, N.

2017-12-01

Evapotranspiration (ET) is an important component of the water and energy balance and accounts for 60 -70% of precipitation losses. However, accurate estimates of ET are difficult to quantify at varying spatial and temporal scales. Eddy covariance methods estimate ET at high temporal resolutions but without capturing the spatial variation in ET within its footprint. On the other hand, remote sensing methods using Landsat imagery provide ET with high spatial resolution but low temporal resolution (16 days). In this study, we used both eddy covariance and remote sensing methods to generate high space-time resolution ET. Daily, monthly and seasonal ET estimates were obtained using the eddy covariance (EC) method, Penman-Monteith (PM) and Mapping Evapotranspiration with Internalized Calibration (METRIC) models to determine cotton and native prairie ET dynamics in the Brazos river basin characterized by varying hydro-climatic and geological gradients. Daily estimates of spatially distributed ET (30 m resolution) were generated using spatial autocorrelation and temporal interpolations between the EC flux variable footprints and METRIC ET for the 2016 and 2017 growing seasons. A comparison of the 2016 and 2017 preliminary daily ET estimates showed similar ET dynamics/trends among the EC, PM and METRIC methods, and 5-20% differences in seasonal ET estimates. This study will improve the spatial estimates of EC ET and temporal resolution of satellite derived ET thus providing better ET data for water use management.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

A Bayesian method for assessing multiscalespecies-habitat relationships

USGS Publications Warehouse

Stuber, Erica F.; Gruber, Lutz F.; Fontaine, Joseph J.

2017-01-01

ContextScientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multi-scale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large.ObjectivesOur objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance.MethodsWe introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA.ResultsOur method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%.ConclusionsGiven the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships.

Autocorrelation of the susceptible-infected-susceptible process on networks

NASA Astrophysics Data System (ADS)

Liu, Qiang; Van Mieghem, Piet

2018-06-01

In this paper, we focus on the autocorrelation of the susceptible-infected-susceptible (SIS) process on networks. The N -intertwined mean-field approximation (NIMFA) is applied to calculate the autocorrelation properties of the exact SIS process. We derive the autocorrelation of the infection state of each node and the fraction of infected nodes both in the steady and transient states as functions of the infection probabilities of nodes. Moreover, we show that the autocorrelation can be used to estimate the infection and curing rates of the SIS process. The theoretical results are compared with the simulation of the exact SIS process. Our work fully utilizes the potential of the mean-field method and shows that NIMFA can indeed capture the autocorrelation properties of the exact SIS process.

Characterization of Forested Landscapes From Remotely Sensed Data Using Fractals and Spatial Autocorrelation

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad Z.; Cruise, James F.; Rickman, Douglas L.; Quattrochi, Dale A.

2007-01-01

The characterization of forested areas is frequently required in resource management practice. Passive remotely sensed data, which are much more accessible and cost effective than are active data, have rarely, if ever, been used to characterize forest structure directly, but rather they usually focus on the estimation of indirect measurement of biomass or canopy coverage. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions, along with a simple spatial autocorrelation technique (Moran s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results of the case study presented herein have shown that as the percentage of smaller diameter trees becomes greater, and particularly if it exceeds 50%, then the canopy image obtained from Landsat TM data becomes sufficiently homogeneous so that the spatial indices reach their lower limits and thus are no longer determinative. It also appears, at least for the Oakmulgee forest, that the relationships between the spatial indices and forest class percentages within the boundaries can reasonably be considered linear. The linear relationship is much more pronounced in the sawtimber and saplings cases than in samples dominated by medium sized trees (poletimber). In addition, it also appears that, at least for the Oakmulgee forest, the relationships between the spatial indices and forest species groups (Hardwood and Softwood) percentages can reasonably be considered linear. The linear relationship is more pronounced in the forest species groups cases than in the forest classes cases. These results appear to indicate that both fractal dimensions and spatial autocorrelation indices hold promise as means of estimating forest stand characteristics from remotely sensed images. However, additional work is needed to confirm that the boundaries identified for Oakmulgee forest and the linear nature of the relationship between image complexity indices and forest characteristics are generally evident in other forests. In addition, the effects of other parameters such ,as topographic relief and image distortion due to sun angle and cloud cover, for example, need to be examined.

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

Animal movement data: GPS telemetry, autocorrelation and the need for path-level analysis [chapter 7

Treesearch

Samuel A. Cushman

2010-01-01

In the previous chapter we presented the idea of a multi-layer, multi-scale, spatially referenced data-cube as the foundation for monitoring and for implementing flexible modeling of ecological pattern-process relationships in particulate, in context and to integrate these across large spatial extents at the grain of the strongest linkage between response and driving...

Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

NASA Astrophysics Data System (ADS)

Verma, S.; Gupta, R. D.

2014-11-01

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

Effects of calcium leaching on diffusion properties of hardened and altered cement pastes

NASA Astrophysics Data System (ADS)

Kurumisawa, Kiyofumi; Haga, Kazuko; Hayashi, Daisuke; Owada, Hitoshi

2017-06-01

It is very important to predict alterations in the concrete used for fabricating disposal containers for radioactive waste. Therefore, it is necessary to understand the alteration of cementitious materials caused by calcium leaching when they are in contact with ground water in the long term. To evaluate the long-term transport characteristics of cementitious materials, the microstructural behavior of these materials should be considered. However, many predictive models of transport characteristics focus on the pore structure, while only few such models consider both, the spatial distribution of calcium silicate hydrate (C-S-H), portlandite, and the pore spaces. This study focused on the spatial distribution of these cement phases. The auto-correlation function of each phase of cementitious materials was calculated from two-dimensional backscattered electron imaging, and the three-dimensional spatial image of the cementitious material was produced using these auto-correlation functions. An attempt was made to estimate the diffusion coefficient of chloride from the three-dimensional spatial image. The estimated diffusion coefficient of the altered sample from the three-dimensional spatial image was found to be comparable to the measured value. This demonstrated that it is possible to predict the diffusion coefficient of the altered cement paste by using the proposed model.

Spatial variation of air quality index and urban driving factors linkages: evidence from Chinese cities.

PubMed

Pu, Haixia; Luo, Kunli; Wang, Pin; Wang, Shaobin; Kang, Shun

2017-02-01

Daily air quality index (AQI) of 161 Chinese cities obtained from the Ministry of Environmental Protection of China in 2015 is conducted. In this study, to better explore spatial distribution and regional characteristic of AQI, global and local spatial autocorrelation is utilized. Pearson's correlation is introduced to determine the influence of single urban indicator on AQI value. Meanwhile, multiple linear stepwise regression is chosen to estimate quantitatively the most influential urban indicators on AQI. The spatial autocorrelation analysis indicates that the AQI value of Chinese 161 cities shows a spatial dependency. Higher AQI is mainly located in north and northwest regions, whereas low AQI is concentrated in the south and the Qinghai-Tibet regions. The low AQI and high AQI values in China both exhibit relative immobility through seasonal variation. The influence degree of three adverse urban driving factors on AQI value is ranked from high to low: coal consumption of manufacturing > building area > coal consumption of the power industry. It is worth noting that the risk of exposed population to poor quality is greater in the northern region than in other regions. The results of the study provide a reference for the formulation of urban policy and improvement of air quality in China.

Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics

NASA Astrophysics Data System (ADS)

Westerberg, I.; Walther, A.; Guerrero, J.-L.; Coello, Z.; Halldin, S.; Xu, C.-Y.; Chen, D.; Lundin, L.-C.

2010-08-01

An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913-2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap-filling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4-5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitation-generating mechanisms and the need for an improved monitoring network.

[Downscaling research of spatial distribution of incidence of hand foot and mouth disease based on area-to-area Poisson Kriging method].

PubMed

Wang, J X; Hu, M G; Yu, S C; Xiao, G X

2017-09-10

Objective: To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources. Methods: The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected. Downscaling interpolation was conducted by using area-to-area Poisson Kriging method. The interpolation results were visualized by using geographic information system (GIS). The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township. Results: In the downscaling interpolation, the range of the fitting semi-variance equation was 20.38 km. Within the range, the incidence had correlation with each other. The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y =1.053 1 x , R (2)=0.99. The incidences at different scale were consistent. Conclusions: The incidence of HFMD had spatial autocorrelation within 20.38 km. When HFMD occurs in one place, it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km. Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

Mistaking geography for biology: inferring processes from species distributions.

PubMed

Warren, Dan L; Cardillo, Marcel; Rosauer, Dan F; Bolnick, Daniel I

2014-10-01

Over the past few decades, there has been a rapid proliferation of statistical methods that infer evolutionary and ecological processes from data on species distributions. These methods have led to considerable new insights, but they often fail to account for the effects of historical biogeography on present-day species distributions. Because the geography of speciation can lead to patterns of spatial and temporal autocorrelation in the distributions of species within a clade, this can result in misleading inferences about the importance of deterministic processes in generating spatial patterns of biodiversity. In this opinion article, we discuss ways in which patterns of species distributions driven by historical biogeography are often interpreted as evidence of particular evolutionary or ecological processes. We focus on three areas that are especially prone to such misinterpretations: community phylogenetics, environmental niche modelling, and analyses of beta diversity (compositional turnover of biodiversity). Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays.

PubMed

Shi, Junpeng; Hu, Guoping; Sun, Fenggang; Zong, Binfeng; Wang, Xin

2017-08-24

This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions.

Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays

PubMed Central

Hu, Guoping; Zong, Binfeng; Wang, Xin

2017-01-01

This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions. PMID:28837115

Experimental evaluation of fluctuating density and radiated noise from a high temperature jet

NASA Technical Reports Server (NTRS)

Massier, P. F.; Parthasarathy, S. P.; Cuffel, R. F.

1973-01-01

An experimental investigation has been conducted to characterize the fluctuating density within a high-temperature (1100 K) subsonic jet and to characterize by the noise radiated to the surroundings. Cross correlations obtained by introducing time delay to the signals detected from spatially separated crossed laser beams set up as a Schlieren system were used to determine radial and axial distributions of the convection velocity of the moving noise sources (eddies). In addition, the autocorrelation of the fluctuating density was evaluated in the moving frame of reference of the eddies. Also, the autocorrelation of the radiated noise in the moving reference frame was evaluated from cross correlations by introducing time delay to the signals detected by spatially separated pairs of microphones. Radial distributions of the mean velocity were obtained from measurements of the stagnation temperature, and stagnation and static pressures with the use of probes.

[Spatial heterogeneity and influencing factors of soil phosphorus concentration in a mid-subtropical Choerospondias axillaris deciduous broad-leaved forest, China.

PubMed

Hu, Rui Bin; Fang, Xi; Xiang, Wen Hua; Jiang, Fang; Lei, Pi Feng; Zhao, Li Juan; Zhu, Wen Juan; Deng, Xiang Wen

2016-03-01

In order to investigate spatial variations in soil phosphorus (P) concentration and the influencing factors, one permanent plot of 1 hm 2 was established and stand structure was surveyed in Choerospondias axillaries deciduous broadleaved forest in Dashanchong Forest Park in Changsha County, Hunan Province, China. Soil samples were collected with equidistant grid point sampling method and soil P concentration and its spatial variation were analyzed by using geo-statistics and geographical information system (GIS) techniques. The results showed that the variations of total P and available P concentrations in humus layer and in the soil profile at depth of 0-10, 10-20 and 20-30 cm were moderate and the available P showed higher variability in a specific soil layer compared with total P. Concentrations of total P and available P in soil decreased, while the variations increased with the increase in soil depth. The total P and available P showed high spatial autocorrelation, primarily resulted from the structural factors. The spatial heterogeneity of available P was stronger than that of total P, and the spatial autocorrelation ranges of total P and available P varied from 92.80 to 168.90 m and from 79.43 to 106.20 m in different soil layers, respectively. At the same soil depth, fractal dimensions of total P were higher than that of available P, with more complex spatial pattern, while available P showed stronger spatial correlation with stronger spatial structure. In humus layer and soil depths of 0-10, 10-20 and 20-30 cm, the spatial variation pattern of total P and available P concentrations showed an apparent belt-shaped and spot massive gradient change. The high value appeared at low elevation and valley position, and the low value appeared in the high elevation and ridge area. The total P and available P concentrations showed significantly negative correlation with elevation and litter, but the relationship with convexity, species, numbers and soil pH was not significant. The total P and available P exhibited significant positive correlations with soil organic carbon (SOC), total nitrogen concentration, indicating the leaching characteristics of soil P. Its spatial variability was affected by many interactive factors.

Autocorrelations of stellar light and mass at z˜ 0 and ˜1: from SDSS to DEEP2

NASA Astrophysics Data System (ADS)

Li, Cheng; White, Simon D. M.; Chen, Yanmei; Coil, Alison L.; Davis, Marc; De Lucia, Gabriella; Guo, Qi; Jing, Y. P.; Kauffmann, Guinevere; Willmer, Christopher N. A.; Zhang, Wei

2012-01-01

We present measurements of projected autocorrelation functions wp(rp) for the stellar mass of galaxies and for their light in the U, B and V bands, using data from the third data release of the DEEP2 Galaxy Redshift Survey and the final data release of the Sloan Digital Sky Survey (SDSS). We investigate the clustering bias of stellar mass and light by comparing these to projected autocorrelations of dark matter estimated from the Millennium Simulations (MS) at z= 1 and 0.07, the median redshifts of our galaxy samples. All of the autocorrelation and bias functions show systematic trends with spatial scale and waveband which are impressively similar at the two redshifts. This shows that the well-established environmental dependence of stellar populations in the local Universe is already in place at z= 1. The recent MS-based galaxy formation simulation of Guo et al. reproduces the scale-dependent clustering of luminosity to an accuracy better than 30 per cent in all bands and at both redshifts, but substantially overpredicts mass autocorrelations at separations below about 2 Mpc. Further comparison of the shapes of our stellar mass bias functions with those predicted by the model suggests that both the SDSS and DEEP2 data prefer a fluctuation amplitude of σ8˜ 0.8 rather than the σ8= 0.9 assumed by the MS.

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

PubMed Central

Tang, Yunwei; Jing, Linhai; Ding, Haifeng

2017-01-01

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods. PMID:29064416

Effect of long-term mechanical perturbation on intertidal soft-bottom meiofaunal community spatial structure

NASA Astrophysics Data System (ADS)

Boldina, Inna; Beninger, Peter G.; Le Coz, Maïwen

2014-01-01

Situated at the interface of the microbial and macrofaunal compartments, soft-bottom meiofauna accomplish important ecological functions. However, little is known of their spatial distribution in the benthic environment. To assess the effects of long-term mechanical disturbance on soft-bottom meiofaunal spatial distribution, we compared a site subjected to long-term clam digging to a nearby site untouched by such activities, in Bourgneuf Bay, on the Atlantic coast of France. Six patterned replicate samples were taken at 3, 6, 9, 12, 15, 18, 21 and 24 cm lags, all sampling stations being separated by 5 m. A combined correlogram-variogram approach was used to enhance interpretation of the meiofaunal spatial distribution; in particular, the definition of autocorrelation strength and its statistical significance, as well as the detailed characteristics of the periodic spatial structure of nematode assemblages, and the determination of the maximum distance of their spatial autocorrelation. At both sites, nematodes and copepods clearly exhibited aggregated spatial structure at the meso scale; this structure was attenuated at the impacted site. The nematode spatial distribution showed periodicity at the non-impacted site, but not at the impacted site. This is the first explicit report of a periodic process in meiofaunal spatial distribution. No such cyclic spatial process was observed for the more motile copepods at either site. This first study to indicate the impacts of long-term anthropogenic mechanical perturbation on meiofaunal spatial structure opens the door to a new dimension of mudflat ecology. Since macrofaunal predator search behaviour is known to be strongly influenced by prey spatial structure, the alteration of this structure may have important consequences for ecosystem functioning.

Separating temperature from other factors in phenological measurements

NASA Astrophysics Data System (ADS)

Schwartz, Mark D.; Hanes, Jonathan M.; Liang, Liang

2014-09-01

Phenological observations offer a simple and effective way to measure climate change effects on the biosphere. While some species in northern mixed forests show a highly sensitive site preference to microenvironmental differences (i.e., the species is present in certain areas and absent in others), others with a more plastic environmental response (e.g., Acer saccharum, sugar maple) allow provisional separation of the universal "background" phenological variation caused by in situ (possibly biological/genetic) variation from the microclimatic gradients in air temperature. Moran's I tests for spatial autocorrelation among the phenological data showed significant ( α ≤ 0.05) clustering across the study area, but random patterns within the microclimates themselves, with isolated exceptions. In other words, the presence of microclimates throughout the study area generally results in spatial autocorrelation because they impact the overall phenological development of sugar maple trees. However, within each microclimate (where temperature conditions are relatively uniform) there is little or no spatial autocorrelation because phenological differences are due largely to randomly distributed in situ factors. The phenological responses from 2008 and 2009 for two sugar maple phenological stages showed the relationship between air temperature degree-hour departure and phenological change ranged from 0.5 to 1.2 days earlier for each additional 100 degree-hours. Further, the standard deviations of phenological event dates within individual microclimates (for specific events and years) ranged from 2.6 to 3.8 days. Thus, that range of days is inferred to be the "background" phenological variation caused by factors other than air temperature variations, such as genetic differences between individuals.

Environmental Risk Assessment: Spatial Analysis of Chemical Hazards and Risks in South Korea

NASA Astrophysics Data System (ADS)

Yu, H.; Heo, S.; Kim, M.; Lee, W. K.; Jong-Ryeul, S.

2017-12-01

This study identified chemical hazard and risk levels in Korea by analyzing the spatial distribution of chemical factories and accidents. The number of chemical factories and accidents in 5-km2 grids were used as the attribute value for spatial analysis. First, semi-variograms were conducted to examine spatial distribution patterns and to identify spatial autocorrelation of chemical factories and accidents. Semi-variograms explained that the spatial distribution of chemical factories and accidents were spatially autocorrelated. Second, the results of the semi-variograms were used in Ordinary Kriging to estimate chemical hazard and risk level. The level values were extracted from the Ordinary Kriging result and their spatial similarity was examined by juxtaposing the two values with respect to their location. Six peaks were identified in both the hazard and risk estimation result, and the peaks correlated with major cities in Korea. Third, the estimated hazard and risk levels were classified with geometrical interval and could be classified into four quadrants: Low Hazard and Low Risk (LHLR), Low Hazard and High Risk (LHHR), High Hazard and Low Risk (HHLR), and High Hazard and High Risk (HHHR). The 4 groups identified different chemical safety management issues in Korea; relatively safe LHLR group, many chemical reseller factories were found in HHLR group, chemical transportation accidents were in the LHHR group, and an abundance of factories and accidents were in the HHHR group. Each quadrant represented different safety management obstacles in Korea, and studying spatial differences can support the establishment of an efficient risk management plan.

Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration

USGS Publications Warehouse

Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.

2011-01-01

Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones") with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity-a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.

Fine-scale spatial distribution of the common lugworm Arenicola marina, and effects of intertidal clam fishing

NASA Astrophysics Data System (ADS)

Boldina, Inna; Beninger, Peter G.

2014-04-01

Despite its ubiquity and its role as an ecosystem engineer on temperate intertidal mudflats, little is known of the spatial ecology of the lugworm Arenicola marina. We estimated lugworm densities and analyzed the spatial distribution of A. marina on a French Atlantic mudflat subjected to long-term clam digging activities, and compared these to a nearby pristine reference mudflat, using a combination of geostatistical techniques: point-pattern analysis, autocorrelation, and wavelet analysis. Lugworm densities were an order of magnitude greater at the reference site. Although A. marina showed an aggregative spatial distribution at both sites, the characteristics and intensity of aggregation differed markedly between sites. The reference site showed an inhibition process (regular distribution) at distances <7.5 cm, whereas the impacted site showed a random distribution at this scale. At distances from 15 cm to several tens of meters, the spatial distribution of A. marina was clearly aggregated at both sites; however, the autocorrelation strength was much weaker at the impacted site. In addition, the non-impacted site presented multi-scale spatial distribution, which was not evident at the impacted site. The differences observed between the spatial distributions of the fishing-impacted vs. the non-impacted site reflect similar findings for other components of these two mudflat ecosystems, suggesting common community-level responses to prolonged mechanical perturbation: a decrease in naturally-occurring aggregation. This change may have consequences for basic biological characteristics such as reproduction, recruitment, growth, and feeding.

Density fingering in spatially modulated Hele-Shaw cells

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

Toth, Tamara; Horvath, Dezso; Toth, Agota

Density fingering of the chlorite-tetrathionate reaction has been studied experimentally in a periodically heterogeneous Hele-Shaw cell where the heterogeneity is introduced in the form of spatial modulation of gap width along the front. Depending on the spatial wavelength, gap width, and chemical composition, three types of cellular structures have been observed. The initial evolution is characterized by dispersion curves, while the long time behavior is described by the change in the autocorrelation function of the front profile and in the mixing length of the patterns.

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Evaluation of Satellite Remote Sensing Albedo Retrievals over the Ablation Area of the Southwestern Greenland Ice Sheet

NASA Technical Reports Server (NTRS)

Moustafa, Samiah E.; Rennermalm, Asa K.; Roman, Miguel O.; Wang, Zhuosen; Schaaf, Crystal B.; Smith, Laurence C.; Koenig, Lora S.; Erb, Angela

2017-01-01

MODerate resolution Imaging Spectroradiometer (MODIS) albedo products have been validated over spatially uniform, snow-covered areas of the Greenland ice sheet (GrIS) using the so-called single 'point-to-pixel' method. This study expands on this methodology by applying a 'multiple-point-to-pixel' method and examination of spatial autocorrelation (here using semivariogram analysis) by using in situ observations, high-resolution World- View-2 (WV-2) surface reflectances, and MODIS Collection V006 daily blue-sky albedo over a spatially heterogeneous surfaces in the lower ablation zone in southwest Greenland. Our results using 232 ground-based samples within two MODIS pixels, one being more spatial heterogeneous than the other, show little difference in accuracy among narrow and broad band albedos (except for Band 2). Within the more homogenous pixel area, in situ and MODIS albedos were very close (error varied from -4% to +7%) and within the range of ASD standard errors. The semivariogram analysis revealed that the minimum observational footprint needed for a spatially representative sample is 30 m. In contrast, over the more spatially heterogeneous surface pixel, a minimum footprint size was not quantifiable due to spatial autocorrelation, and far exceeds the effective resolution of the MODIS retrievals. Over the high spatial heterogeneity surface pixel, MODIS is lower than ground measurements by 4-7%, partly due to a known in situ undersampling of darker surfaces that often are impassable by foot (e.g., meltwater features and shadowing effects over crevasses). Despite the sampling issue, our analysis errors are very close to the stated general accuracy of the MODIS product of 5%. Thus, our study suggests that the MODIS albedo product performs well in a very heterogeneous, low-albedo, area of the ice sheet ablation zone. Furthermore, we demonstrate that single 'point-to-pixel' methods alone are insufficient in characterizing and validating the variation of surface albedo displayed in the lower ablation area. This is true because the distribution of in situ data deviations from MODIS albedo show a substantial range, with the average values for the 10th and 90th percentiles being -0.30 and 0.43 across all bands. Thus, if only single point is taken for ground validation, and is randomly selected from either distribution tails, the error would appear to be considerable. Given the need for multiple in-situ points, concurrent albedo measurements derived from existing AWSs, (low-flying vehicles (airborne or unmanned) and high-resolution imagery (WV-2)) are needed to resolve high sub-pixel variability in the ablation zone, and thus, further improve our characterization of Greenland's surface albedo.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Does foreign direct investment affect environmental pollution in China's cities? A spatial econometric perspective.

PubMed

Liu, Qianqian; Wang, Shaojian; Zhang, Wenzhong; Zhan, Dongsheng; Li, Jiaming

2018-02-01

Environmental pollution has aroused extensive concern worldwide. Existing literature on the relationship between foreign direct investment (FDI) and environmental pollution has, however, seldom taken into account spatial effects. Addressing this gap, this paper investigated the spatial agglomeration effects and dynamics at work in FDI and environmental pollution (namely, in waste soot and dust, sulfur dioxide, and wastewater) in 285 Chinese cities during the period 2003-2014, using global and local measures of spatial autocorrelation. Our results showed significant spatial autocorrelation in FDI and environmental pollution levels, both of which demonstrated obvious path dependence characteristics in their geographical distribution. A range of agglomeration regions were observed. The high-value and low-value agglomeration areas of FDI were not fully consistent with those of environmental pollution. This result indicates that higher inflows of FDI did not necessarily lead to greater environmental pollution from a geographic perspective, and vice versa. Spatial panel data models were further adopted to explore the impact of FDI on environmental pollution. The results of a spatial lag model (SLM) and a spatial error model (SEM) revealed that the inflow of FDI had distinct effects on different environmental pollutants, thereby confirming the Pollution Heaven Hypothesis and Pollution Halo Hypothesis. The inflow of FDI was found to have reduced waste soot and dust pollution to a certain extent, while it increased the degree of wastewater and sulfur dioxide pollution. The findings set out in this paper hold significant implications for Chinese environmental pollution protection. Copyright © 2017 Elsevier B.V. All rights reserved.

Detecting defective electrical components in heterogeneous infra-red images by spatial control charts

NASA Astrophysics Data System (ADS)

Jamshidieini, Bahman; Fazaee, Reza

2016-05-01

Distribution network components connect machines and other loads to electrical sources. If resistance or current of any component is more than specified range, its temperature may exceed the operational limit which can cause major problems. Therefore, these defects should be found and eliminated according to their severity. Although infra-red cameras have been used for inspection of electrical components, maintenance prioritization of distribution cubicles is mostly based on personal perception and lack of training data prevents engineers from developing image processing methods. New research on the spatial control chart encouraged us to use statistical approaches instead of the pattern recognition for the image processing. In the present study, a new scanning pattern which can tolerate heavy autocorrelation among adjacent pixels within infra-red image was developed and for the first time combination of kernel smoothing, spatial control charts and local robust regression were used for finding defects within heterogeneous infra-red images of old distribution cubicles. This method does not need training data and this advantage is crucially important when the training data is not available.

Crustal thickness across the Trans-European Suture Zone from ambient noise autocorrelations

NASA Astrophysics Data System (ADS)

Becker, G.; Knapmeyer-Endrun, B.

2018-02-01

We derive autocorrelations from ambient seismic noise to image the reflectivity of the subsurface and to extract the Moho depth beneath the stations for two different data sets in Central Europe. The autocorrelations are calculated by smoothing the spectrum of the data in order to suppress high amplitude, narrow-band signals of industrial origin, applying a phase autocorrelation algorithm and time-frequency domain phase-weighted stacking. The stacked autocorrelation results are filtered and analysed predominantly in the frequency range of 1-2 Hz. Moho depth is automatically picked inside uncertainty windows obtained from prior information. The processing scheme we developed is applied to data from permanent seismic stations located in different geological provinces across Europe, with varying Moho depths between 25 and 50 km, and to the mainly short period temporary PASSEQ stations along seismic profile POLONAISE P4. The autocorrelation results are spatially and temporarily stable, but show a clear correlation with the existence of cultural noise. On average, a minimum of six months of data is needed to obtain stable results. The obtained Moho depth results are in good agreement with the subsurface model provided by seismic profiling, receiver function estimates and the European Moho depth map. In addition to extracting the Moho depth, it is possible to identify an intracrustal layer along the profile, again closely matching the seismic model. For more than half of the broad-band stations, another change in reflectivity within the mantle is observed and can be correlated with the lithosphere-asthenosphere boundary to the west and a mid-lithospheric discontinuity beneath the East European Craton. With the application of the developed autocorrelation processing scheme to different stations with varying crustal thicknesses, it is shown that Moho depth can be extracted independent of subsurface structure, when station coverage is low, when no strong seismic sources are present, and when only limited amounts of data are available.

a Simple Spatially Weighted Measure of Temporal Stability for Data with Limited Temporal Observations

NASA Astrophysics Data System (ADS)

Piburn, J.; Stewart, R.; Morton, A.

2017-10-01

Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These new developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Most existing methodologies simply cannot say anything interesting with so few data points, yet researchers are still tasked to work within in the confines of the data. Recently a method for characterizing instability in a time series with limitedtemporal observations was published. This method, Attribute Stability Index (ASI), uses an approximate entropy based method tocharacterize a time series' instability. In this paper we propose an explicitly spatially weighted extension of the Attribute StabilityIndex. By including a mechanism to account for spatial autocorrelation, this work represents a novel approach for the characterizationof space-time instability. As a case study we explore national youth male unemployment across the world from 1991-2014.

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

PubMed

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

Spatial autocorrelation of microbial communities atop a debris-covered glacier is evidence of a supraglacial chronosequence.

PubMed

Darcy, John L; King, Andrew J; Gendron, Eli M S; Schmidt, Steven K

2017-08-01

Although microbial communities from many glacial environments have been analyzed, microbes living in the debris atop debris-covered glaciers represent an understudied frontier in the cryosphere. The few previous molecular studies of microbes in supraglacial debris have either had limited phylogenetic resolution, limited spatial resolution (e.g. only one sample site on the glacier) or both. Here, we present the microbiome of a debris-covered glacier across all three domains of life, using a spatially-explicit sampling scheme to characterize the Middle Fork Toklat Glacier's microbiome from its terminus to sites high on the glacier. Our results show that microbial communities differ across the supraglacial transect, but surprisingly these communities are strongly spatially autocorrelated, suggesting the presence of a supraglacial chronosequence. This pattern is dominated by phototrophic microbes (both bacteria and eukaryotes) which are less abundant near the terminus and more abundant higher on the glacier. We use these data to refute the hypothesis that the inhabitants of the glacier are randomly deposited atmospheric microbes, and to provide evidence that succession from a predominantly photosynthetic to a more heterotrophic community is occurring on the glacier. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field

NASA Technical Reports Server (NTRS)

Franklin, Rima B.; Mills, Aaron L.

2003-01-01

To better understand the distribution of soil microbial communities at multiple spatial scales, a survey was conducted to examine the spatial organization of community structure in a wheat field in eastern Virginia (USA). Nearly 200 soil samples were collected at a variety of separation distances ranging from 2.5 cm to 11 m. Whole-community DNA was extracted from each sample, and community structure was compared using amplified fragment length polymorphism (AFLP) DNA fingerprinting. Relative similarity was calculated between each pair of samples and compared using geostatistical variogram analysis to study autocorrelation as a function of separation distance. Spatial autocorrelation was found at scales ranging from 30 cm to more than 6 m, depending on the sampling extent considered. In some locations, up to four different correlation length scales were detected. The presence of nested scales of variability suggests that the environmental factors regulating the development of the communities in this soil may operate at different scales. Kriging was used to generate maps of the spatial organization of communities across the plot, and the results demonstrated that bacterial distributions can be highly structured, even within a habitat that appears relatively homogeneous at the plot and field scale. Different subsets of the microbial community were distributed differently across the plot, and this is thought to be due to the variable response of individual populations to spatial heterogeneity associated with soil properties. c2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Fine-scale genetic structure and cryptic associations reveal evidence of kin-based sociality in the African forest elephant.

PubMed

Schuttler, Stephanie G; Philbrick, Jessica A; Jeffery, Kathryn J; Eggert, Lori S

2014-01-01

Spatial patterns of relatedness within animal populations are important in the evolution of mating and social systems, and have the potential to reveal information on species that are difficult to observe in the wild. This study examines the fine-scale genetic structure and connectivity of groups within African forest elephants, Loxodonta cyclotis, which are often difficult to observe due to forest habitat. We tested the hypothesis that genetic similarity will decline with increasing geographic distance, as we expect kin to be in closer proximity, using spatial autocorrelation analyses and Tau K(r) tests. Associations between individuals were investigated through a non-invasive genetic capture-recapture approach using network models, and were predicted to be more extensive than the small groups found in observational studies, similar to fission-fusion sociality found in African savanna (Loxodonta africana) and Asian (Elephas maximus) species. Dung samples were collected in Lopé National Park, Gabon in 2008 and 2010 and genotyped at 10 microsatellite loci, genetically sexed, and sequenced at the mitochondrial DNA control region. We conducted analyses on samples collected at three different temporal scales: a day, within six-day sampling sessions, and within each year. Spatial autocorrelation and Tau K(r) tests revealed genetic structure, but results were weak and inconsistent between sampling sessions. Positive spatial autocorrelation was found in distance classes of 0-5 km, and was strongest for the single day session. Despite weak genetic structure, individuals within groups were significantly more related to each other than to individuals between groups. Social networks revealed some components to have large, extensive groups of up to 22 individuals, and most groups were composed of individuals of the same matriline. Although fine-scale population genetic structure was weak, forest elephants are typically found in groups consisting of kin and based on matrilines, with some individuals having more associates than observed from group sizes alone.

Species density of waterbirds in offshore habitats in western Lake Erie

USGS Publications Warehouse

Stapanian, M.A.; Waite, Thomas A.

2003-01-01

Offshore censuses of birds are lacking for inland seas, such as the Laurentian Great Lakes, but may provide valuable information for managing species that are in conflict with human interests. Birds were counted along 31 established transects in four habitats in western Lake Erie: offshore of waterbird refuges, offshore of beaches with human development, on reefs and shoals, and in open water. A total of 161 10-min counts were conducted between 24 April and 1 September 2000. The mean number of aquatic bird species/kmA? (species density) was greater offshore of refuges than on open water. For all habitats combined, species density increased over time. This was mainly due to the arrival of Bonaparte's Gulls (Larus philadelphia) and Great Black-backed Gulls (L. marinus), two fall and winter residents that do not breed in the study area, and increased use of open water and reefs and shoals by Herring Gulls (L argentatus) and Ring-billed Gulls (L delawarensis) after the nesting season. Species density was not strongly spatially autocorrelated, either for all species or for only those species that were floating on the water when recorded. Neither Double-crested Cormorants (Phalacrocorax auritus) nor Herring Gulls exhibited spatial autocorrelation. In contrast, Bonaparte's and Ring-billed gulls exhibited positive spatial autocorrelations. Unlike marine studies, species density was only weakly associated with water depth. This result was due mainly to Double-crested Cormorants, the only diving bird species that lived year-round in the area, which preferred reefs and shoals (depth 3-6 m) over open water (10 m). The results suggest that offshore habitat influences species density in this area during the breeding and immediate post-breeding seasons.

Temporal and spatiotemporal autocorrelation of daily concentrations of Alnus, Betula, and Corylus pollen in Poland.

PubMed

Nowosad, J; Stach, A; Kasprzyk, I; Grewling, Ł; Latałowa, M; Puc, M; Myszkowska, D; Weryszko-Chmielewska, E; Piotrowska-Weryszko, K; Chłopek, K; Majkowska-Wojciechowska, B; Uruska, A

The aim of the study was to determine the characteristics of temporal and space-time autocorrelation of pollen counts of Alnus , Betula , and Corylus in the air of eight cities in Poland. Daily average pollen concentrations were monitored over 8 years (2001-2005 and 2009-2011) using Hirst-designed volumetric spore traps. The spatial and temporal coherence of data was investigated using the autocorrelation and cross-correlation functions. The calculation and mathematical modelling of 61 correlograms were performed for up to 25 days back. The study revealed an association between temporal variations in Alnus , Betula , and Corylus pollen counts in Poland and three main groups of factors such as: (1) air mass exchange after the passage of a single weather front (30-40 % of pollen count variation); (2) long-lasting factors (50-60 %); and (3) random factors, including diurnal variations and measurements errors (10 %). These results can help to improve the quality of forecasting models.

Intrinsic autocorrelation time of picoseconds for thermal noise in water.

PubMed

Zhu, Zhi; Sheng, Nan; Wan, Rongzheng; Fang, Haiping

2014-10-02

Whether thermal noise is colored or white is of fundamental importance. In conventional theory, thermal noise is usually treated as white noise so that there are no directional transportations in the asymmetrical systems without external inputs, since only the colored fluctuations with appropriate autocorrelation time length can lead to directional transportations in the asymmetrical systems. Here, on the basis of molecular dynamics simulations, we show that the autocorrelation time length of thermal noise in water is ~10 ps at room temperature, which indicates that thermal noise is not white in the molecular scale while thermal noise can be reasonably assumed as white in macro- and meso-scale systems. The autocorrelation time length of thermal noise is intrinsic, since the value is almost unchanged for different temperature coupling methods. Interestingly, the autocorrelation time of thermal noise is correlated with the lifetime of hydrogen bonds, suggesting that the finite autocorrelation time length of thermal noise mainly comes from the finite lifetime of the interactions between neighboring water molecules.

Investigation of the marked and long-standing spatial inhomogeneity of the Hungarian suicide rate: a spatial regression approach.

PubMed

Balint, Lajos; Dome, Peter; Daroczi, Gergely; Gonda, Xenia; Rihmer, Zoltan

2014-02-01

In the last century Hungary had astonishingly high suicide rates characterized by marked regional within-country inequalities, a spatial pattern which has been quite stable over time. To explain the above phenomenon at the level of micro-regions (n=175) in the period between 2005 and 2011. Our dependent variable was the age and gender standardized mortality ratio (SMR) for suicide while explanatory variables were factors which are supposed to influence suicide risk, such as measures of religious and political integration, travel time accessibility of psychiatric services, alcohol consumption, unemployment and disability pensionery. When applying the ordinary least squared regression model, the residuals were found to be spatially autocorrelated, which indicates the violation of the assumption on the independence of error terms and - accordingly - the necessity of application of a spatial autoregressive (SAR) model to handle this problem. According to our calculations the SARlag model was a better way (versus the SARerr model) of addressing the problem of spatial autocorrelation, furthermore its substantive meaning is more convenient. SMR was significantly associated with the "political integration" variable in a negative and with "lack of religious integration" and "disability pensionery" variables in a positive manner. Associations were not significant for the remaining explanatory variables. Several important psychiatric variables were not available at the level of micro-regions. We conducted our analysis on aggregate data. Our results may draw attention to the relevance and abiding validity of the classic Durkheimian suicide risk factors - such as lack of social integration - apropos of the spatial pattern of Hungarian suicides. © 2013 Published by Elsevier B.V.

Pair and triplet approximation of a spatial lattice population model with multiscale dispersal using Markov chains for estimating spatial autocorrelation.

PubMed

Hiebeler, David E; Millett, Nicholas E

2011-06-21

We investigate a spatial lattice model of a population employing dispersal to nearest and second-nearest neighbors, as well as long-distance dispersal across the landscape. The model is studied via stochastic spatial simulations, ordinary pair approximation, and triplet approximation. The latter method, which uses the probabilities of state configurations of contiguous blocks of three sites as its state variables, is demonstrated to be greatly superior to pair approximations for estimating spatial correlation information at various scales. Correlations between pairs of sites separated by arbitrary distances are estimated by constructing spatial Markov processes using the information from both approximations. These correlations demonstrate why pair approximation misses basic qualitative features of the model, such as decreasing population density as a large proportion of offspring are dropped on second-nearest neighbors, and why triplet approximation is able to include them. Analytical and numerical results show that, excluding long-distance dispersal, the initial growth rate of an invading population is maximized and the equilibrium population density is also roughly maximized when the population spreads its offspring evenly over nearest and second-nearest neighboring sites. Copyright © 2011 Elsevier Ltd. All rights reserved.

General simulation algorithm for autocorrelated binary processes.

PubMed

Serinaldi, Francesco; Lombardo, Federico

2017-02-01

The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can effectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure effectively embedding a spectrum-based iterative amplitude-adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding, for instance, to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.

Method and apparatus for in-situ characterization of energy storage and energy conversion devices

DOEpatents

Christophersen, Jon P [Idaho Falls, ID; Motloch, Chester G [Idaho Falls, ID; Morrison, John L [Butte, MT; Albrecht, Weston [Layton, UT

2010-03-09

Disclosed are methods and apparatuses for determining an impedance of an energy-output device using a random noise stimulus applied to the energy-output device. A random noise signal is generated and converted to a random noise stimulus as a current source correlated to the random noise signal. A bias-reduced response of the energy-output device to the random noise stimulus is generated by comparing a voltage at the energy-output device terminal to an average voltage signal. The random noise stimulus and bias-reduced response may be periodically sampled to generate a time-varying current stimulus and a time-varying voltage response, which may be correlated to generate an autocorrelated stimulus, an autocorrelated response, and a cross-correlated response. Finally, the autocorrelated stimulus, the autocorrelated response, and the cross-correlated response may be combined to determine at least one of impedance amplitude, impedance phase, and complex impedance.

Data Analysis Methods for Synthetic Polymer Mass Spectrometry: Autocorrelation

PubMed Central

Wallace, William E.; Guttman, Charles M.

2002-01-01

Autocorrelation is shown to be useful in describing the periodic patterns found in high- resolution mass spectra of synthetic polymers. Examples of this usefulness are described for a simple linear homopolymer to demonstrate the method fundamentals, a condensation polymer to demonstrate its utility in understanding complex spectra with multiple repeating patterns on different mass scales, and a condensation copolymer to demonstrate how it can elegantly and efficiently reveal unexpected phenomena. It is shown that using autocorrelation to determine where the signal devolves into noise can be useful in determining molecular mass distributions of synthetic polymers, a primary focus of the NIST synthetic polymer mass spectrometry effort. The appendices describe some of the effects of transformation from time to mass space when time-of-flight mass separation is used, as well as the effects of non-trivial baselines on the autocorrelation function. PMID:27446716

Constant-Envelope Waveform Design for Optimal Target-Detection and Autocorrelation Performances

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

Sen, Satyabrata

2013-01-01

We propose an algorithm to directly synthesize in time-domain a constant-envelope transmit waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. This approach is in contrast to the traditional indirect methods that synthesize the transmit signal following the computation of the optimal energy spectral density. Additionally, we aim to maintain a good autocorrelation property of the designed signal. Therefore, our waveform design technique solves a bi-objective optimization problem in order to simultaneously improve the detection and autocorrelation performances, which are in general conflicting in nature. We demonstrate this compromising characteristics of themore » detection and autocorrelation performances with numerical examples. Furthermore, in the absence of the autocorrelation criterion, our designed signal is shown to achieve a near-optimum detection performance.« less

The Use of Time Series Analysis and t Tests with Serially Correlated Data Tests.

ERIC Educational Resources Information Center

Nicolich, Mark J.; Weinstein, Carol S.

1981-01-01

Results of three methods of analysis applied to simulated autocorrelated data sets with an intervention point (varying in autocorrelation degree, variance of error term, and magnitude of intervention effect) are compared and presented. The three methods are: t tests; maximum likelihood Box-Jenkins (ARIMA); and Bayesian Box Jenkins. (Author/AEF)

A mini-review on econophysics: Comparative study of Chinese and western financial markets

NASA Astrophysics Data System (ADS)

Zheng, Bo; Jiang, Xiong-Fei; Ni, Peng-Yun

2014-07-01

We present a review of our recent research in econophysics, and focus on the comparative study of Chinese and western financial markets. By virtue of concepts and methods in statistical physics, we investigate the time correlations and spatial structure of financial markets based on empirical high-frequency data. We discover that the Chinese stock market shares common basic properties with the western stock markets, such as the fat-tail probability distribution of price returns, the long-range auto-correlation of volatilities, and the persistence probability of volatilities, while it exhibits very different higher-order time correlations of price returns and volatilities, spatial correlations of individual stock prices, and large-fluctuation dynamic behaviors. Furthermore, multi-agent-based models are developed to simulate the microscopic interaction and dynamic evolution of the stock markets.

Reconfigurable wavefront sensor for ultrashort pulses.

PubMed

Bock, Martin; Das, Susanta Kumar; Fischer, Carsten; Diehl, Michael; Börner, Peter; Grunwald, Ruediger

2012-04-01

A highly flexible Shack-Hartmann wavefront sensor for ultrashort pulse diagnostics is presented. The temporal system performance is studied in detail. Reflective operation is enabled by programming tilt-tolerant microaxicons into a liquid-crystal-on-silicon spatial light modulator. Nearly undistorted pulse transfer is obtained by generating nondiffracting needle beams as subbeams. Reproducible wavefront analysis and spatially resolved second-order autocorrelation are demonstrated at incident angles up to 50° and pulse durations down to 6 fs.

An asymptotic theory for cross-correlation between auto-correlated sequences and its application on neuroimaging data.

PubMed

Zhou, Yunyi; Tao, Chenyang; Lu, Wenlian; Feng, Jianfeng

2018-04-20

Functional connectivity is among the most important tools to study brain. The correlation coefficient, between time series of different brain areas, is the most popular method to quantify functional connectivity. Correlation coefficient in practical use assumes the data to be temporally independent. However, the time series data of brain can manifest significant temporal auto-correlation. A widely applicable method is proposed for correcting temporal auto-correlation. We considered two types of time series models: (1) auto-regressive-moving-average model, (2) nonlinear dynamical system model with noisy fluctuations, and derived their respective asymptotic distributions of correlation coefficient. These two types of models are most commonly used in neuroscience studies. We show the respective asymptotic distributions share a unified expression. We have verified the validity of our method, and shown our method exhibited sufficient statistical power for detecting true correlation on numerical experiments. Employing our method on real dataset yields more robust functional network and higher classification accuracy than conventional methods. Our method robustly controls the type I error while maintaining sufficient statistical power for detecting true correlation in numerical experiments, where existing methods measuring association (linear and nonlinear) fail. In this work, we proposed a widely applicable approach for correcting the effect of temporal auto-correlation on functional connectivity. Empirical results favor the use of our method in functional network analysis. Copyright © 2018. Published by Elsevier B.V.

Characterizing tissue microstructure using an ultrasound system-independent spatial autocorrelation function

NASA Astrophysics Data System (ADS)

Dong, Fang

1999-09-01

The research described in this dissertation is related to characterization of tissue microstructure using a system- independent spatial autocorrelation function (SAF). The function was determined using a reference phantom method, which employed a well-defined ``point- scatterer'' reference phantom to account for instrumental factors. The SAF's were estimated for several tissue-mimicking (TM) phantoms and fresh dog livers. Both phantom tests and in vitro dog liver measurements showed that the reference phantom method is relatively simple and fairly accurate, providing the bandwidth of the measurement system is sufficient for the size of the scatterer being involved in the scattering process. Implementation of this method in clinical scanner requires that distortions from patient's body wall be properly accounted for. The SAF's were estimated for two phantoms with body-wall-like distortions. The experimental results demonstrated that body wall distortions have little effect if echo data are acquired from a large scattering volume. One interesting application of the SAF is to form a ``scatterer size image''. The scatterer size image may help providing diagnostic tools for those diseases in which the tissue microstructure is different from the normal. Another method, the BSC method, utilizes information contained in the frequency dependence of the backscatter coefficient to estimate the scatterer size. The SAF technique produced accurate scatterer size images of homogeneous TM phantoms and the BSC method was capable of generating accurate size images for heterogeneous phantoms. In the scatterer size image of dog kidneys, the contrast-to-noise-ratio (CNR) between renal cortex and medulla was improved dramatically compared to the gray- scale image. The effect of nonlinear propagation was investigated by using a custom-designed phantom with overlaying TM fat layer. The results showed that the correlation length decreased when the transmitting power increased. The measurement results support the assumption that nonlinear propagation generates harmonic energies and causes underestimation of scatterer diameters. Nonlinear propagation can be further enhanced by those materials with high B/A value-a parameter which characterizes the degree of nonlinearity. Nine versions of TM fat and non-fat materials were measured for their B/A values using a new measurement technique, the ``simplified finite amplitude insertion substitution'' (SFAIS) method.

Identification of the driving factors' influences on regional energy-related carbon emissions in China based on geographical detector method.

PubMed

Zhang, Xinlin; Zhao, Yuan

2018-04-01

To investigate the influences of different factors on spatial heterogeneity of regional carbon emissions, we firstly studied the spatial-temporal dynamics of regional energy-related carbon emissions using global Moran's I and Getis-Ord Gi and applied geographical detector model to explain the spatial heterogeneity of regional carbon emissions. Some conclusions were drawn. Regional carbon emissions showed significant global and local spatial autocorrelation. The carbon emissions were greater in eastern and northern regions than in western and southern regions. Fixed assets investment and economic output had been the main contributing factors over the study period, and economic output had been decreasing its influence. Industrial structure's influence showed a decrease trend and became smaller in 2015. The results of the interaction detections in 2015 can be divided into two types: enhance and nonlinear, and enhance and bivariate. The interactive influences between technological level and fixed assets investment, economic output and technological level, population size and technological level, and economic output and economic development were greater than others. Some policy recommendations were proposed.

Mapping the Spread of Methamphetamine Abuse in California From 1995 to 2008

PubMed Central

Ponicki, William R.; Remer, Lillian G.; Waller, Lance A.; Zhu, Li; Gorman, Dennis M.

2013-01-01

Objectives. From 1983 to 2008, the incidence of methamphetamine abuse and dependence (MA) presenting at hospitals in California increased 13-fold. We assessed whether this growth could be characterized as a drug epidemic. Methods. We geocoded MA discharges to residential zip codes from 1995 through 2008. We related discharges to population and environmental characteristics using Bayesian Poisson conditional autoregressive models, correcting for small area effects and spatial misalignment and enabling an assessment of contagion between areas. Results. MA incidence increased exponentially in 3 phases interrupted by implementation of laws limiting access to methamphetamine precursors. MA growth from 1999 through 2008 was 17% per year. MA was greatest in areas with larger White or Hispanic low-income populations, small household sizes, and good connections to highway systems. Spatial misalignment was a source of bias in estimated effects. Spatial autocorrelation was substantial, accounting for approximately 80% of error variance in the model. Conclusions. From 1995 through 2008, MA exhibited signs of growth and spatial spread characteristic of drug epidemics, spreading most rapidly through low-income White and Hispanic populations living outside dense urban areas. PMID:23078474

Neighborhood Characteristics and the Social Control of Registered Sex Offenders

ERIC Educational Resources Information Center

Socia, Kelly M.; Stamatel, Janet P.

2012-01-01

This study uses geospatial and regression analyses to examine the relationships among social disorganization, collective efficacy, social control, residence restrictions, spatial autocorrelation, and the neighborhood distribution of registered sex offenders (RSOs) in Chicago. RSOs were concentrated in neighborhoods that had higher levels of social…

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Spectra of empirical autocorrelation matrices: A random-matrix-theory-inspired perspective

NASA Astrophysics Data System (ADS)

Jamali, Tayeb; Jafari, G. R.

2015-07-01

We construct an autocorrelation matrix of a time series and analyze it based on the random-matrix theory (RMT) approach. The autocorrelation matrix is capable of extracting information which is not easily accessible by the direct analysis of the autocorrelation function. In order to provide a precise conclusion based on the information extracted from the autocorrelation matrix, the results must be first evaluated. In other words they need to be compared with some sort of criterion to provide a basis for the most suitable and applicable conclusions. In the context of the present study, the criterion is selected to be the well-known fractional Gaussian noise (fGn). We illustrate the applicability of our method in the context of stock markets. For the former, despite the non-Gaussianity in returns of the stock markets, a remarkable agreement with the fGn is achieved.

Spatial distribution of cutaneous leishmaniasis in the state of Paraná, Brazil.

PubMed

Melo, Helen Aline; Rossoni, Diogo Francisco; Teodoro, Ueslei

2017-01-01

The geographic distribution of cutaneous leishmaniasis (CL) makes it a disease of major clinical importance in Brazil, where it is endemic in the state of Paraná. The objective of this study was to analyze the spatial distribution of CL in Paraná between 2001 and 2015, based on data from the Sistema de Informação de Agravos de Notificação (Information System for Notifiable Diseases) regarding autochthonous CL cases. Spatial autocorrelation was performed using Moran's Global Index and the Local Indicator of Spatial Association (LISA). The construction of maps was based on categories of association (high-high, low-low, high-low, and low-high). A total of 4,557 autochthonous cases of CL were registered in the state of Paraná, with an annual average of 303.8 (± 135.2) and a detection coefficient of 2.91. No correlation was found between global indices and their respective significance in 2001 (I = -0.456, p = 0.676), but evidence of spatial autocorrelation was found in other years (p< 0.05). In the construction and analysis of the cluster maps, areas with a high-high positive association were found in the Ivaí-Pirapó, Tibagi, Cinzas-Laranjinha, and Ribeira areas. The state of Paraná should keep a constant surveillance over CL due to the prominent presence of socioeconomic and environmental factors such as the favorable circumstances for the vectors present in peri-urban and agriculture áreas.

Spatial distribution of cutaneous leishmaniasis in the state of Paraná, Brazil

PubMed Central

2017-01-01

The geographic distribution of cutaneous leishmaniasis (CL) makes it a disease of major clinical importance in Brazil, where it is endemic in the state of Paraná. The objective of this study was to analyze the spatial distribution of CL in Paraná between 2001 and 2015, based on data from the Sistema de Informação de Agravos de Notificação (Information System for Notifiable Diseases) regarding autochthonous CL cases. Spatial autocorrelation was performed using Moran’s Global Index and the Local Indicator of Spatial Association (LISA). The construction of maps was based on categories of association (high-high, low-low, high-low, and low-high). A total of 4,557 autochthonous cases of CL were registered in the state of Paraná, with an annual average of 303.8 (± 135.2) and a detection coefficient of 2.91. No correlation was found between global indices and their respective significance in 2001 (I = -0.456, p = 0.676), but evidence of spatial autocorrelation was found in other years (p< 0.05). In the construction and analysis of the cluster maps, areas with a high-high positive association were found in the Ivaí-Pirapó, Tibagi, Cinzas-Laranjinha, and Ribeira areas. The state of Paraná should keep a constant surveillance over CL due to the prominent presence of socioeconomic and environmental factors such as the favorable circumstances for the vectors present in peri-urban and agriculture áreas. PMID:28938013

Off-axis electron holography combining summation of hologram series with double-exposure phase-shifting: Theory and application.

PubMed

Boureau, Victor; McLeod, Robert; Mayall, Benjamin; Cooper, David

2018-06-04

In this paper we discuss developments for Lorentz mode or "medium resolution" off-axis electron holography such that it is now routinely possible obtain very high sensitivity phase maps with high spatial resolution whilst maintaining a large field of view. Modifications of the usual Fourier reconstruction procedure have been used to combine series of holograms for sensitivity improvement with a phase-shifting method for doubling the spatial resolution. In the frame of these developments, specific attention is given to the phase standard deviation description and its interaction with the spatial resolution as well as the processing of reference holograms. An experimental study based on Dark-Field Electron Holography (DFEH), using a SiGe/Si multilayer epitaxy sample is compared with theory. The method's efficiency of removing the autocorrelation term during hologram reconstruction is discussed. Software has been written in DigitalMicrograph that can be used to routinely perform these tasks. To illustrate the real improvements made using these methods we show that a strain measurement sensitivity of  ±  0.025 % can be achieved with a spatial resolution of 2 nm and  ±  0.13 % with a spatial resolution of 1 nm whilst maintaining a useful field of view of 300 nm. In the frame of these measurements a model of strain noise for DFEH has also been developed. Copyright © 2018. Published by Elsevier B.V.

River eutrophication: irrigated vs. non-irrigated agriculture through different spatial scales.

PubMed

Monteagudo, Laura; Moreno, José Luis; Picazo, Félix

2012-05-15

The main objective of this study was to determine how spatial scale may affect the results when relating land use to nutrient enrichment of rivers and, secondly, to investigate which agricultural practices are more responsible for river eutrophication in the study area. Agriculture was split into three subclasses (irrigated, non-irrigated and low-impact agriculture) which were correlated to stream nutrient concentration on four spatial scales: large scale (drainage area of total subcatchment and 100 m wide subcatchment corridors) and local scale (5 and 1 km radius buffers). Nitrate, ammonium and orthophosphate concentrations and land use composition (agriculture, urban and forest) were measured at 130 river reaches in south-central Spain during the 2001-2009 period. Results suggested that different spatial scales may lead to different conclusions. Spatial autocorrelation and the inadequate representation of some land uses produced unreal results on large scales. Conversely, local scales did not show data autocorrelation and agriculture subclasses were well represented. The local scale of 1 km buffer was the most appropriate to detect river eutrophication in central Spanish rivers, with irrigated cropland as the main cause of river pollution by nitrate. As regards river management, a threshold of 50% irrigated cropland within a 1 km radius buffer has been obtained using breakpoint regression analysis. This means that no more than 50% of irrigation croplands should be allowed near river banks in order to avoid river eutrophication. Finally, a methodological approach is proposed to choose the appropriate spatial scale when studying river eutrophication caused by diffuse pollution like agriculture. Copyright © 2012 Elsevier Ltd. All rights reserved.

Detection of forest stand-level spatial structure in ectomycorrhizal fungal communities.

PubMed

Lilleskov, Erik A; Bruns, Thomas D; Horton, Thomas R; Taylor, D; Grogan, Paul

2004-08-01

Ectomycorrhizal fungal (EMF) communities are highly diverse at the stand level. To begin to understand what might lead to such diversity, and to improve sampling designs, we investigated the spatial structure of these communities. We used EMF community data from a number of studies carried out in seven mature and one recently fire-initiated forest stand. We applied various measures of spatial pattern to characterize distributions at EMF community and species levels: Mantel tests, Mantel correlograms, variance/mean and standardized variograms. Mantel tests indicated that in four of eight sites community similarity decreased with distance, whereas Mantel correlograms also found spatial autocorrelation in those four plus two additional sites. In all but one of these sites elevated similarity was evident only at relatively small spatial scales (< 2.6 m), whereas one exhibited a larger scale pattern ( approximately 25 m). Evenness of biomass distribution among cores varied widely among taxa. Standardized variograms indicated that most of the dominant taxa showed patchiness at a scale of less than 3 m, with a range from 0 to < or =17 m. These results have implications for both sampling scale and intensity to achieve maximum efficiency of community sampling. In the systems we examined, cores should be at least 3 m apart to achieve the greatest sampling efficiency for stand-level community analysis. In some cases even this spacing may result in reduced sampling efficiency arising from patterns of spatial autocorrelation. Interpretation of the causes and significance of these patterns requires information on the genetic identity of individuals in the communities.

Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China

PubMed Central

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-01-01

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables. PMID:25317762

Hybrid optimal design of the eco-hydrological wireless sensor network in the middle reach of the Heihe River Basin, China.

PubMed

Kang, Jian; Li, Xin; Jin, Rui; Ge, Yong; Wang, Jinfeng; Wang, Jianghao

2014-10-14

The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2013-08-01

Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

Noise-tolerant instantaneous heart rate and R-peak detection using short-term autocorrelation for wearable healthcare systems.

PubMed

Fujii, Takahide; Nakano, Masanao; Yamashita, Ken; Konishi, Toshihiro; Izumi, Shintaro; Kawaguchi, Hiroshi; Yoshimoto, Masahiko

2013-01-01

This paper describes a robust method of Instantaneous Heart Rate (IHR) and R-peak detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the R-wave interval. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable bio-signal monitoring systems, noise increases the incidence of misdetection and false detection of R-peaks. To prevent incorrect detection, we introduce a short-term autocorrelation (STAC) technique and a small-window autocorrelation (SWAC) technique, which leverages the similarity of QRS complex waveforms. Simulation results show that the proposed method improves the noise tolerance of R-peak detection.

A general statistical test for correlations in a finite-length time series.

PubMed

Hanson, Jeffery A; Yang, Haw

2008-06-07

The statistical properties of the autocorrelation function from a time series composed of independently and identically distributed stochastic variables has been studied. Analytical expressions for the autocorrelation function's variance have been derived. It has been found that two common ways of calculating the autocorrelation, moving-average and Fourier transform, exhibit different uncertainty characteristics. For periodic time series, the Fourier transform method is preferred because it gives smaller uncertainties that are uniform through all time lags. Based on these analytical results, a statistically robust method has been proposed to test the existence of correlations in a time series. The statistical test is verified by computer simulations and an application to single-molecule fluorescence spectroscopy is discussed.

Spatio-temporal wildland arson crime functions

Treesearch

David T. Butry; Jeffrey P. Prestemon

2005-01-01

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p)...

Update and review of accuracy assessment techniques for remotely sensed data

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Heinen, J. T.; Oderwald, R. G.

1983-01-01

Research performed in the accuracy assessment of remotely sensed data is updated and reviewed. The use of discrete multivariate analysis techniques for the assessment of error matrices, the use of computer simulation for assessing various sampling strategies, and an investigation of spatial autocorrelation techniques are examined.

The potential of 2D Kalman filtering for soil moisture data assimilation

USDA-ARS?s Scientific Manuscript database

We examine the potential for parameterizing a two-dimensional (2D) land data assimilation system using spatial error auto-correlation statistics gleaned from a triple collocation analysis and the triplet of: (1) active microwave-, (2) passive microwave- and (3) land surface model-based surface soil ...

A model relating Eulerian spatial and temporal velocity correlations

NASA Astrophysics Data System (ADS)

Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.

Applying spatial regression to evaluate risk factors for microbiological contamination of urban groundwater sources in Juba, South Sudan

NASA Astrophysics Data System (ADS)

Engström, Emma; Mörtberg, Ulla; Karlström, Anders; Mangold, Mikael

2017-06-01

This study developed methodology for statistically assessing groundwater contamination mechanisms. It focused on microbial water pollution in low-income regions. Risk factors for faecal contamination of groundwater-fed drinking-water sources were evaluated in a case study in Juba, South Sudan. The study was based on counts of thermotolerant coliforms in water samples from 129 sources, collected by the humanitarian aid organisation Médecins Sans Frontières in 2010. The factors included hydrogeological settings, land use and socio-economic characteristics. The results showed that the residuals of a conventional probit regression model had a significant positive spatial autocorrelation (Moran's I = 3.05, I-stat = 9.28); therefore, a spatial model was developed that had better goodness-of-fit to the observations. The most significant factor in this model ( p-value 0.005) was the distance from a water source to the nearest Tukul area, an area with informal settlements that lack sanitation services. It is thus recommended that future remediation and monitoring efforts in the city be concentrated in such low-income regions. The spatial model differed from the conventional approach: in contrast with the latter case, lowland topography was not significant at the 5% level, as the p-value was 0.074 in the spatial model and 0.040 in the traditional model. This study showed that statistical risk-factor assessments of groundwater contamination need to consider spatial interactions when the water sources are located close to each other. Future studies might further investigate the cut-off distance that reflects spatial autocorrelation. Particularly, these results advise research on urban groundwater quality.

Increasing Accuracy of Tissue Shear Modulus Reconstruction Using Ultrasonic Strain Tensor Measurement

NASA Astrophysics Data System (ADS)

Sumi, C.

Previously, we developed three displacement vector measurement methods, i.e., the multidimensional cross-spectrum phase gradient method (MCSPGM), the multidimensional autocorrelation method (MAM), and the multidimensional Doppler method (MDM). To increase the accuracies and stabilities of lateral and elevational displacement measurements, we also developed spatially variant, displacement component-dependent regularization. In particular, the regularization of only the lateral/elevational displacements is advantageous for the lateral unmodulated case. The demonstrated measurements of the displacement vector distributions in experiments using an inhomogeneous shear modulus agar phantom confirm that displacement-component-dependent regularization enables more stable shear modulus reconstruction. In this report, we also review our developed lateral modulation methods that use Parabolic functions, Hanning windows, and Gaussian functions in the apodization function and the optimized apodization function that realizes the designed point spread function (PSF). The modulations significantly increase the accuracy of the strain tensor measurement and shear modulus reconstruction (demonstrated using an agar phantom).

The Azimuth Structure of Nuclear Collisions — I

NASA Astrophysics Data System (ADS)

Trainor, Thomas A.; Kettler, David T.

We describe azimuth structure commonly associated with elliptic and directed flow in the context of 2D angular autocorrelations for the purpose of precise separation of so-called nonflow (mainly minijets) from flow. We extend the Fourier-transform description of azimuth structure to include power spectra and autocorrelations related by the Wiener-Khintchine theorem. We analyze several examples of conventional flow analysis in that context and question the relevance of reaction plane estimation to flow analysis. We introduce the 2D angular autocorrelation with examples from data analysis and describe a simulation exercise which demonstrates precise separation of flow and nonflow using the 2D autocorrelation method. We show that an alternative correlation measure based on Pearson's normalized covariance provides a more intuitive measure of azimuth structure.

Cobble cam: Grain-size measurements of sand to boulder from digital photographs and autocorrelation analyses

USGS Publications Warehouse

Warrick, J.A.; Rubin, D.M.; Ruggiero, P.; Harney, J.N.; Draut, A.E.; Buscombe, D.

2009-01-01

A new application of the autocorrelation grain size analysis technique for mixed to coarse sediment settings has been investigated. Photographs of sand- to boulder-sized sediment along the Elwha River delta beach were taken from approximately 1??2 m above the ground surface, and detailed grain size measurements were made from 32 of these sites for calibration and validation. Digital photographs were found to provide accurate estimates of the long and intermediate axes of the surface sediment (r2 > 0??98), but poor estimates of the short axes (r2 = 0??68), suggesting that these short axes were naturally oriented in the vertical dimension. The autocorrelation method was successfully applied resulting in total irreducible error of 14% over a range of mean grain sizes of 1 to 200 mm. Compared with reported edge and object-detection results, it is noted that the autocorrelation method presented here has lower error and can be applied to a much broader range of mean grain sizes without altering the physical set-up of the camera (~200-fold versus ~6-fold). The approach is considerably less sensitive to lighting conditions than object-detection methods, although autocorrelation estimates do improve when measures are taken to shade sediments from direct sunlight. The effects of wet and dry conditions are also evaluated and discussed. The technique provides an estimate of grain size sorting from the easily calculated autocorrelation standard error, which is correlated with the graphical standard deviation at an r2 of 0??69. The technique is transferable to other sites when calibrated with linear corrections based on photo-based measurements, as shown by excellent grain-size analysis results (r2 = 0??97, irreducible error = 16%) from samples from the mixed grain size beaches of Kachemak Bay, Alaska. Thus, a method has been developed to measure mean grain size and sorting properties of coarse sediments. ?? 2009 John Wiley & Sons, Ltd.

Analysing magnetism using scanning SQUID microscopy.

PubMed

Reith, P; Renshaw Wang, X; Hilgenkamp, H

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

Analysing magnetism using scanning SQUID microscopy

NASA Astrophysics Data System (ADS)

Reith, P.; Renshaw Wang, X.; Hilgenkamp, H.

2017-12-01

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last few decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength, and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape, and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.

Localization of atomic excitation beyond the diffraction limit using electromagnetically induced transparency

NASA Astrophysics Data System (ADS)

Miles, J. A.; Das, Diptaranjan; Simmons, Z. J.; Yavuz, D. D.

2015-09-01

We experimentally demonstrate the localization of excitation between hyperfine ground states of 87Rb atoms to as small as λ /13 -wide spatial regions. We use ultracold atoms trapped in a dipole trap and utilize electromagnetically induced transparency (EIT) for the atomic excitation. The localization is achieved by combining a spatially varying coupling laser (standing wave) with the intensity dependence of EIT. The excitation is fast (150 ns laser pulses) and the dark-state fidelity can be made higher than 94% throughout the standing wave. Because the width of the localized regions is much smaller than the wavelength of the driving light, traditional optical imaging techniques cannot resolve the localized features. Therefore, to measure the excitation profile, we use an autocorrelation-like method where we perform two EIT sequences separated by a time delay, during which we move the standing wave.

Geostatistical borehole image-based mapping of karst-carbonate aquifer pores

USGS Publications Warehouse

Michael Sukop,; Cunningham, Kevin J.

2016-01-01

Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes.

Environmental Assessment and Monitoring with ICAMS (Image Characterization and Modeling System) Using Multiscale Remote-Sensing Data

NASA Technical Reports Server (NTRS)

Lam, N.; Qiu, H.-I.; Quattrochi, Dale A.; Zhao, Wei

1997-01-01

With the rapid increase in spatial data, especially in the NASA-EOS (Earth Observing System) era, it is necessary to develop efficient and innovative tools to handle and analyze these data so that environmental conditions can be assessed and monitored. A main difficulty facing geographers and environmental scientists in environmental assessment and measurement is that spatial analytical tools are not easily accessible. We have recently developed a remote sensing/GIS software module called Image Characterization and Modeling System (ICAMS) to provide specialized spatial analytical tools for the measurement and characterization of satellite and other forms of spatial data. ICAMS runs on both the Intergraph-MGE and Arc/info UNIX and Windows-NT platforms. The main techniques in ICAMS include fractal measurement methods, variogram analysis, spatial autocorrelation statistics, textural measures, aggregation techniques, normalized difference vegetation index (NDVI), and delineation of land/water and vegetated/non-vegetated boundaries. In this paper, we demonstrate the main applications of ICAMS on the Intergraph-MGE platform using Landsat Thematic Mapper images from the city of Lake Charles, Louisiana. While the utilities of ICAMS' spatial measurement methods (e.g., fractal indices) in assessing environmental conditions remain to be researched, making the software available to a wider scientific community can permit the techniques in ICAMS to be evaluated and used for a diversity of applications. The findings from these various studies should lead to improved algorithms and more reliable models for environmental assessment and monitoring.

SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

PubMed Central

Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo

2009-01-01

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450

Analysis of Spatial Autocorrelation for Optimal Observation Network in Korea

NASA Astrophysics Data System (ADS)

Park, S.; Lee, S.; Lee, E.; Park, S. K.

2016-12-01

Many studies for improving prediction of high-impact weather have been implemented, such as THORPEX (The Observing System Research and Predictability Experiment), FASTEX (Fronts and Atlantic Storm-Track Experiment), NORPEX (North Pacific Experiment), WSR/NOAA (Winter Storm Reconnaissance), and DOTSTAR (Dropwindsonde Observations for Typhoon Surveillance near the TAiwan Region). One of most important objectives in these studies is to find effects of observation on forecast, and to establish optimal observation network. However, there are lack of such studies on Korea, although Korean peninsula exhibits a highly complex terrain so it is difficult to predict its weather phenomena. Through building the future optimal observation network, it is necessary to increase utilization of numerical weather prediction and improve monitoring·tracking·prediction skills of high-impact weather in Korea. Therefore, we will perform preliminary study to understand the spatial scale for an expansion of observation system through Spatial Autocorrelation (SAC) analysis. In additions, we will develop a testbed system to design an optimal observation network. Analysis is conducted with Automatic Weather System (AWS) rainfall data, global upper air grid observation (i.e., temperature, pressure, humidity), Himawari satellite data (i.e., water vapor) during 2013-2015 of Korea. This study will provide a guideline to construct observation network for not only improving weather prediction skill but also cost-effectiveness.

The acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010.

PubMed

Alves, André T J; Nobre, Flávio F

2014-05-01

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality. Sequential choropleth maps were developed and used to analyse the incidence distribution and Moran's I spatial autocorrelation statistics was applied for characterisation of the spatio-temporal distribution pattern. A significant, positive spatial autocorrelation of AIDS incidence was observed indicating that municipalities with high incidence are likely to be close to other municipalities with similarly high incidence and, conversely, municipalities with low incidence are likely to be surrounded by municipalities with low incidence. Two clusters were identified; one hotspot related to the State Capital and the other with low to intermediate AIDS incidence comprising municipalities in the north-eastern region of the State of Rio de Janeiro.

Generalizability Assessment of Autocorrelated Direct Observation Data: The Applicability of the Tiao-Tan Method and Alternative.

ERIC Educational Resources Information Center

Suen, Hoi K.; And Others

The applicability is explored of the Bayesian random-effect analysis of variance (ANOVA) model developed by G. C. Tiao and W. Y. Tan (1966) and a method suggested by H. K. Suen and P. S. Lee (1987) for the generalizability analysis of autocorrelated data. According to Tiao and Tan, if time series data could be described as a first-order…

Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935 1999) as demonstrated by autoregressive modelling

NASA Astrophysics Data System (ADS)

Jones, A. L.; Smart, P. L.

2005-08-01

Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.

Submillimeter-scale heterogeneity of labile phosphorus in sediments characterized by diffusive gradients in thin films and spatial analysis.

PubMed

Meng, Yuting; Ding, Shiming; Gong, Mengdan; Chen, Musong; Wang, Yan; Fan, Xianfang; Shi, Lei; Zhang, Chaosheng

2018-03-01

Sediments have a heterogeneous distribution of labile redox-sensitive elements due to a drastic downward transition from oxic to anoxic condition as a result of organic matter degradation. Characterization of the heterogeneous nature of sediments is vital for understanding of small-scale biogeochemical processes. However, there are limited reports on the related specialized methodology. In this study, the monthly distributions of labile phosphorus (P), a redox-sensitive limiting nutrient, were measured in the eutrophic Lake Taihu by Zr-oxide diffusive gradients in thin films (Zr-oxide DGT) on a two-dimensional (2D) submillimeter level. Geographical information system (GIS) techniques were used to visualize the labile P distribution at such a micro-scale, showing that the DGT-labile P was low in winter and high in summer. Spatial analysis methods, including semivariogram and Moran's I, were used to quantify the spatial variation of DGT-labile P. The distribution of DGT-labile P had clear submillimeter-scale spatial patterns with significant spatial autocorrelation during the whole year and displayed seasonal changes. High values of labile P with strong spatial variation were observed in summer, while low values of labile P with relatively uniform spatial patterns were detected in winter, demonstrating the strong influences of temperature on the mobility and spatial distribution of P in sediment profiles. Copyright © 2017 Elsevier Ltd. All rights reserved.

[Spatial analysis of mortality from cardiovascular diseases in Madrid City, Spain].

PubMed

Gómez-Barroso, Diana; Prieto-Flores, María-Eugenia; Mellado San Gabino, Ana; Moreno Jiménez, Antonio

2015-01-01

Cardiovascular disease is the leading cause of death worldwide, but its spatial distribution is not homogeneous. The objective of this study is to analyze the spatial pattern of mortality from these diseases for men and women, in the populated urban area (AUP) of the municipality of Madrid, and to identify spatial aggregations. An ecological study was carried out by census tract, for men and women in 2010. Standardized Mortality Ratio (SMR), Relative Risk Smoothing (RRS) and Posterior Probability (PP) were calculated to consider the spatial pattern of the disease. To identify spatial clusters the Moran index (Moran I) and the Local Index of Spatial Autocorrelation (LISA) were used. The results were mapped. SMR higher than 1.1 was observed mainly in central areas among men and in peripheral areas among women. The PP that RRS was higher than 1 surpassed 0.8 in the center and in the periphery, in both men and women. Moran's I was 0.04 for men and 0.03 for women (p <0.05 in both cases). Sex differences were observed in the spatial distribution of mortality cases. RME RRS and PP maps showed a heterogeneous pattern in men, whereas in women a clearer pattern was detected, with a relatively higher risk in peripheral areas of the AUP. The LISA method showed similar patterns to those previously observed.

Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

NASA Technical Reports Server (NTRS)

Craig, R. G. (Principal Investigator)

1983-01-01

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Forest Stand Canopy Structure Attribute Estimation from High Resolution Digital Airborne Imagery

Treesearch

Demetrios Gatziolis

2006-01-01

A study of forest stand canopy variable assessment using digital, airborne, multispectral imagery is presented. Variable estimation involves stem density, canopy closure, and mean crown diameter, and it is based on quantification of spatial autocorrelation among pixel digital numbers (DN) using variogram analysis and an alternative, non-parametric approach known as...

GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update.

PubMed

Peakall, Rod; Smouse, Peter E

2012-10-01

GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G'(ST), G''(ST), Jost's D(est) and F'(ST) through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. rod.peakall@anu.edu.au.

Where do the Field Plots Belong? A Multiple-Constraint Sampling Design for the BigFoot Project

NASA Astrophysics Data System (ADS)

Kennedy, R. E.; Cohen, W. B.; Kirschbaum, A. A.; Gower, S. T.

2002-12-01

A key component of a MODIS validation project is effective characterization of biophysical measures on the ground. Fine-grain ecological field measurements must be placed strategically to capture variability at the scale of the MODIS imagery. Here we describe the BigFoot project's revised sampling scheme, designed to simultaneously meet three important goals: capture landscape variability, avoid spatial autocorrelation between field plots, and minimize time and expense of field sampling. A stochastic process places plots in clumped constellations to reduce field sampling costs, while minimizing spatial autocorrelation. This stochastic process is repeated, creating several hundred realizations of plot constellations. Each constellation is scored and ranked according to its ability to match landscape variability in several Landsat-based spectral indices, and its ability to minimize field sampling costs. We show how this approach has recently been used to place sample plots at the BigFoot project's two newest study areas, one in a desert system and one in a tundra system. We also contrast this sampling approach to that already used at the four prior BigFoot project sites.

[Soil and forest structure in the Colombian Amazon].

PubMed

Calle-Rendón, Bayron R; Moreno, Flavio; Cárdenas López, Dairon

2011-09-01

Forests structural differences could result of environmental variations at different scales. Because soils are an important component of plant's environment, it is possible that edaphic and structural variables are associated and that, in consequence, spatial autocorrelation occurs. This paper aims to answer two questions: (1) are structural and edaphic variables associated at local scale in a terra firme forest of Colombian Amazonia? and (2) are these variables regionalized at the scale of work? To answer these questions we analyzed the data of a 6ha plot established in a terra firme forest of the Amacayacu National Park. Structural variables included basal area and density of large trees (diameter > or = 10cm) (Gdos and Ndos), basal area and density of understory individuals (diameter < 10cm) (Gsot and Nsot) and number of species of large trees (sp). Edaphic variables included were pH, organic matter, P, Mg, Ca, K, Al, sand, silt and clay. Structural and edaphic variables were reduced through a principal component analysis (PCA); then, the association between edaphic and structural components from PCA was evaluated by multiple regressions. The existence of regionalization of these variables was studied through isotropic variograms, and autocorrelated variables were spatially mapped. PCA found two significant components for structure, corresponding to the structure of large trees (G, Gdos, Ndos and sp) and of small trees (N, Nsot and Gsot), which explained 43.9% and 36.2% of total variance, respectively. Four components were identified for edaphic variables, which globally explained 81.9% of total variance and basically represent drainage and soil fertility. Regression analyses were significant (p < 0.05) and showed that the structure of both large and small trees is associated with greater sand contents and low soil fertility, though they explained a low proportion of total variability (R2 was 4.9% and 16.5% for the structure of large trees and small tress, respectively). Variables with spatial autocorrelation were the structure of small trees, Al, silt, and sand. Among them, Nsot and sand content showed similar patterns of spatial distribution inside the plot.

Contrasting patterns of fine-scale herb layer species composition in temperate forests

NASA Astrophysics Data System (ADS)

Chudomelová, Markéta; Zelený, David; Li, Ching-Feng

2017-04-01

Although being well described at the landscape level, patterns in species composition of forest herb layer are rarely studied at smaller scales. Here, we examined fine-scale environmental determinants and spatial structures of herb layer communities in thermophilous oak- and hornbeam dominated forests of the south-eastern part of the Czech Republic. Species composition of herb layer vegetation and environmental variables were recorded within a fixed grid of 2 × 2 m subplots regularly distributed within 1-ha quadrate plots in three forest stands. For each site, environmental models best explaining species composition were constructed using constrained ordination analysis. Spatial eigenvector mapping was used to model and account for spatial structures in community variation. Mean Ellenberg indicator values calculated for each subplot were used for ecological interpretation of spatially structured residual variation. The amount of variation explained by environmental and spatial models as well as the selection of variables with the best explanatory power differed among sites. As an important environmental factor, relative elevation was common to all three sites, while pH and canopy openness were shared by two sites. Both environmental and community variation was mostly coarse-scaled, as was the spatially structured portion of residual variation. When corrected for bias due to spatial autocorrelation, those environmental factors with already weak explanatory power lost their significance. Only a weak evidence of possibly omitted environmental predictor was found for autocorrelated residuals of site models using mean Ellenberg indicator values. Community structure was determined by different factors at different sites. The relative importance of environmental filtering vs. spatial processes was also site specific, implying that results of fine-scale studies tend to be shaped by local conditions. Contrary to expectations based on other studies, overall dominance of spatial processes at fine scale has not been detected. Ecologists should keep this in mind when making generalizations about community dynamics.

A new radial strain and strain rate estimation method using autocorrelation for carotid artery

NASA Astrophysics Data System (ADS)

Ye, Jihui; Kim, Hoonmin; Park, Jongho; Yeo, Sunmi; Shim, Hwan; Lim, Hyungjoon; Yoo, Yangmo

2014-03-01

Atherosclerosis is a leading cause of cardiovascular disease. The early diagnosis of atherosclerosis is of clinical interest since it can prevent any adverse effects of atherosclerotic vascular diseases. In this paper, a new carotid artery radial strain estimation method based on autocorrelation is presented. In the proposed method, the strain is first estimated by the autocorrelation of two complex signals from the consecutive frames. Then, the angular phase from autocorrelation is converted to strain and strain rate and they are analyzed over time. In addition, a 2D strain image over region of interest in a carotid artery can be displayed. To evaluate the feasibility of the proposed radial strain estimation method, radiofrequency (RF) data of 408 frames in the carotid artery of a volunteer were acquired by a commercial ultrasound system equipped with a research package (V10, Samsung Medison, Korea) by using a L5-13IS linear array transducer. From in vivo carotid artery data, the mean strain estimate was -0.1372 while its minimum and maximum values were -2.961 and 0.909, respectively. Moreover, the overall strain estimates are highly correlated with the reconstructed M-mode trace. Similar results were obtained from the estimation of the strain rate change over time. These results indicate that the proposed carotid artery radial strain estimation method is useful for assessing the arterial wall's stiffness noninvasively without increasing the computational complexity.

Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping.

PubMed

Goungounga, Juste Aristide; Gaudart, Jean; Colonna, Marc; Giorgi, Roch

2016-10-12

The reliability of spatial statistics is often put into question because real spatial variations may not be found, especially in heterogeneous areas. Our objective was to compare empirically different cluster detection methods. We assessed their ability to find spatial clusters of cancer cases and evaluated the impact of the socioeconomic status (e.g., the Townsend index) on cancer incidence. Moran's I, the empirical Bayes index (EBI), and Potthoff-Whittinghill test were used to investigate the general clustering. The local cluster detection methods were: i) the spatial oblique decision tree (SpODT); ii) the spatial scan statistic of Kulldorff (SaTScan); and, iii) the hierarchical Bayesian spatial modeling (HBSM) in a univariate and multivariate setting. These methods were used with and without introducing the Townsend index of socioeconomic deprivation known to be related to the distribution of cancer incidence. Incidence data stemmed from the Cancer Registry of Isère and were limited to prostate, lung, colon-rectum, and bladder cancers diagnosed between 1999 and 2007 in men only. The study found a spatial heterogeneity (p < 0.01) and an autocorrelation for prostate (EBI = 0.02; p = 0.001), lung (EBI = 0.01; p = 0.019) and bladder (EBI = 0.007; p = 0.05) cancers. After introduction of the Townsend index, SaTScan failed in finding cancers clusters. This introduction changed the results obtained with the other methods. SpODT identified five spatial classes (p < 0.05): four in the Western and one in the Northern parts of the study area (standardized incidence ratios: 1.68, 1.39, 1.14, 1.12, and 1.16, respectively). In the univariate setting, the Bayesian smoothing method found the same clusters as the two other methods (RR >1.2). The multivariate HBSM found a spatial correlation between lung and bladder cancers (r = 0.6). In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.

Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study.

PubMed

Insaf, Tabassum Z; Talbot, Thomas

2016-07-01

To assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions. LBW was defined as birth weight less than 2500g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model. Observed low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I-0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors. Neighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

The pink shrimp Farfantepenaeus duorarum, its symbionts and helminths as bioindicators of chemical pollution in Campeche Sound, Mexico.

PubMed

Vidal-Martínez, V M; Aguirre-Macedo, M L; Del Rio-Rodríguez, R; Gold-Bouchot, G; Rendón-von Osten, J; Miranda-Rosas, G A

2006-06-01

The pink shrimp Farfantepenaeus duorarum may acquire pollutants, helminths and symbionts from their environment. Statistical associations were studied between the symbionts and helminths of F. duorarum and pollutants in sediments, water and shrimps in Campeche Sound, Mexico. The study area spatially overlapped between offshore oil platforms and natural shrimp mating grounds. Spatial autocorrelation of data was controlled with spatial analysis using distance indices (SADIE) which identifies parasite or pollutant patches (high levels) and gaps (low levels), expressing them as clustering indices compared at each point to produce a measure of spatial association. Symbionts included the peritrich ciliates Epistylis sp. and Zoothamnium penaei and all symbionts were pooled. Helminths included Hysterothylacium sp., Opecoeloides fimbriatus, Prochristianella penaei and an unidentified cestode. Thirty-five pollutants were identified, including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides and heavy metals. The PAHs (2-3 ring) in water, unresolved complex mixture (UCM), Ni and V in sediments, and Zn, Cr and heptachlor in shrimps were significantly clustered. The remaining pollutants were randomly distributed in the study area. Juvenile shrimps acquired pesticides, PAHs (2-3 rings) and Zn, while adults acquired PAHs (4-5 rings), Cu and V. Results suggest natural PAH spillovers, and continental runoff of dichlorodiphenyltrichloroethane (DDT), PCBs and PAHs (2-3 ring). There were no significant associations between pollutants and helminths. However, there were significant negative associations of pesticides, UCM and PCBs with symbiont numbers after controlling shrimp size and spatial autocorrelation. Shrimps and their symbionts appear to be promising bioindicators of organic chemical pollution in Campeche Sound.

Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers

NASA Astrophysics Data System (ADS)

Lakshmi Madhavan, Bomidi; Deneke, Hartwig; Witthuhn, Jonas; Macke, Andreas

2017-03-01

The time series of global radiation observed by a dense network of 99 autonomous pyranometers during the HOPE campaign around Jülich, Germany, are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken-cloud conditions compared to clear, cirrus, or overcast skies. The spatial autocorrelation function including its frequency dependence is determined to quantify the degree of similarity of two time series measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies above 1/3 min-1 and points separated by more than 1 km, variations in transmittance become completely uncorrelated. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness; on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid box of 10 km × 10 km and averaging periods of 1.5-3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 (clear), 1.8 (cirrus), 1.5 (overcast), and 4.2 % (broken clouds). The solar global radiation observed at a single station is found to deviate from the spatial average by as much as 14-23 (clear), 8-26 (cirrus), 4-23 (overcast), and 31-79 W m-2 (broken clouds) from domain averages ranging from 1 km × 1 km to 10 km × 10 km in area.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012.

PubMed

Naish, Suchithra; Dale, Pat; Mackenzie, John S; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f.  = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

Spatiotemporal analysis of Quaternary normal faults in the Northern Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Davarpanah, A.; Babaie, H. A.; Reed, P.

2010-12-01

The mid-Tertiary Basin-and-Range extensional tectonic event developed most of the normal faults that bound the ranges in the northern Rocky Mountains within Montana, Wyoming, and Idaho. The interaction of the thermally induced stress field of the Yellowstone hot spot with the existing Basin-and-Range fault blocks, during the last 15 my, has produced a new, spatially and temporally variable system of normal faults in these areas. The orientation and spatial distribution of the trace of these hot-spot induced normal faults, relative to earlier Basin-and-Range faults, have significant implications for the effect of the temporally varying and spatially propagating thermal dome on the growth of new hot spot related normal faults and reactivation of existing Basin-and-Range faults. Digitally enhanced LANDSAT 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 4 and 5 Thematic Mapper (TM) bands, with spatial resolution of 30 m, combined with analytical GIS and geological techniques helped in determining and analyzing the lineaments and traces of the Quaternary, thermally-induced normal faults in the study area. Applying the color composite (CC) image enhancement technique, the combination of bands 3, 2 and 1 of the ETM+ and TM images was chosen as the best statistical choice to create a color composite for lineament identification. The spatiotemporal analysis of the Quaternary normal faults produces significant information on the structural style, timing, spatial variation, spatial density, and frequency of the faults. The seismic Quaternary normal faults, in the whole study area, are divided, based on their age, into four specific sets, which from oldest to youngest include: Quaternary (>1.6 Ma), middle and late Quaternary (>750 ka), latest Quaternary (>15 ka), and the last 150 years. A density map for the Quaternary faults reveals that most active faults are near the current Yellowstone National Park area (YNP), where most seismically active faults, in the past 1.6 my, are located. The GIS based autocorrelation method, applied to the trace orientation, length, frequency, and spatial distribution for each age-defined fault set, revealed spatial homogeneity for each specific set. The results of the method of Moran`sI and Geary`s C show no spatial autocorrelation among the trend of the fault traces and their location. Our results suggest that while lineaments of similar age define a clustered pattern in each domain, the overall distribution pattern of lineaments with different ages seems to be non-uniform (random). The directional distribution analysis reveals a distinct range of variation for fault traces of different ages (i.e., some displaying ellipsis behavior). Among the Quaternary normal fault sets, the youngest lineament set (i.e., last 150 years) defines the greatest ellipticity (eccentricity) and the least lineaments distribution variation. The frequency rose diagram for the entire Quaternary normal faults, shows four major modes (around 360o, 330o, 300o, and 270o), and two minor modes (around 235 and 205).

Deciphering the adjustment between environment and life history in annuals: lessons from a geographically-explicit approach in Arabidopsis thaliana.

PubMed

Manzano-Piedras, Esperanza; Marcer, Arnald; Alonso-Blanco, Carlos; Picó, F Xavier

2014-01-01

The role that different life-history traits may have in the process of adaptation caused by divergent selection can be assessed by using extensive collections of geographically-explicit populations. This is because adaptive phenotypic variation shifts gradually across space as a result of the geographic patterns of variation in environmental selective pressures. Hence, large-scale experiments are needed to identify relevant adaptive life-history traits as well as their relationships with putative selective agents. We conducted a field experiment with 279 geo-referenced accessions of the annual plant Arabidopsis thaliana collected across a native region of its distribution range, the Iberian Peninsula. We quantified variation in life-history traits throughout the entire life cycle. We built a geographic information system to generate an environmental data set encompassing climate, vegetation and soil data. We analysed the spatial autocorrelation patterns of environmental variables and life-history traits, as well as the relationship between environmental and phenotypic data. Almost all environmental variables were significantly spatially autocorrelated. By contrast, only two life-history traits, seed weight and flowering time, exhibited significant spatial autocorrelation. Flowering time, and to a lower extent seed weight, were the life-history traits with the highest significant correlation coefficients with environmental factors, in particular with annual mean temperature. In general, individual fitness was higher for accessions with more vigorous seed germination, higher recruitment and later flowering times. Variation in flowering time mediated by temperature appears to be the main life-history trait by which A. thaliana adjusts its life history to the varying Iberian environmental conditions. The use of extensive geographically-explicit data sets obtained from field experiments represents a powerful approach to unravel adaptive patterns of variation. In a context of current global warming, geographically-explicit approaches, evaluating the match between organisms and the environments where they live, may contribute to better assess and predict the consequences of global warming.

Spatial autocorrelation in uptake of antenatal care and relationship to individual, household and village-level factors: results from a community-based survey of pregnant women in six districts in western Kenya.

PubMed

Prudhomme O'Meara, Wendy; Platt, Alyssa; Naanyu, Violet; Cole, Donald; Ndege, Samson

2013-12-07

The majority of maternal deaths, stillbirths, and neonatal deaths are concentrated in a few countries, many of which have weak health systems, poor access to health services, and low coverage of key health interventions. Early and consistent antenatal care (ANC) attendance could significantly reduce maternal and neonatal morbidity and mortality. Despite this, most Kenyan mothers initiate ANC care late in pregnancy and attend fewer than the recommended visits. We used survey data from 6,200 pregnant women across six districts in western Kenya to understand demand-side factors related to use of ANC. Bayesian multi-level models were developed to explore the relative importance of individual, household and village-level factors in relation to ANC use. There is significant spatial autocorrelation of ANC attendance in three of the six districts and considerable heterogeneity in factors related to ANC use between districts. Working outside the home limited ANC attendance. Maternal age, the number of small children in the household, and ownership of livestock were important in some districts, but not all. Village proportions of pregnancy in women of child-bearing age was significantly correlated to ANC use in three of the six districts. Geographic distance to health facilities and the type of nearest facility was not correlated with ANC use. After incorporating individual, household and village-level covariates, no residual spatial autocorrelation remained in the outcome. ANC attendance was consistently low across all the districts, but factors related to poor attendance varied. This heterogeneity is expected for an outcome that is highly influenced by socio-cultural values and local context. Interventions to improve use of ANC must be tailored to local context and should include explicit approaches to reach women who work outside the home.

Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses.

PubMed

Griffith, Daniel A; Peres-Neto, Pedro R

2006-10-01

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.

Spatio-temporal Eigenvector Filtering: Application on Bioenergy Crop Impacts

NASA Astrophysics Data System (ADS)

Wang, M.; Kamarianakis, Y.; Georgescu, M.

2017-12-01

A suite of 10-year ensemble-based simulations was conducted to investigate the hydroclimatic impacts due to large-scale deployment of perennial bioenergy crops across the continental United States. Given the large size of the simulated dataset (about 60Tb), traditional hierarchical spatio-temporal statistical modelling cannot be implemented for the evaluation of physics parameterizations and biofuel impacts. In this work, we propose a filtering algorithm that takes into account the spatio-temporal autocorrelation structure of the data while avoiding spatial confounding. This method is used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations and observational datasets. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

USGS Publications Warehouse

Rey, Sergio J.; Stephens, Philip A.; Laura, Jason R.

2017-01-01

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.

Geospatial patterns in influenza vaccination: evidence from uninsured and publicly insured children in North Carolina.

PubMed

Trogdon, Justin G; Ahn, Thomas

2015-03-01

The purpose of this study was to explore geospatial patterns in influenza vaccination. We conducted an ecological analysis of publicly funded influenza vaccinations at the ZIP code tabulation area (ZCTA) level using secondary data for publicly funded influenza vaccinations among eligible school-aged children (age range, 5-17 years) for the 2010-2011 and 2011-2012 influenza seasons from the North Carolina Immunization Registry (NCIR). NCIR data were merged by ZCTA with other publicly available data. We tested for spatial autocorrelation in unadjusted influenza vaccination rates using choropleth maps and Moran's I. We estimated nonspatial and spatial negative binomial models with spatially correlated random effects adjusted for demographic, economic, and health care variables. The study was conducted at the University of North Carolina at Chapel Hill in the spring of 2014. The NCIR demonstrated spatial autocorrelation in publicly funded influenza vaccinations among uninsured and means-tested, publicly insured school-aged children; ZCTAs tended to have influenza vaccination rates that were similar to their neighbors. This result was partially explained by included ZCTA characteristics, but not wholly. To the extent that the geospatial clustering of vaccination rates is the result of social influences, targeting interventions to increase influenza vaccination among school-aged children in one area could also lead to increases in neighboring areas. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

Do Sexually Oriented Massage Parlors Cluster in Specific Neighborhoods? A Spatial Analysis of Indoor Sex Work in Los Angeles and Orange Counties, California

PubMed Central

Kim, Anna J.; Takahashi, Lois; Wiebe, Douglas J.

2015-01-01

Objective Social determinants of health may be substantially affected by spatial factors, which together may explain the persistence of health inequities. Clustering of possible sources of negative health and social outcomes points to a spatial focus for future interventions. We analyzed the spatial clustering of sex work businesses in Southern California to examine where and why they cluster. We explored economic and legal factors as possible explanations of clustering. Methods We manually coded data from a website used by paying members to post reviews of female massage parlor workers. We identified clusters of sexually oriented massage parlor businesses using spatial autocorrelation tests. We conducted spatial regression using census tract data to identify predictors of clustering. Results A total of 889 venues were identified. Clusters of tracts having higher-than-expected numbers of sexually oriented massage parlors (“hot spots”) were located outside downtowns. These hot spots were characterized by a higher proportion of adult males, a higher proportion of households below the federal poverty level, and a smaller average household size. Conclusion Sexually oriented massage parlors in Los Angeles and Orange counties cluster in particular neighborhoods. More research is needed to ascertain the causal factors of such clusters and how interventions can be designed to leverage these spatial factors. PMID:26327731

An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process

NASA Technical Reports Server (NTRS)

Parrish, R. S.; Carter, M. C.

1974-01-01

This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.

Method to manage integration error in the Green-Kubo method.

PubMed

Oliveira, Laura de Sousa; Greaney, P Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Method to manage integration error in the Green-Kubo method

NASA Astrophysics Data System (ADS)

Oliveira, Laura de Sousa; Greaney, P. Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Propagation of mechanical waves through a stochastic medium with spherical symmetry

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, J. Adrián

2018-01-01

We theoretically analyze the propagation of outgoing mechanical waves through an infinite isotropic elastic medium possessing spherical symmetry whose Lamé coefficients and density are spatial random functions characterized by well-defined statistical parameters. We derive the differential equation that governs the average displacement for a system whose properties depend on the radial coordinate. We show that such an equation is an extended version of the well-known Bessel differential equation whose perturbative additional terms contain coefficients that depend directly on the squared noise intensities and the autocorrelation lengths in an exponential decay fashion. We numerically solve the second order differential equation for several values of noise intensities and autocorrelation lengths and compare the corresponding displacement profiles with that of the exact analytic solution for the case of absent inhomogeneities.

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms I: Revisiting Cluster-Based Inferences.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Sathian, K

2018-02-01

In a recent study, Eklund et al. employed resting-state functional magnetic resonance imaging data as a surrogate for null functional magnetic resonance imaging (fMRI) datasets and posited that cluster-wise family-wise error (FWE) rate-corrected inferences made by using parametric statistical methods in fMRI studies over the past two decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; this was principally because the spatial autocorrelation functions (sACF) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggested otherwise. Here, we show that accounting for non-Gaussian signal components such as those arising from resting-state neural activity as well as physiological responses and motion artifacts in the null fMRI datasets yields first- and second-level general linear model analysis residuals with nearly uniform and Gaussian sACF. Further comparison with nonparametric permutation tests indicates that cluster-based FWE corrected inferences made with Gaussian spatial noise approximations are valid.

Defining surfaces for skewed, highly variable data

USGS Publications Warehouse

Helsel, D.R.; Ryker, S.J.

2002-01-01

Skewness of environmental data is often caused by more than simply a handful of outliers in an otherwise normal distribution. Statistical procedures for such datasets must be sufficiently robust to deal with distributions that are strongly non-normal, containing both a large proportion of outliers and a skewed main body of data. In the field of water quality, skewness is commonly associated with large variation over short distances. Spatial analysis of such data generally requires either considerable effort at modeling or the use of robust procedures not strongly affected by skewness and local variability. Using a skewed dataset of 675 nitrate measurements in ground water, commonly used methods for defining a surface (least-squares regression and kriging) are compared to a more robust method (loess). Three choices are critical in defining a surface: (i) is the surface to be a central mean or median surface? (ii) is either a well-fitting transformation or a robust and scale-independent measure of center used? (iii) does local spatial autocorrelation assist in or detract from addressing objectives? Published in 2002 by John Wiley & Sons, Ltd.

Temporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia.

PubMed

Sham, Noraishah M; Krishnarajah, Isthrinayagy; Ibrahim, Noor Akma; Lye, Munn-Sann

2014-05-01

Hand, foot and mouth disease (HFMD) is endemic in Sarawak, Malaysia. In this study, a geographical information system (GIS) was used to investigate the relationship between the reported HFMD cases and the spatial patterns in 11 districts of Sarawak from 2006 to 2012. Within this 7-years period, the highest number of reported HFMD cases occurred in 2006, followed by 2012, 2008, 2009, 2007, 2010 and 2011, in descending order. However, while there was no significant distribution pattern or clustering in the first part of the study period (2006 to 2011) based on Moran's I statistic, spatial autocorrelation (P = 0.068) was observed in 2012.

Impacting the effect of fMRI noise through hardware and acquisition choices - Implications for controlling false positive rates.

PubMed

Wald, Lawrence L; Polimeni, Jonathan R

2017-07-01

We review the components of time-series noise in fMRI experiments and the effect of image acquisition parameters on the noise. In addition to helping determine the total amount of signal and noise (and thus temporal SNR), the acquisition parameters have been shown to be critical in determining the ratio of thermal to physiological induced noise components in the time series. Although limited attention has been given to this latter metric, we show that it determines the degree of spatial correlations seen in the time-series noise. The spatially correlations of the physiological noise component are well known, but recent studies have shown that they can lead to a higher than expected false-positive rate in cluster-wise inference based on parametric statistical methods used by many researchers. Based on understanding the effect of acquisition parameters on the noise mixture, we propose several acquisition strategies that might be helpful reducing this elevated false-positive rate, such as moving to high spatial resolution or using highly-accelerated acquisitions where thermal sources dominate. We suggest that the spatial noise correlations at the root of the inflated false-positive rate problem can be limited with these strategies, and the well-behaved spatial auto-correlation functions (ACFs) assumed by the conventional statistical methods are retained if the high resolution data is smoothed to conventional resolutions. Copyright © 2017 Elsevier Inc. All rights reserved.

Spatiotemporal distribution patterns of forest fires in northern Mexico

Treesearch

Gustavo Pérez-Verdin; M. A. Márquez-Linares; A. Cortes-Ortiz; M. Salmerón-Macias

2013-01-01

Using the 2000-2011 CONAFOR databases, a spatiotemporal analysis of the occurrence of forest fires in Durango, one of the most affected States in Mexico, was conducted. The Moran's index was used to determine a spatial distribution pattern; also, an analysis of seasonal and temporal autocorrelation of the data collected was completed. The geographically weighted...

User's guide to Monte Carlo methods for evaluating path integrals

NASA Astrophysics Data System (ADS)

Westbroek, Marise J. E.; King, Peter R.; Vvedensky, Dimitri D.; Dürr, Stephan

2018-04-01

We give an introduction to the calculation of path integrals on a lattice, with the quantum harmonic oscillator as an example. In addition to providing an explicit computational setup and corresponding pseudocode, we pay particular attention to the existence of autocorrelations and the calculation of reliable errors. The over-relaxation technique is presented as a way to counter strong autocorrelations. The simulation methods can be extended to compute observables for path integrals in other settings.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands.

PubMed

Rood, Ente J J; Goris, Marga G A; Pijnacker, Roan; Bakker, Mirjam I; Hartskeerl, Rudy A

2017-01-01

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995-2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas.

Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands

PubMed Central

Goris, Marga G. A.; Pijnacker, Roan; Bakker, Mirjam I.; Hartskeerl, Rudy A.

2017-01-01

Leptospirosis is a globally emerging zoonotic disease, associated with various climatic, biotic and abiotic factors. Mapping and quantifying geographical variations in the occurrence of leptospirosis and the surrounding environment offer innovative methods to study disease transmission and to identify associations between the disease and the environment. This study aims to investigate geographic variations in leptospirosis incidence in the Netherlands and to identify associations with environmental factors driving the emergence of the disease. Individual case data derived over the period 1995–2012 in the Netherlands were geocoded and aggregated by municipality. Environmental covariate data were extracted for each municipality and stored in a spatial database. Spatial clusters were identified using kernel density estimations and quantified using local autocorrelation statistics. Associations between the incidence of leptospirosis and the local environment were determined using Simultaneous Autoregressive Models (SAR) explicitly modelling spatial dependence of the model residuals. Leptospirosis incidence rates were found to be spatially clustered, showing a marked spatial pattern. Fitting a spatial autoregressive model significantly improved model fit and revealed significant association between leptospirosis and the coverage of arable land, built up area, grassland and sabulous clay soils. The incidence of leptospirosis in the Netherlands could effectively be modelled using a combination of soil and land-use variables accounting for spatial dependence of incidence rates per municipality. The resulting spatially explicit risk predictions provide an important source of information which will benefit clinical awareness on potential leptospirosis infections in endemic areas. PMID:29065186

Eigenvector Spatial Filtering Regression Modeling of Ground PM2.5 Concentrations Using Remotely Sensed Data.

PubMed

Zhang, Jingyi; Li, Bin; Chen, Yumin; Chen, Meijie; Fang, Tao; Liu, Yongfeng

2018-06-11

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM 2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM 2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM 2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM 2.5 analysis and prediction.

Advances in nonmarket valuation econometrics: Spatial heterogeneity in hedonic pricing models and preference heterogeneity in stated preference models

NASA Astrophysics Data System (ADS)

Yoo, Jin Woo

In my 1st essay, the study explores Pennsylvania residents. willingness to pay for development of renewable energy technologies such as solar power, wind power, biomass electricity, and other renewable energy using a choice experiment method. Principle component analysis identified 3 independent attitude components that affect the variation of preference, a desire for renewable energy and environmental quality and concern over cost. The results show that urban residents have a higher desire for environmental quality and concern less about cost than rural residents and consequently have a higher willingness to pay to increase renewable energy production. The results of sub-sample analysis show that a representative respondent in rural (urban) Pennsylvania is willing to pay 3.8(5.9) and 4.1(5.7)/month for increasing the share of Pennsylvania electricity generated from wind power and other renewable energy by 1 percent point, respectively. Mean WTP for solar and biomass electricity was not significantly different from zero. In my second essay, heterogeneity of individual WTP for various renewable energy technologies is investigated using several different variants of the multinomial logit model: a simple MNL with interaction terms, a latent class choice model, a random parameter mixed logit choice model, and a random parameter-latent class choice model. The results of all models consistently show that respondents. preference for individual renewable technology is heterogeneous, but the degree of heterogeneity differs for different renewable technologies. In general, the random parameter logit model with interactions and a hybrid random parameter logit-latent class model fit better than other models and better capture respondents. heterogeneity of preference for renewable energy. The impact of the land under agricultural conservation easement (ACE) contract on the values of nearby residential properties is investigated using housing sales data in two Pennsylvania Counties. The spatial-lag (SLM), the spatial error (SEM) and the spatial error component (SEC) models were compared. A geographically weighted regression (GWR) model is estimated to study the spatial heterogeneity of the marginal implicit prices of ACE impact within each county. New hybrid spatial hedonic models, the GWR-SEC and a modified GWR-SEM, are estimated such that both spatial autocorrelation and heterogeneity are accounted. The results show that the coefficient of land under easement contract varies spatially within one county, but not within the other county studied. Also, ACE's are found to have both positive and negative impacts on the values of nearby residential properties. Among global spatial models, the SEM fit better than the SLM and the SEC. Statistical goodness of fit measures showed that the GWR-SEC model fit better than the GWR or the GWR-SEC model. Finally, the GWR-SEC showed spatial autocorrelation is stronger in one county than in the other county.

Source localization of narrow band signals in multipath environments, with application to marine mammals

NASA Astrophysics Data System (ADS)

Valtierra, Robert Daniel

Passive acoustic localization has benefited from many major developments and has become an increasingly important focus point in marine mammal research. Several challenges still remain. This work seeks to address several of these challenges such as tracking the calling depths of baleen whales. In this work, data from an array of widely spaced Marine Acoustic Recording Units (MARUs) was used to achieve three dimensional localization by combining the methods Time Difference of Arrival (TDOA) and Direct-Reflected Time Difference of Arrival (DRTD) along with a newly developed autocorrelation technique. TDOA was applied to data for two dimensional (latitude and longitude) localization and depth was resolved using DRTD. Previously, DRTD had been limited to pulsed broadband signals, such as sperm whale or dolphin echolocation, where individual direct and reflected signals are separated in time. Due to the length of typical baleen whale vocalizations, individual multipath signal arrivals can overlap making time differences of arrival difficult to resolve. This problem can be solved using an autocorrelation, which can extract reflection information from overlapping signals. To establish this technique, a derivation was made to model the autocorrelation of a direct signal and its overlapping reflection. The model was exploited to derive performance limits allowing for prediction of the minimum resolvable direct-reflected time difference for a known signal type. The dependence on signal parameters (sweep rate, call duration) was also investigated. The model was then verified using both recorded and simulated data from two analysis cases for North Atlantic right whales (NARWs, Eubalaena glacialis) and humpback whales (Megaptera noveaengliae). The newly developed autocorrelation technique was then combined with DRTD and tested using data from playback transmissions to localize an acoustic transducer at a known depth and location. The combined DRTD-autocorrelation methods enabled calling depth and range estimations of a vocalizing NARW and humpback whale in two separate cases. The DRTD-autocorrelation method was then combined with TDOA to create a three dimensional track of a NARW in the Stellwagen Bank National Marine Sanctuary. Results from these experiments illustrated the potential of the combined methods to successfully resolve baleen calling depths in three dimensions.

Geographic Distribution of Trauma Centers and Injury Related Mortality in the United States

PubMed Central

Brown, Joshua B.; Rosengart, Matthew R.; Billiar, Timothy R.; Peitzman, Andrew B.; Sperry, Jason L.

2015-01-01

Background Regionalized trauma care improves outcomes; however access to care is not uniform across the US. The objective was to evaluate whether geographic distribution of trauma centers correlates with injury mortality across state trauma systems. Methods Level I/II trauma centers in the contiguous US were mapped. State-level age-adjusted injury fatality rates/100,000people were obtained and evaluated for spatial autocorrelation. Nearest neighbor ratios (NNR) were generated for each state. A NNR<1 indicates clustering, while NNR>1 indicates dispersion. NNR were tested for difference from random geographic distribution. Fatality rates and NNR were examined for correlation. Fatality rates were compared between states with trauma center clustering versus dispersion. Trauma center distribution and population density were evaluated. Spatial-lag regression determined the association between fatality rate and NNR, controlling for state-level demographics, population density, injury severity, trauma system resources, and socioeconomic factors. Results Fatality rates were spatially autocorrelated (Moran's I=0.35, p<0.01). Nine states had a clustered pattern (median NNR 0.55, IQR 0.48–0.60), 22 had a dispersed pattern (median NNR 2.00, IQR 1.68–3.99), and 10 had a random pattern (median NNR 0.90, IQR 0.85–1.00) of trauma center distribution. Fatality rate and NNR were correlated (ρ=0.34, p=0.03). Clustered states had a lower median injury fatality rate compared to dispersed states (56.9 [IQR 46.5–58.9] versus 64.9 [IQR 52.5–77.1], p=0.04). Dispersed compared to clustered states had more counties without a trauma center that had higher population density than counties with a trauma center (5.7% versus 1.2%, p<0.01). Spatial-lag regression demonstrated fatality rates increased 0.02/100,000persons for each unit increase in NNR (p<0.01). Conclusions Geographic distribution of trauma centers correlates with injury mortality, with more clustered state trauma centers associated with lower fatality rates. This may be a result of access relative to population density. These results may have implications for trauma system planning and requires further study to investigate underlying mechanisms PMID:26517780

Simulating and mapping spatial complexity using multi-scale techniques

USGS Publications Warehouse

De Cola, L.

1994-01-01

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields. -Author

DETAILED DATA ANALYSIS OF ECHO I, ECHO II AND MOON REFLECTED SIGNALS. VOLUME 2. AUTOCORRELATION FUNCTIONS OF ECHO II REFLECTED SIGNALS,

DTIC Science & Technology

techniques is presented. Two methods for linearizing the data are given. An expression for the specular-to-spattered power ratio is derived, and the inverse ... transform of the autocorrelation function is discussed. The surface roughness of the reflector, the discrete fading rates, and fading frequencies

Pattern detection in stream networks: Quantifying spatialvariability in fish distribution

USGS Publications Warehouse

Torgersen, Christian E.; Gresswell, Robert E.; Bateman, Douglas S.

2004-01-01

Biological and physical properties of rivers and streams are inherently difficult to sample and visualize at the resolution and extent necessary to detect fine-scale distributional patterns over large areas. Satellite imagery and broad-scale fish survey methods are effective for quantifying spatial variability in biological and physical variables over a range of scales in marine environments but are often too coarse in resolution to address conservation needs in inland fisheries management. We present methods for sampling and analyzing multiscale, spatially continuous patterns of stream fishes and physical habitat in small- to medium-size watersheds (500–1000 hectares). Geospatial tools, including geographic information system (GIS) software such as ArcInfo dynamic segmentation and ArcScene 3D analyst modules, were used to display complex biological and physical datasets. These tools also provided spatial referencing information (e.g. Cartesian and route-measure coordinates) necessary for conducting geostatistical analyses of spatial patterns (empirical semivariograms and wavelet analysis) in linear stream networks. Graphical depiction of fish distribution along a one-dimensional longitudinal profile and throughout the stream network (superimposed on a 10-metre digital elevation model) provided the spatial context necessary for describing and interpreting the relationship between landscape pattern and the distribution of coastal cutthroat trout (Oncorhynchus clarki clarki) in western Oregon, U.S.A. The distribution of coastal cutthroat trout was highly autocorrelated and exhibited a spherical semivariogram with a defined nugget, sill, and range. Wavelet analysis of the main-stem longitudinal profile revealed periodicity in trout distribution at three nested spatial scales corresponding ostensibly to landscape disturbances and the spacing of tributary junctions.

The Effects of City Streets on an Urban Disease Vector

PubMed Central

Barbu, Corentin M.; Hong, Andrew; Manne, Jennifer M.; Small, Dylan S.; Quintanilla Calderón, Javier E.; Sethuraman, Karthik; Quispe-Machaca, Víctor; Ancca-Juárez, Jenny; Cornejo del Carpio, Juan G.; Málaga Chavez, Fernando S.; Náquira, César; Levy, Michael Z.

2013-01-01

With increasing urbanization vector-borne diseases are quickly developing in cities, and urban control strategies are needed. If streets are shown to be barriers to disease vectors, city blocks could be used as a convenient and relevant spatial unit of study and control. Unfortunately, existing spatial analysis tools do not allow for assessment of the impact of an urban grid on the presence of disease agents. Here, we first propose a method to test for the significance of the impact of streets on vector infestation based on a decomposition of Moran's spatial autocorrelation index; and second, develop a Gaussian Field Latent Class model to finely describe the effect of streets while controlling for cofactors and imperfect detection of vectors. We apply these methods to cross-sectional data of infestation by the Chagas disease vector Triatoma infestans in the city of Arequipa, Peru. Our Moran's decomposition test reveals that the distribution of T. infestans in this urban environment is significantly constrained by streets (p<0.05). With the Gaussian Field Latent Class model we confirm that streets provide a barrier against infestation and further show that greater than 90% of the spatial component of the probability of vector presence is explained by the correlation among houses within city blocks. The city block is thus likely to be an appropriate spatial unit to describe and control T. infestans in an urban context. Characteristics of the urban grid can influence the spatial dynamics of vector borne disease and should be considered when designing public health policies. PMID:23341756

Restoration of motion blurred image with Lucy-Richardson algorithm

NASA Astrophysics Data System (ADS)

Li, Jing; Liu, Zhao Hui; Zhou, Liang

2015-10-01

Images will be blurred by relative motion between the camera and the object of interest. In this paper, we analyzed the process of motion-blurred image, and demonstrated a restoration method based on Lucy-Richardson algorithm. The blur extent and angle can be estimated by Radon transform algorithm and auto-correlation function, respectively, and then the point spread function (PSF) of the motion-blurred image can be obtained. Thus with the help of the obtained PSF, the Lucy-Richardson restoration algorithm is used for experimental analysis on the motion-blurred images that have different blur extents, spatial resolutions and signal-to-noise ratios (SNR's). Further, its effectiveness is also evaluated by structural similarity (SSIM). Further studies show that, at first, for the image with a spatial frequency of 0.2 per pixel, the modulation transfer function (MTF) of the restored images can maintains above 0.7 when the blur extent is no bigger than 13 pixels. That means the method compensates low frequency information of the image, while attenuates high frequency information. At second, we fund that the method is more effective on condition that the product of the blur extent and spatial frequency is smaller than 3.75. Finally, the Lucy-Richardson algorithm is found insensitive to the Gaussian noise (of which the variance is not bigger than 0.1) by calculating the MTF of the restored image.

Monitoring Method of Cow Anthrax Based on Gis and Spatial Statistical Analysis

NASA Astrophysics Data System (ADS)

Li, Lin; Yang, Yong; Wang, Hongbin; Dong, Jing; Zhao, Yujun; He, Jianbin; Fan, Honggang

Geographic information system (GIS) is a computer application system, which possesses the ability of manipulating spatial information and has been used in many fields related with the spatial information management. Many methods and models have been established for analyzing animal diseases distribution models and temporal-spatial transmission models. Great benefits have been gained from the application of GIS in animal disease epidemiology. GIS is now a very important tool in animal disease epidemiological research. Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. In this paper, we analyzed the cow anthrax spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow anthrax in this district in combination of spatial statistical analysis and GIS. The Cow anthrax is biogeochemical disease, and its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of anthrax cow for monitoring and the prevention and control of anthrax has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness. The GIS computer application software ArcGIS9.1 was used to overcome the lack of data of sampling sites.Using ArcGIS 9.1 and GEODA to analyze the cow anthrax spatial distribution of district A. we gained some conclusions about cow anthrax' density: (1) there is a spatial clustering model. (2) there is an intensely spatial autocorrelation. We established a prediction model to estimate the anthrax distribution based on the spatial characteristic of the density of cow anthrax. Comparing with the true distribution, the prediction model has a well coincidence and is feasible to the application. The method using a GIS tool facilitates can be implemented significantly in the cow anthrax monitoring and investigation, and the space statistics - related prediction model provides a fundamental use for other study on space-related animal diseases.

Dispersion relation for electromagnetic propagation in stochastic dielectric and magnetic helical photonic crystals

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, Arturo

2017-03-01

We theoretically study the dispersion relation for axially propagating electromagnetic waves throughout a one-dimensional helical structure whose pitch and dielectric and magnetic properties are spatial random functions with specific statistical characteristics. In the system of coordinates rotating with the helix, by using a matrix formalism, we write the set of differential equations that governs the expected value of the electromagnetic field amplitudes and we obtain the corresponding dispersion relation. We show that the dispersion relation depends strongly on the noise intensity introduced in the system and the autocorrelation length. When the autocorrelation length increases at fixed fluctuation and when the fluctuation augments at fixed autocorrelation length, the band gap widens and the attenuation coefficient of electromagnetic waves propagating in the random medium gets larger. By virtue of the degeneracy in the imaginary part of the eigenvalues associated with the propagating modes, the random medium acts as a filter for circularly polarized electromagnetic waves, in which only the propagating backward circularly polarized wave can propagate with no attenuation. Our results are valid for any kind of dielectric and magnetic structures which possess a helical-like symmetry such as cholesteric and chiral smectic-C liquid crystals, structurally chiral materials, and stressed cholesteric elastomers.

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York

PubMed Central

Goovaerts, Pierre; Jacquez, Geoffrey M

2004-01-01

Background Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA. Results We developed a typology of neutral models that progressively relaxes the assumptions of null hypotheses, allowing for the presence of spatial autocorrelation, non-uniform risk, and incorporation of spatially heterogeneous population sizes. Incorporation of spatial autocorrelation led to fewer significant ZIP codes than found in previous studies, confirming earlier claims that CSR can lead to over-identification of the number of significant spatial clusters or outliers. Accounting for population size through geostatistical filtering increased the size of clusters while removing most of the spatial outliers. Integration of regional background into the neutral models yielded substantially different spatial clusters and outliers, leading to the identification of ZIP codes where SMR values significantly depart from their regional background. Conclusion The approach presented in this paper enables researchers to assess geographic relationships using appropriate null hypotheses that account for the background variation extant in real-world systems. In particular, this new methodology allows one to identify geographic pattern above and beyond background variation. The implementation of this approach in spatial statistical software will facilitate the detection of spatial disparities in mortality rates, establishing the rationale for targeted cancer control interventions, including consideration of health services needs, and resource allocation for screening and diagnostic testing. It will allow researchers to systematically evaluate how sensitive their results are to assumptions implicit under alternative null hypotheses. PMID:15272930

Spatiotemporal patterns of severe fever with thrombocytopenia syndrome in China, 2011-2016.

PubMed

Sun, Jimin; Lu, Liang; Wu, Haixia; Yang, Jun; Liu, Keke; Liu, Qiyong

2018-05-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and the number of SFTS cases have increased year by year in China. However, spatiotemporal patterns and trends of SFTS are less clear up to date. In order to explore spatiotemporal patterns and predict SFTS incidences, we analyzed temporal trends of SFTS using autoregressive integrated moving average (ARIMA) model, spatial patterns, and spatiotemporal clusters of SFTS cases at the county level based on SFTS data in China during 2011-2016. We determined the optimal time series model was ARIMA (2, 0, 1) × (0, 0, 1) 12 which fitted the SFTS cases reasonably well during the training process and forecast process. In the spatial clustering analysis, the global autocorrelation suggested that SFTS cases were not of random distribution. Local spatial autocorrelation analysis of SFTS identified foci mainly concentrated in Hubei Province, Henan Province, Anhui Province, Shandong Province, Liaoning Province, and Zhejiang Province. A most likely cluster including 21 counties in Henan Province and Hubei Province was observed in the central region of China from April 2015 to August 2016. Our results will provide a sound evidence base for future prevention and control programs of SFTS such as allocation of the health resources, surveillance in high-risk regions, health education, improvement of diagnosis and so on. Copyright © 2018 Elsevier GmbH. All rights reserved.

Quantitative analysis of scale of aeromagnetic data raises questions about geologic-map scale

USGS Publications Warehouse

Nykanen, V.; Raines, G.L.

2006-01-01

A recently published study has shown that small-scale geologic map data can reproduce mineral assessments made with considerably larger scale data. This result contradicts conventional wisdom about the importance of scale in mineral exploration, at least for regional studies. In order to formally investigate aspects of scale, a weights-of-evidence analysis using known gold occurrences and deposits in the Central Lapland Greenstone Belt of Finland as training sites provided a test of the predictive power of the aeromagnetic data. These orogenic-mesothermal-type gold occurrences and deposits have strong lithologic and structural controls associated with long (up to several kilometers), narrow (up to hundreds of meters) hydrothermal alteration zones with associated magnetic lows. The aeromagnetic data were processed using conventional geophysical methods of successive upward continuation simulating terrane clearance or 'flight height' from the original 30 m to an artificial 2000 m. The analyses show, as expected, that the predictive power of aeromagnetic data, as measured by the weights-of-evidence contrast, decreases with increasing flight height. Interestingly, the Moran autocorrelation of aeromagnetic data representing differing flight height, that is spatial scales, decreases with decreasing resolution of source data. The Moran autocorrelation coefficient scems to be another measure of the quality of the aeromagnetic data for predicting exploration targets. ?? Springer Science+Business Media, LLC 2007.

Surface‐wave Green’s tensors in the near field

USGS Publications Warehouse

Haney, Matt; Nakahara, Hisashi

2014-01-01

We demonstrate the connection between theoretical expressions for the correlation of ambient noise Rayleigh and Love waves and the exact surface‐wave Green’s tensors for a point force. The surface‐wave Green’s tensors are well known in the far‐field limit. On the other hand, the imaginary part of the exact Green’s tensors, including near‐field effects, arises in correlation techniques such as the spatial autocorrelation (SPAC) method. Using the imaginary part of the exact Green’s tensors from the SPAC method, we find the associated real part using the Kramers–Kronig relations. The application of the Kramers–Kronig relations is not straightforward, however, because the causality properties of the different tensor components vary. In addition to the Green’s tensors for a point force, we also derive expressions for a general point moment tensor source.

A geostatistical analysis of small-scale spatial variability in bacterial abundance and community structure in salt marsh creek bank sediments

NASA Technical Reports Server (NTRS)

Franklin, Rima B.; Blum, Linda K.; McComb, Alison C.; Mills, Aaron L.

2002-01-01

Small-scale variations in bacterial abundance and community structure were examined in salt marsh sediments from Virginia's eastern shore. Samples were collected at 5 cm intervals (horizontally) along a 50 cm elevation gradient, over a 215 cm horizontal transect. For each sample, bacterial abundance was determined using acridine orange direct counts and community structure was analyzed using randomly amplified polymorphic DNA fingerprinting of whole-community DNA extracts. A geostatistical analysis was used to determine the degree of spatial autocorrelation among the samples, for each variable and each direction (horizontal and vertical). The proportion of variance in bacterial abundance that could be accounted for by the spatial model was quite high (vertical: 60%, horizontal: 73%); significant autocorrelation was found among samples separated by 25 cm in the vertical direction and up to 115 cm horizontally. In contrast, most of the variability in community structure was not accounted for by simply considering the spatial separation of samples (vertical: 11%, horizontal: 22%), and must reflect variability from other parameters (e.g., variation at other spatial scales, experimental error, or environmental heterogeneity). Microbial community patch size based upon overall similarity in community structure varied between 17 cm (vertical) and 35 cm (horizontal). Overall, variability due to horizontal position (distance from the creek bank) was much smaller than that due to vertical position (elevation) for both community properties assayed. This suggests that processes more correlated with elevation (e.g., drainage and redox potential) vary at a smaller scale (therefore producing smaller patch sizes) than processes controlled by distance from the creek bank. c2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.

PubMed

Houseman, E Andres; Virji, M Abbas

2017-08-01

Direct reading instruments are valuable tools for measuring exposure as they provide real-time measurements for rapid decision making. However, their use is limited to general survey applications in part due to issues related to their performance. Moreover, statistical analysis of real-time data is complicated by autocorrelation among successive measurements, non-stationary time series, and the presence of left-censoring due to limit-of-detection (LOD). A Bayesian framework is proposed that accounts for non-stationary autocorrelation and LOD issues in exposure time-series data in order to model workplace factors that affect exposure and estimate summary statistics for tasks or other covariates of interest. A spline-based approach is used to model non-stationary autocorrelation with relatively few assumptions about autocorrelation structure. Left-censoring is addressed by integrating over the left tail of the distribution. The model is fit using Markov-Chain Monte Carlo within a Bayesian paradigm. The method can flexibly account for hierarchical relationships, random effects and fixed effects of covariates. The method is implemented using the rjags package in R, and is illustrated by applying it to real-time exposure data. Estimates for task means and covariates from the Bayesian model are compared to those from conventional frequentist models including linear regression, mixed-effects, and time-series models with different autocorrelation structures. Simulations studies are also conducted to evaluate method performance. Simulation studies with percent of measurements below the LOD ranging from 0 to 50% showed lowest root mean squared errors for task means and the least biased standard deviations from the Bayesian model compared to the frequentist models across all levels of LOD. In the application, task means from the Bayesian model were similar to means from the frequentist models, while the standard deviations were different. Parameter estimates for covariates were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data. The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

Effects of natural factors on the spatial distribution of heavy metals in soils surrounding mining regions.

PubMed

Ding, Qian; Cheng, Gong; Wang, Yong; Zhuang, Dafang

2017-02-01

Various studies have shown that soils surrounding mining areas are seriously polluted with heavy metals. Determining the effects of natural factors on spatial distribution of heavy metals is important for determining the distribution characteristics of heavy metals in soils. In this study, an 8km buffer zone surrounding a typical non-ferrous metal mine in Suxian District of Hunan Province, China, was selected as the study area, and statistical, spatial autocorrelation and spatial interpolation analyses were used to obtain descriptive statistics and spatial autocorrelation characteristics of As, Pb, Cu, and Zn in soil. Additionally, the distributions of soil heavy metals under the influences of natural factors, including terrain (elevation and slope), wind direction and distance from a river, were determined. Layout of sampling sites, spatial changes of heavy metal contents at high elevations and concentration differences between upwind and downwind directions were then evaluated. The following results were obtained: (1) At low elevations, heavy metal concentrations decreased slightly, then increased considerably with increasing elevation. At high elevations, heavy metal concentrations first decreased, then increased, then decreased with increasing elevation. As the slope increased, heavy metal contents increased then decreased. (2) Heavy metal contents changed consistently in the upwind and downwind directions. Heavy metal contents were highest in 1km buffer zone and decreased with increasing distance from the mining area. The largest decrease in heavy metal concentrations was in 2km buffer zone. Perennial wind promotes the transport of heavy metals in downwind direction. (3) The spatial extent of the influence of the river on Pb, Zn and Cu in the soil was 800m. (4) The influence of the terrain on the heavy metal concentrations was greater than that of the wind. These results provide a scientific basis for preventing and mitigating heavy metal soil pollution in areas surrounding mines. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatial distribution of soil organic carbon and total nitrogen based on GIS and geostatistics in a small watershed in a hilly area of northern China.

PubMed

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0-20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km(2)) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

PubMed Central

Peng, Gao; Bing, Wang; Guangpo, Geng; Guangcan, Zhang

2013-01-01

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed. PMID:24391791

Unidirectional trends in annual and seasonal climate and extremes in Egypt

NASA Astrophysics Data System (ADS)

Nashwan, Mohamed Salem; Shahid, Shamsuddin; Abd Rahim, Norhan

2018-05-01

The presence of short- and long-term autocorrelations can lead to considerable change in significance of trend in hydro-climatic time series. Therefore, past findings of climatic trend studies that did not consider autocorrelations became a questionable issue. The spatial patterns in the trends of annual and seasonal temperature, rainfall, and related extremes in Egypt have been assessed in this paper using modified Mann-Kendal (MMK) trend test which can detect unidirectional trends in time series in the presence of short- and long-term autocorrelations. The trends obtained using the MMK test was compared with that obtained using standard Mann-Kendall (MK) test to show how natural variability in climate affects the trends. The daily rainfall and temperature data of Princeton Global Meteorological Forcing for the period 1948-2010 having a spatial resolution of 0.25° × 0.25° was used for this purpose. The results showed a large difference between the trends obtained using MMK and MK tests. The MMK test showed increasing trends in temperature and a number of temperature extremes in Egypt, but almost no change in rainfall and rainfall extremes. The minimum temperature was found to increase (0.08-0.29 °C/decade) much faster compared to maximum temperature (0.07-0.24 °C/decade) and therefore, a decrease in diurnal temperature range (- 0.01 to - 0.16 °C/decade) in most part of Egypt. The number of winter hot days and nights are increasing, while the number of cold days is decreasing in most part of the country. The study provides a more realistic scenario of the changes in climate and weather extremes of Egypt.

Decay of the supersonic turbulent wakes from micro-ramps

NASA Astrophysics Data System (ADS)

Sun, Z.; Schrijer, F. F. J.; Scarano, F.; van Oudheusden, B. W.

2014-02-01

The wakes resulting from micro-ramps immersed in a supersonic turbulent boundary layer at Ma = 2.0 are investigated by means of particle image velocimetry. Two micro-ramps are investigated with height of 60% and 80% of the undisturbed boundary layer, respectively. The measurement domain is placed at the symmetry plane of the ramp and encompasses the range from 10 to 32 ramp heights downstream of the ramp. The decay of the flow field properties is evaluated in terms of time-averaged and root-mean-square (RMS) statistics. In the time-averaged flow field, the recovery from the imparted momentum deficit and the decay of upwash motion are analyzed. The RMS fluctuations of the velocity components exhibit strong anisotropy at the most upstream location and develop into a more isotropic regime downstream. The self-similarity properties of velocity components and fluctuation components along wall-normal direction are followed. The investigation of the unsteady large scale motion is carried out by means of snapshot analysis and by a statistical approach based on the spatial auto-correlation function. The Kelvin-Helmholtz (K-H) instability at the upper shear layer is observed to develop further with the onset of vortex pairing. The average distance between vortices is statistically estimated using the spatial auto-correlation. A marked transition with the wavelength increase is observed across the pairing regime. The K-H instability, initially observed only at the upper shear layer also begins to appear in the lower shear layer as soon as the wake is elevated sufficiently off the wall. The auto-correlation statistics confirm the coherence of counter-rotating vortices from the upper and lower sides, indicating the formation of vortex rings downstream of the pairing region.

From fast fluorescence imaging to molecular diffusion law on live cell membranes in a commercial microscope.

PubMed

Di Rienzo, Carmine; Gratton, Enrico; Beltram, Fabio; Cardarelli, Francesco

2014-10-09

It has become increasingly evident that the spatial distribution and the motion of membrane components like lipids and proteins are key factors in the regulation of many cellular functions. However, due to the fast dynamics and the tiny structures involved, a very high spatio-temporal resolution is required to catch the real behavior of molecules. Here we present the experimental protocol for studying the dynamics of fluorescently-labeled plasma-membrane proteins and lipids in live cells with high spatiotemporal resolution. Notably, this approach doesn't need to track each molecule, but it calculates population behavior using all molecules in a given region of the membrane. The starting point is a fast imaging of a given region on the membrane. Afterwards, a complete spatio-temporal autocorrelation function is calculated correlating acquired images at increasing time delays, for example each 2, 3, n repetitions. It is possible to demonstrate that the width of the peak of the spatial autocorrelation function increases at increasing time delay as a function of particle movement due to diffusion. Therefore, fitting of the series of autocorrelation functions enables to extract the actual protein mean square displacement from imaging (iMSD), here presented in the form of apparent diffusivity vs average displacement. This yields a quantitative view of the average dynamics of single molecules with nanometer accuracy. By using a GFP-tagged variant of the Transferrin Receptor (TfR) and an ATTO488 labeled 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (PPE) it is possible to observe the spatiotemporal regulation of protein and lipid diffusion on µm-sized membrane regions in the micro-to-milli-second time range.

On a method to detect long-latency excitations and inhibitions of single hand muscle motoneurons in man.

PubMed

Awiszus, F; Feistner, H; Schäfer, S S

1991-01-01

The peri-stimulus-time histogram (PSTH) analysis of stimulus-related neuronal spike train data is usually regarded as a method to detect stimulus-induced excitations or inhibitions. However, for a fairly regularly discharging neuron such as the human alpha-motoneuron, long-latency modulations of a PSTH are difficult to interpret as PSTH modulations can also occur as a consequence of a modulated neuronal autocorrelation. The experiments reported here were made (i) to investigate the extent to which a PSTH of a human hand-muscle motoneuron may be contaminated by features of the autocorrelation and (ii) to develop methods that display the motoneuronal excitations and inhibitions without such contamination. Responses of 29 single motor units to electrical ulnar nerve stimulation below motor threshold were investigated in the first dorsal interosseous muscle of three healthy volunteers using an experimental protocol capable of demonstrating the presence of autocorrelative modulations in the neuronal response. It was found for all units that the PSTH as well as the cumulative sum (CUSUM) derived from these responses were severely affected by the presence of autocorrelative features. On the other hand, calculating the CUSUM in a slightly modified form yielded--for all units investigated--a neuronal output feature sensitive only to motoneuronal excitations and inhibitions induced by the afferent volley. The price that has to be paid to arrive at such a modified CUSUM (mCUSUM) was a high computational effort prohibiting the on-line availability of this output feature during the experiment. It was found, however, that an interspike interval superposition plot (IISP)--easily obtainable during the experiment--is also free of autocorrelative features.(ABSTRACT TRUNCATED AT 250 WORDS)

The lepidoptera as predictable communities of herbivores: a test of niche assembly using the moth communities of Morgan-Monroe State Forest

Treesearch

Keith S. Summerville; Michael R. Saunders; Jamie L. Lane

2013-01-01

The response of forest insect communities to disturbances such as timber harvest likely will depend on the underlying ecological assembly rules that affect community structure. Two competing hypotheses are niche assembly, which seeks to demonstrate significant species-environment correlations, and dispersal-assembly, which seeks to demonstrate spatial autocorrelation...

GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update

PubMed Central

Peakall, Rod; Smouse, Peter E.

2012-01-01

Summary: GenAlEx: Genetic Analysis in Excel is a cross-platform package for population genetic analyses that runs within Microsoft Excel. GenAlEx offers analysis of diploid codominant, haploid and binary genetic loci and DNA sequences. Both frequency-based (F-statistics, heterozygosity, HWE, population assignment, relatedness) and distance-based (AMOVA, PCoA, Mantel tests, multivariate spatial autocorrelation) analyses are provided. New features include calculation of new estimators of population structure: G′ST, G′′ST, Jost’s Dest and F′ST through AMOVA, Shannon Information analysis, linkage disequilibrium analysis for biallelic data and novel heterogeneity tests for spatial autocorrelation analysis. Export to more than 30 other data formats is provided. Teaching tutorials and expanded step-by-step output options are included. The comprehensive guide has been fully revised. Availability and implementation: GenAlEx is written in VBA and provided as a Microsoft Excel Add-in (compatible with Excel 2003, 2007, 2010 on PC; Excel 2004, 2011 on Macintosh). GenAlEx, and supporting documentation and tutorials are freely available at: http://biology.anu.edu.au/GenAlEx. Contact: rod.peakall@anu.edu.au PMID:22820204

Statistical comparison of coherent structures in fully developed turbulent pipe flow with and without drag reduction

NASA Astrophysics Data System (ADS)

Sogaro, Francesca; Poole, Robert; Dennis, David

2014-11-01

High-speed stereoscopic particle image velocimetry has been performed in fully developed turbulent pipe flow at moderate Reynolds numbers with and without a drag-reducing additive (an aqueous solution of high molecular weight polyacrylamide). Three-dimensional large and very large-scale motions (LSM and VLSM) are extracted from the flow fields by a detection algorithm and the characteristics for each case are statistically compared. The results show that the three-dimensional extent of VLSMs in drag reduced (DR) flow appears to increase significantly compared to their Newtonian counterparts. A statistical increase in azimuthal extent of DR VLSM is observed by means of two-point spatial autocorrelation of the streamwise velocity fluctuation in the radial-azimuthal plane. Furthermore, a remarkable increase in length of these structures is observed by three-dimensional two-point spatial autocorrelation. These results are accompanied by an analysis of the swirling strength in the flow field that shows a significant reduction in strength and number of the vortices for the DR flow. The findings suggest that the damping of the small scales due to polymer addition results in the undisturbed development of longer flow structures.

Spatial statistical network models for stream and river temperature in New England, USA

NASA Astrophysics Data System (ADS)

Detenbeck, Naomi E.; Morrison, Alisa C.; Abele, Ralph W.; Kopp, Darin A.

2016-08-01

Watershed managers are challenged by the need for predictive temperature models with sufficient accuracy and geographic breadth for practical use. We described thermal regimes of New England rivers and streams based on a reduced set of metrics for the May-September growing season (July or August median temperature, diurnal rate of change, and magnitude and timing of growing season maximum) chosen through principal component analysis of 78 candidate metrics. We then developed and assessed spatial statistical models for each of these metrics, incorporating spatial autocorrelation based on both distance along the flow network and Euclidean distance between points. Calculation of spatial autocorrelation based on travel or retention time in place of network distance yielded tighter-fitting Torgegrams with less scatter but did not improve overall model prediction accuracy. We predicted monthly median July or August stream temperatures as a function of median air temperature, estimated urban heat island effect, shaded solar radiation, main channel slope, watershed storage (percent lake and wetland area), percent coarse-grained surficial deposits, and presence or maximum depth of a lake immediately upstream, with an overall root-mean-square prediction error of 1.4 and 1.5°C, respectively. Growing season maximum water temperature varied as a function of air temperature, local channel slope, shaded August solar radiation, imperviousness, and watershed storage. Predictive models for July or August daily range, maximum daily rate of change, and timing of growing season maximum were statistically significant but explained a much lower proportion of variance than the above models (5-14% of total).

Sampling design and required sample size for evaluating contamination levels of 137Cs in Japanese fir needles in a mixed deciduous forest stand in Fukushima, Japan.

PubMed

Oba, Yurika; Yamada, Toshihiro

2017-05-01

We estimated the sample size (the number of samples) required to evaluate the concentration of radiocesium ( 137 Cs) in Japanese fir (Abies firma Sieb. & Zucc.), 5 years after the outbreak of the Fukushima Daiichi Nuclear Power Plant accident. We investigated the spatial structure of the contamination levels in this species growing in a mixed deciduous broadleaf and evergreen coniferous forest stand. We sampled 40 saplings with a tree height of 150 cm-250 cm in a Fukushima forest community. The results showed that: (1) there was no correlation between the 137 Cs concentration in needles and soil, and (2) the difference in the spatial distribution pattern of 137 Cs concentration between needles and soil suggest that the contribution of root uptake to 137 Cs in new needles of this species may be minor in the 5 years after the radionuclides were released into the atmosphere. The concentration of 137 Cs in needles showed a strong positive spatial autocorrelation in the distance class from 0 to 2.5 m, suggesting that the statistical analysis of data should consider spatial autocorrelation in the case of an assessment of the radioactive contamination of forest trees. According to our sample size analysis, a sample size of seven trees was required to determine the mean contamination level within an error in the means of no more than 10%. This required sample size may be feasible for most sites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Technical support for creating an artificial intelligence system for feature extraction and experimental design

NASA Technical Reports Server (NTRS)

Glick, B. J.

1985-01-01

Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.

Geostatistical Borehole Image-Based Mapping of Karst-Carbonate Aquifer Pores.

PubMed

Sukop, Michael C; Cunningham, Kevin J

2016-03-01

Quantification of the character and spatial distribution of porosity in carbonate aquifers is important as input into computer models used in the calculation of intrinsic permeability and for next-generation, high-resolution groundwater flow simulations. Digital, optical, borehole-wall image data from three closely spaced boreholes in the karst-carbonate Biscayne aquifer in southeastern Florida are used in geostatistical experiments to assess the capabilities of various methods to create realistic two-dimensional models of vuggy megaporosity and matrix-porosity distribution in the limestone that composes the aquifer. When the borehole image data alone were used as the model training image, multiple-point geostatistics failed to detect the known spatial autocorrelation of vuggy megaporosity and matrix porosity among the three boreholes, which were only 10 m apart. Variogram analysis and subsequent Gaussian simulation produced results that showed a realistic conceptualization of horizontal continuity of strata dominated by vuggy megaporosity and matrix porosity among the three boreholes. © 2015, National Ground Water Association.

A spatial model of bird abundance as adjusted for detection probability

USGS Publications Warehouse

Gorresen, P.M.; Mcmillan, G.P.; Camp, R.J.; Pratt, T.K.

2009-01-01

Modeling the spatial distribution of animals can be complicated by spatial and temporal effects (i.e. spatial autocorrelation and trends in abundance over time) and other factors such as imperfect detection probabilities and observation-related nuisance variables. Recent advances in modeling have demonstrated various approaches that handle most of these factors but which require a degree of sampling effort (e.g. replication) not available to many field studies. We present a two-step approach that addresses these challenges to spatially model species abundance. Habitat, spatial and temporal variables were handled with a Bayesian approach which facilitated modeling hierarchically structured data. Predicted abundance was subsequently adjusted to account for imperfect detection and the area effectively sampled for each species. We provide examples of our modeling approach for two endemic Hawaiian nectarivorous honeycreepers: 'i'iwi Vestiaria coccinea and 'apapane Himatione sanguinea. ?? 2009 Ecography.

Characterization of the epidemiology of bat-borne rabies in Chile between 2003 and 2013.

PubMed

Alegria-Moran, Raul; Miranda, Daniela; Barnard, Matt; Parra, Alonso; Lapierre, Lisette

2017-08-01

Rabies is a zoonotic disease of great impact to public health. According to the World Health Organization, the country of Chile is currently declared free from human rabies transmitted by dogs. An epidemiological characterization and description was conducted using rabies data from 2003 to 2013 held by the National Program for Prevention and Control of Rabies from the Ministry of Health, consisting of bats samples reported as suspect and samples taken by active surveillance (bats brain tissue). Spatial autocorrelation analysis was performed using Local Indicators of Spatial Association (LISA) statistics, particularly Moran's I index, for the detection of spatial clusters. Temporal descriptive analysis was also carried out. Nine hundred and twenty-seven positive cases were reported, presenting an average of 84 cases per year, mainly originated from passive surveillance (98.5%), whilst only 1.5% of cases were reported by active surveillance. Global positivity for the study period was 7.02% and 0.1% in passive and active surveillance respectively. Most of the cases were reported in the central zone of Chile (88.1%), followed by south zone (9.1%) and north zone (2.8%). At a regional level, Metropolitana (40.6%), Valparaíso (19.1%) and Maule (11.8%) regions reported the majority of the cases. Tadarida brasiliensis (92%) presented the majority of the cases reported, with viral variant 4 (82%) being most commonly diagnosed. Only two cases were detected in companion animals. The central zone presented a positive spatial autocorrelation (Moran's I index=0.1537, 95% CI=0.1141-0.1933; p-value=0.02); north and south zones returned non-significant results (Moran's I index=0.0517 and -0.0117, 95% CI=-0.0358-0.1392 and -0.0780-0.0546, and p-values=0.21 and 0.34 respectively). The number of rabies cases decreased between May and August (late fall and winter) and tended to increase during the hot season (December to March), confirmed with the evidence from Autocorrelation analysis and the Ljun-Box test (X 2 =234.85 and p-value<0.0001). Knowledge of animal rabies epidemiologic behaviour becomes relevant when designing prevention and control measures and surveillance programs. This is especially important considering the high impact to Public Health of this disease and that wildlife rabies in bats remains endemic in Chile. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessment of environmental injustice in Korea using synthetic air quality index and multiple indicators of socioeconomic status: A cross-sectional study.

PubMed

Choi, Giehae; Heo, Seulkee; Lee, Jong-Tae

2016-01-01

Despite the existence of the universal right to a healthy environment, the right is being violated in some populations. The objective of the current study is to verify environmental discrimination associated with socioeconomic status in Korea, using synthetic air quality index and multiple indicators of socioeconomic status. The concentrations of NO₂(nitrogen dioxide), CO (carbon monoxide), SO₂(sulfur dioxide), PM10 (particulate matter with an aerodynamic diameter <10 μm), and O₃(ozone) in ambient air were integrated into a synthetic air quality index. Socioeconomic status was measured at individual level (income, education, number of household members, occupation, and National Basic Livelihood status) and area level (neighborhood index). The neighborhood index was calculated in the finest administrative unit (municipality) by performing standardization and integration of municipality-level data of the following: number of families receiving National Basic Livelihood, proportion of people engaged in an elementary occupation, population density, and number of service industries. Each study participant was assigned a neighborhood index value of the municipality in which they reside. Six regression models were generated to analyze the relationship between socioeconomic status and overall air pollution. All models were adjusted with sex, age, and smoking status. Stratification was conducted by residency (urban/rural). Moran's I was calculated to identify spatial clusters, and adjusted regression analysis was conducted to account for spatial autocorrelation. Results showed that people with higher neighborhood index, people living with smaller number of family members, and people with no education lived in municipalities with better overall air quality. The association differed by residency in some cases, and consideration of spatial autocorrelation altered the association. This study gives strength to the idea that environmental discrimination exists in some socioeconomic groups in Korea, and that residency and spatial autocorrelation must be considered in order to fully understand environmental disparities. This is the first study that provides the possible evidence of the environmental injustice in Korea using air quality index. The findings suggested that air quality index was negatively correlated with several important socioeconomic status measured at either individual or area level. The main implication of this paper, therefore, is to provide another insight to environmental policy makers to consider environmental injustice problem into community intervention for resolving the public health problems by air pollution.

Microtremors study applying the SPAC method in Colima state, Mexico.

NASA Astrophysics Data System (ADS)

Vázquez Rosas, R.; Aguirre González, J.; Mijares Arellano, H.

2007-05-01

One of the main parts of seismic risk studies is to determine the site effect. This can be estimated by means of the microtremors measurements. From the H/V spectral ratio (Nakamura, 1989), the predominant period of the site can be estimated. Although the predominant period by itself can not represent the site effect in a wide range of frequencies and doesn't provide information of the stratigraphy. The SPAC method (Spatial Auto-Correlation Method, Aki 1957), on the other hand, is useful to estimate the stratigraphy of the site. It is based on the simultaneous recording of microtremors in several stations deployed in an instrumental array. Through the spatial autocorrelation coefficient computation, the Rayleigh wave dispersion curve can be cleared. Finally the stratigraphy model (thickness, S and P wave velocity, and density of each layer) is estimated by fitting the theoretical dispersion curve with the observed one. The theoretical dispersion curve is initially computed using a proposed model. That model is modified several times until the theoretical curve fit the observations. This method requires of a minimum of three stations where the microtremors are observed simultaneously in all the stations. We applied the SPAC method to six sites in Colima state, Mexico. Those sites are Santa Barbara, Cerro de Ortega, Tecoman, Manzanillo and two in Colima city. Totally 16 arrays were carried out using equilateral triangles with different apertures with a minimum of 5 m and a maximum of 60 m. For recording microtremors we used short period (5 seconds) velocity type vertical sensors connected to a K2 (Kinemetrics) acquisition system. We could estimate the velocities of the most superficial layers reaching different depths in each site. For Santa Bárbara site the exploration depth was about 30 m, for Tecoman 12 m, for Manzanillo 35 m, for Cerro de Ortega 68 m, and the deepest site exploration was obtained in Colima city with a depth of around 73 m. The S wave velocities fluctuate between 230 m/s and 420 m/s for the most superficial layer. It means that, in general, the most superficial layers are quite competent. The superficial layer with smaller S wave velocity was observed in Tecoman, while that of largest S wave velocity was observed in Cerro de Ortega. Our estimations are consistent with down-hole velocity records obtained in Santa Barbara by previous studies.

Complete chirp analysis of a gain-switched pulse using an interferometric two-photon absorption autocorrelation.

PubMed

Chin, Sang Hoon; Kim, Young Jae; Song, Ho Seong; Kim, Dug Young

2006-10-10

We propose a simple but powerful scheme for the complete analysis of the frequency chirp of a gain-switched optical pulse using a fringe-resolved interferometric two-photon absorption autocorrelator. A frequency chirp imposed on the gain-switched pulse from a laser diode was retrieved from both the intensity autocorrelation trace and the envelope of the second-harmonic interference fringe pattern. To verify the accuracy of the proposed phase retrieval method, we have performed an optical pulse compression experiment by using dispersion-compensating fibers with different lengths. We have obtained close agreement by less than a 1% error between the compressed pulse widths and numerically calculated pulse widths.

Practical guidance on characterizing availability in resource selection functions under a use-availability design

USGS Publications Warehouse

Northrup, Joseph M.; Hooten, Mevin B.; Anderson, Charles R.; Wittemyer, George

2013-01-01

Habitat selection is a fundamental aspect of animal ecology, the understanding of which is critical to management and conservation. Global positioning system data from animals allow fine-scale assessments of habitat selection and typically are analyzed in a use-availability framework, whereby animal locations are contrasted with random locations (the availability sample). Although most use-availability methods are in fact spatial point process models, they often are fit using logistic regression. This framework offers numerous methodological challenges, for which the literature provides little guidance. Specifically, the size and spatial extent of the availability sample influences coefficient estimates potentially causing interpretational bias. We examined the influence of availability on statistical inference through simulations and analysis of serially correlated mule deer GPS data. Bias in estimates arose from incorrectly assessing and sampling the spatial extent of availability. Spatial autocorrelation in covariates, which is common for landscape characteristics, exacerbated the error in availability sampling leading to increased bias. These results have strong implications for habitat selection analyses using GPS data, which are increasingly prevalent in the literature. We recommend researchers assess the sensitivity of their results to their availability sample and, where bias is likely, take care with interpretations and use cross validation to assess robustness.

Controls on drainage divide migration in the northern Sierras Pampeanas assessed through morphometric indicators

NASA Astrophysics Data System (ADS)

Seagren, E. G.; Schoenbohm, L. M.

2017-12-01

Drainage reorganization, primarily through progressive divide migration leading to discrete stream captures, is increasingly recognized as a common phenomenon during mountain-building events. This drainage rearrangement reflects complex interactions between tectonics, climate, and lithology, and can fundamentally change erosion and sedimentation patterns; therefore, determining the spatial extent and potential controls of divide migration is vital to understanding the topographic evolution of orogenic landscapes. Both geomorphic and morphometric evidence can be used to identify such drainage reorganization. The northern Sierras Pampeanas is an ideal location in which to study divide migration as limited glaciation and low out-of-channel erosion rates preserve evidence of reorganization. Additionally, several ranges in the region, such as Sierra de las Planchadas, exhibit geomorphic evidence of drainage rearrangement, including wind gaps and hairpin turns. Using ArcGIS, LSDTopoTools, and TopoToolbox, we conducted a systematic analysis of the spatial distribution of three morphometric indicators of divide migration: χ, Mx, and local headwater relief. Local `hotspots' undergoing drainage divide migration were identified using spatial autocorrelation and clustering methods - Gi* and Moran's I. Using spatial regression analysis, we assessed the potential controls of lithology, modern TRMM precipitation rates, and tectonics over divide migration. Preliminary results suggest broad westward migration of main drainage divides, following both the orographic precipitation gradient and regional slope.

On the reliability and limitations of the SPAC method with a directional wavefield

NASA Astrophysics Data System (ADS)

Luo, Song; Luo, Yinhe; Zhu, Lupei; Xu, Yixian

2016-03-01

The spatial autocorrelation (SPAC) method is one of the most efficient ways to extract phase velocities of surface waves from ambient seismic noise. Most studies apply the method based on the assumption that the wavefield of ambient noise is diffuse. However, the actual distribution of sources is neither diffuse nor stationary. In this study, we examined the reliability and limitations of the SPAC method with a directional wavefield. We calculated the SPAC coefficients and phase velocities from a directional wavefield for a four-layer model and characterized the limitations of the SPAC. We then applied the SPAC method to real data in Karamay, China. Our results show that, 1) the SPAC method can accurately measure surface wave phase velocities from a square array with a directional wavefield down to a wavelength of twice the shortest interstation distance; and 2) phase velocities obtained from real data by the SPAC method are stable and reliable, which demonstrates that this method can be applied to measure phase velocities in a square array with a directional wavefield.

A phase match based frequency estimation method for sinusoidal signals

NASA Astrophysics Data System (ADS)

Shen, Yan-Lin; Tu, Ya-Qing; Chen, Lin-Jun; Shen, Ting-Ao

2015-04-01

Accurate frequency estimation affects the ranging precision of linear frequency modulated continuous wave (LFMCW) radars significantly. To improve the ranging precision of LFMCW radars, a phase match based frequency estimation method is proposed. To obtain frequency estimation, linear prediction property, autocorrelation, and cross correlation of sinusoidal signals are utilized. The analysis of computational complex shows that the computational load of the proposed method is smaller than those of two-stage autocorrelation (TSA) and maximum likelihood. Simulations and field experiments are performed to validate the proposed method, and the results demonstrate the proposed method has better performance in terms of frequency estimation precision than methods of Pisarenko harmonic decomposition, modified covariance, and TSA, which contribute to improving the precision of LFMCW radars effectively.

Space-time description of dengue outbreaks in Cruzeiro, São Paulo, in 2006 and 2011.

PubMed

Carvalho, Renata Marzzano de; Nascimento, Luiz Fernando Costa

2014-01-01

to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of São Paulo (SP). an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran's I, month to month, and the Kernel estimator. in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran's I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation. we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.

Temporal and spatial variability in thalweg profiles of a gravel-bed river

USGS Publications Warehouse

Madej, Mary Ann

1999-01-01

This study used successive longitudinal thalweg profiles in gravel-bed rivers to monitor changes in bed topography following floods and associated large sediment inputs. Variations in channel bed elevations, distributions of residual water depths, percentage of channel length occupied by riffles, and a spatial autocorrelation coefficient (Moran's I) were used to quantify changes in morphological diversity and spatial structure in Redwood Creek basin, northwestern California. Bed topography in Redwood Creek and its major tributaries consists primarily of a series of pools and riffles. The size, frequency and spatial distribution of the pools and riffles have changed significantly during the past 20 years. Following large floods and high sediment input in Redwood Creek and its tributaries in 1975, variation in channel bed elevations was low and the percentage of the channel length occupied by riffles was high. Over the next 20 years, variation in bed elevations increased while the length of channel occupied by riffles decreased. An index [(standard deviation of residual water depth/bankfull depth) × 100] was developed to compare variations in bed elevation over a range of stream sizes, with a higher index being indicative of greater morphological diversity. Spatial autocorrelation in the bed elevation data was apparent at both fine and coarse scales in many of the thalweg profiles and the observed spatial pattern of bed elevations was found to be related to the dominant channel material and the time since disturbance. River reaches in which forced pools dominated, and in which large woody debris and bed particles could not be easily mobilized, exhibited a random distribution of bed elevations. In contrast, in reaches where alternate bars dominated, and both wood and gravel were readily transported, regularly spaced bed topography developed at a spacing that increased with time since disturbance. This pattern of regularly spaced bed features was reversed following a 12-year flood when bed elevations became more randomly arranged.

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

NASA Astrophysics Data System (ADS)

Trásy, Balázs; Garamhegyi, Tamás; Laczkó-Dobos, Péter; Kovács, József; Hatvani, István Gábor

2018-04-01

The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in 2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

Spatial Variability of Plant Available Water, Soil Organic Carbon, and Microbial Biomass under Divergent Land Uses: A Comparison among Regression-Kriging, Cokriging, and Regression-Cokriging

NASA Astrophysics Data System (ADS)

Kiani, M.; Hernandez Ramirez, G.; Quideau, S.

2016-12-01

Improved knowledge about the spatial variability of plant available water (PAW), soil organic carbon (SOC), and microbial biomass carbon (MBC) as affected by land-use systems can underpin the identification and inventory of beneficial ecosystem good and services in both agricultural and wild lands. Little research has been done that addresses the spatial patterns of PAW, SOC, and MBC under different land use types at a field scale. Therefore, we collected 56 soil samples (5-10 cm depth increment), using a nested cyclic sampling design within both a native grassland (NG) site and an irrigated cultivated (IC) site located near Brooks, Alberta. Using classical statistical and geostatistical methods, we characterized the spatial heterogeneities of PAW, SOC, and MBC under NG and IC using several geostatistical methods such as ordinary kriging (OK), regression-kriging (RK), cokriging (COK), and regression-cokriging (RCOK). Converting the native grassland to irrigated cultivated land altered soil pore distribution by reducing macroporosity which led to lower saturated water content and half hydraulic conductivity in IC compared to NG. This conversion also decreased the relative abundance of gram-negative bacteria, while increasing both the proportion of gram-positive bacteria and MBC concentration. At both studied sites, the best fitted spatial model was Gaussian based on lower RSS and higher R2 as criteria. The IC had stronger degree of spatial dependence and longer range of spatial auto-correlation revealing a homogenization of the spatial variability of soil properties as a result of intensive, recurrent agricultural activities. Comparison of OK, RK, COK, and RCOK approaches indicated that cokriging method had the best performance demonstrating a profound improvement in the accuracy of spatial estimations of PAW, SOC, and MBC. It seems that the combination of terrain covariates such as elevation and depth-to-water with kriging techniques offers more capability for incorporating explicit ancillary information in predictive soil mapping. Overall, identification of spatial patterns of soil properties in agricultural lands gives a bird's eye view to land owners to implement and improve management practices which lead to more sustainable production.

Atomistic observation and simulation analysis of spatio-temporal fluctuations during radiation-induced amorphization.

PubMed

Watanabe, Seiichi; Hoshino, Misaki; Koike, Takuto; Suda, Takanori; Ohnuki, Soumei; Takahashi, Heishichirou; Lam, Nighi Q

2003-01-01

We performed a dynamical-atomistic study of radiation-induced amorphization in the NiTi intermetallic compound using in situ high-resolution high-voltage electron microscopy and molecular dynamics simulations in connection with image simulation. Spatio-temporal fluctuations as non-equilibrium fluctuations in an energy-dissipative system, due to transient atom-cluster formation during amorphization, were revealed by the present spatial autocorrelation analysis.

Time-to-space mapping of femtosecond pulses.

PubMed

Nuss, M C; Li, M; Chiu, T H; Weiner, A M; Partovi, A

1994-05-01

We report time-to-space mapping of femtosecond light pulses in a temporal holography setup. By reading out a temporal hologram of a short optical pulse with a continuous-wave diode laser, we accurately convert temporal pulse-shape information into a spatial pattern that can be viewed with a camera. We demonstrate real-time acquisition of electric-field autocorrelation and cross correlation of femtosecond pulses with this technique.

Spatial distribution and socioeconomic context of tuberculosis in Rio de Janeiro, Brazil

PubMed Central

Pereira, Alessandra Gonçalves Lisbôa; Medronho, Roberto de Andrade; Escosteguy, Claudia Caminha; Valencia, Luis Iván Ortiz; Magalhães, Mônica de Avelar Figueiredo Mafra

2015-01-01

OBJECTIVE To analyze the spatial distribution of risk for tuberculosis and its socioeconomic determinants in the city of Rio de Janeiro, Brazil. METHODS An ecological study on the association between the mean incidence rate of tuberculosis from 2004 to 2006 and socioeconomic indicators of the Censo Demográfico (Demographic Census) of 2000. The unit of analysis was the home district registered in the Sistema de Informação de Agravos de Notificação (Notifiable Diseases Information System) of Rio de Janeiro, Southeastern Brazil. The rates were standardized by sex and age group, and smoothed by the empirical Bayes method. Spatial autocorrelation was evaluated by Moran’s I. Multiple linear regression models were studied and the appropriateness of incorporating the spatial component in modeling was evaluated. RESULTS We observed a higher risk of the disease in some neighborhoods of the port and north regions, as well as a high incidence in the slums of Rocinha and Vidigal, in the south region, and Cidade de Deus, in the west. The final model identified a positive association for the variables: percentage of permanent private households in which the head of the house earns three to five minimum wages; percentage of individual residents in the neighborhood; and percentage of people living in homes with more than two people per bedroom. CONCLUSIONS The spatial analysis identified areas of risk of tuberculosis incidence in the neighborhoods of the city of Rio de Janeiro and also found spatial dependence for the incidence of tuberculosis and some socioeconomic variables. However, the inclusion of the space component in the final model was not required during the modeling process. PMID:26270014

Spatial distribution and socioeconomic context of tuberculosis in Rio de Janeiro, Brazil.

PubMed

Pereira, Alessandra Gonçalves Lisbôa; Medronho, Roberto de Andrade; Escosteguy, Claudia Caminha; Valencia, Luis Iván Ortiz; Magalhães, Mônica de Avelar Figueiredo Mafra

2015-01-01

OBJECTIVE To analyze the spatial distribution of risk for tuberculosis and its socioeconomic determinants in the city of Rio de Janeiro, Brazil. METHODS An ecological study on the association between the mean incidence rate of tuberculosis from 2004 to 2006 and socioeconomic indicators of the Censo Demográfico (Demographic Census) of 2000. The unit of analysis was the home district registered in the Sistema de Informação de Agravos de Notificação (Notifiable Diseases Information System) of Rio de Janeiro, Southeastern Brazil. The rates were standardized by sex and age group, and smoothed by the empirical Bayes method. Spatial autocorrelation was evaluated by Moran's I. Multiple linear regression models were studied and the appropriateness of incorporating the spatial component in modeling was evaluated. RESULTS We observed a higher risk of the disease in some neighborhoods of the port and north regions, as well as a high incidence in the slums of Rocinha and Vidigal, in the south region, and Cidade de Deus, in the west. The final model identified a positive association for the variables: percentage of permanent private households in which the head of the house earns three to five minimum wages; percentage of individual residents in the neighborhood; and percentage of people living in homes with more than two people per bedroom. CONCLUSIONS The spatial analysis identified areas of risk of tuberculosis incidence in the neighborhoods of the city of Rio de Janeiro and also found spatial dependence for the incidence of tuberculosis and some socioeconomic variables. However, the inclusion of the space component in the final model was not required during the modeling process.

The use of copula functions for predictive analysis of correlations between extreme storm tides

NASA Astrophysics Data System (ADS)

Domino, Krzysztof; Błachowicz, Tomasz; Ciupak, Maurycy

2014-11-01

In this paper we present a method used in quantitative description of weakly predictable hydrological, extreme events at inland sea. Investigations for correlations between variations of individual measuring points, employing combined statistical methods, were carried out. As a main tool for this analysis we used a two-dimensional copula function sensitive for correlated extreme effects. Additionally, a new proposed methodology, based on Detrended Fluctuations Analysis (DFA) and Anomalous Diffusion (AD), was used for the prediction of negative and positive auto-correlations and associated optimum choice of copula functions. As a practical example we analysed maximum storm tides data recorded at five spatially separated places at the Baltic Sea. For the analysis we used Gumbel, Clayton, and Frank copula functions and introduced the reversed Clayton copula. The application of our research model is associated with modelling the risk of high storm tides and possible storm flooding.

Space-Based Identification of Archaeological Illegal Excavations and a New Automatic Method for Looting Feature Extraction in Desert Areas

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Masini, Nicola

2018-06-01

The identification and quantification of disturbance of archaeological sites has been generally approached by visual inspection of optical aerial or satellite pictures. In this paper, we briefly summarize the state of the art of the traditionally satellite-based approaches for looting identification and propose a new automatic method for archaeological looting feature extraction approach (ALFEA). It is based on three steps: the enhancement using spatial autocorrelation, unsupervised classification, and segmentation. ALFEA has been applied to Google Earth images of two test areas, selected in desert environs in Syria (Dura Europos), and in Peru (Cahuachi-Nasca). The reliability of ALFEA was assessed through field surveys in Peru and visual inspection for the Syrian case study. Results from the evaluation procedure showed satisfactory performance from both of the two analysed test cases with a rate of success higher than 90%.

Emergence, spread, persistence and fade-out of sylvatic plague in Kazakhstan

PubMed Central

Heier, Lise; Storvik, Geir O.; Davis, Stephen A.; Viljugrein, Hildegunn; Ageyev, Vladimir S.; Klassovskaya, Evgeniya; Stenseth, Nils Chr.

2011-01-01

Predicting the dynamics of zoonoses in wildlife is important not only for prevention of transmission to humans, but also for improving the general understanding of epidemiological processes. A large dataset on sylvatic plague in the Pre-Balkhash area of Kazakhstan (collected for surveillance purposes) provides a rare opportunity for detailed statistical modelling of an infectious disease. Previous work using these data has revealed a host abundance threshold for epizootics, and climatic influences on plague prevalence. Here, we present a model describing the local space–time dynamics of the disease at a spatial scale of 20 × 20 km2 and a biannual temporal scale, distinguishing between invasion and persistence events. We used a Bayesian imputation method to account for uncertainties resulting from poor data in explanatory variables and response variables. Spatial autocorrelation in the data was accounted for in imputations and analyses through random effects. The results show (i) a clear effect of spatial transmission, (ii) a high probability of persistence compared with invasion, and (iii) a stronger influence of rodent abundance on invasion than on persistence. In particular, there was a substantial probability of persistence also at low host abundance. PMID:21345866

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

Davies, R.

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles lessmore » than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.« less

Demonstration of a real-time interferometer as a bunch-length monitor in a high-current electron beam accelerator.

PubMed

Thangaraj, J; Andonian, G; Thurman-Keup, R; Ruan, J; Johnson, A S; Lumpkin, A; Santucci, J; Maxwell, T; Murokh, A; Ruelas, M; Ovodenko, A

2012-04-01

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps (~0.24 mm) and 1.5 ps (~0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera. The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches. © 2012 American Institute of Physics

Correlation range in a supercooled liquid via Green-Kubo expression for viscosity, local atomic stresses, and MD simulations

NASA Astrophysics Data System (ADS)

Levashov, Valentin A.; Egami, Takeshi; Morris, James R.

2009-03-01

We present a new approach to the issue of correlation range in supercooled liquids based on Green-Kubo expression for viscosity. The integrand of this expression is the average stress-stress autocorrelation function. This correlation function could be rewritten in terms of correlations among local atomic stresses at different times and distances. The features of the autocorrelation function decay with time depend on temperature and correlation range. Through this approach we can study the development of spatial correlation with time, thus directly addressing the question of dynamic heterogeneity. We performed MD simulations on a single component system of particles interacting through short range pair potential. Our results indicate that even above the crossover temperature correlations extend well beyond the nearest neighbors. Surprisingly we found that the system size effects exist even on relatively large systems. We also address the role of diffusion in decay of stress-stress correlation function.

Statistical spatial properties of speckle patterns generated by multiple laser beams

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

Le Cain, A.; Sajer, J. M.; Riazuelo, G.

2011-08-15

This paper investigates hot spot characteristics generated by the superposition of multiple laser beams. First, properties of speckle statistics are studied in the context of only one laser beam by computing the autocorrelation function. The case of multiple laser beams is then considered. In certain conditions, it is shown that speckles have an ellipsoidal shape. Analytical expressions of hot spot radii generated by multiple laser beams are derived and compared to numerical estimates made from the autocorrelation function. They are also compared to numerical simulations performed within the paraxial approximation. Excellent agreement is found for the speckle width as wellmore » as for the speckle length. Application to the speckle patterns generated in the Laser MegaJoule configuration in the zone where all the beams overlap is presented. Influence of polarization on the size of the speckles as well as on their abundance is studied.« less

Demonstration of a real-time interferometer as a bunch-lenght monitor in a high-current electron beam accelerator

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

Thangaraj, J.; Thurman-Keup, R.; Ruan, J.

2012-03-01

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps (-0.24 mm) and 1.5 ps (-0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera.more » The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches.« less

Demonstration of a real-time interferometer as a bunch-length monitor in a high-current electron beam accelerator

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

Thangaraj, J.; Thurman-Keup, R.; Ruan, J.

2012-04-15

A real-time interferometer (RTI) has been developed to monitor the bunch length of an electron beam in an accelerator. The RTI employs spatial autocorrelation, reflective optics, and a fast response pyro-detector array to obtain a real-time autocorrelation trace of the coherent radiation from an electron beam thus providing the possibility of online bunch-length diagnostics. A complete RTI system has been commissioned at the A0 photoinjector facility to measure sub-mm bunches at 13 MeV. Bunch length variation (FWHM) between 0.8 ps ({approx}0.24 mm) and 1.5 ps ({approx}0.45 mm) has been measured and compared with a Martin-Puplett interferometer and a streak camera.more » The comparisons show that RTI is a viable, complementary bunch length diagnostic for sub-mm electron bunches.« less

A generalization of random matrix theory and its application to statistical physics.

PubMed

Wang, Duan; Zhang, Xin; Horvatic, Davor; Podobnik, Boris; Eugene Stanley, H

2017-02-01

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

Spatial variation in nutrient and water color effects on lake chlorophyll at macroscales

USGS Publications Warehouse

Fergus, C. Emi; Finley, Andrew O.; Soranno, Patricia A.; Wagner, Tyler

2016-01-01

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals.

Spatial Variation in Nutrient and Water Color Effects on Lake Chlorophyll at Macroscales

PubMed Central

Finley, Andrew O.; Soranno, Patricia A.; Wagner, Tyler

2016-01-01

The nutrient-water color paradigm is a framework to characterize lake trophic status by relating lake primary productivity to both nutrients and water color, the colored component of dissolved organic carbon. Total phosphorus (TP), a limiting nutrient, and water color, a strong light attenuator, influence lake chlorophyll a concentrations (CHL). But, these relationships have been shown in previous studies to be highly variable, which may be related to differences in lake and catchment geomorphology, the forms of nutrients and carbon entering the system, and lake community composition. Because many of these factors vary across space it is likely that lake nutrient and water color relationships with CHL exhibit spatial autocorrelation, such that lakes near one another have similar relationships compared to lakes further away. Including this spatial dependency in models may improve CHL predictions and clarify how well the nutrient-water color paradigm applies to lakes distributed across diverse landscape settings. However, few studies have explicitly examined spatial heterogeneity in the effects of TP and water color together on lake CHL. In this study, we examined spatial variation in TP and water color relationships with CHL in over 800 north temperate lakes using spatially-varying coefficient models (SVC), a robust statistical method that applies a Bayesian framework to explore space-varying and scale-dependent relationships. We found that TP and water color relationships were spatially autocorrelated and that allowing for these relationships to vary by individual lakes over space improved the model fit and predictive performance as compared to models that did not vary over space. The magnitudes of TP effects on CHL differed across lakes such that a 1 μg/L increase in TP resulted in increased CHL ranging from 2–24 μg/L across lake locations. Water color was not related to CHL for the majority of lakes, but there were some locations where water color had a positive effect such that a unit increase in water color resulted in a 2 μg/L increase in CHL and other locations where it had a negative effect such that a unit increase in water color resulted in a 2 μg/L decrease in CHL. In addition, the spatial scales that captured variation in TP and water color effects were different for our study lakes. Variation in TP–CHL relationships was observed at intermediate distances (~20 km) compared to variation in water color–CHL relationships that was observed at regional distances (~200 km). These results demonstrate that there are lake-to-lake differences in the effects of TP and water color on lake CHL and that this variation is spatially structured. Quantifying spatial structure in these relationships furthers our understanding of the variability in these relationships at macroscales and would improve model prediction of chlorophyll a to better meet lake management goals. PMID:27736962

Method of optical coherence tomography with parallel depth-resolved signal reception and fibre-optic phase modulators

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

Morozov, A N; Turchin, I V

2013-12-31

The method of optical coherence tomography with the scheme of parallel reception of the interference signal (P-OCT) is developed on the basis of spatial paralleling of the reference wave by means of a phase diffraction grating producing the appropriate time delay in the Mach–Zehnder interferometer. The absence of mechanical variation of the optical path difference in the interferometer essentially reduces the time required for 2D imaging of the object internal structure, as compared to the classical OCT that uses the time-domain method of the image construction, the sensitivity and the dynamic range being comparable in both approaches. For the resultingmore » field of the interfering object and reference waves an analytical expression is derived that allows the calculation of the autocorrelation function in the plane of photodetectors. For the first time a method of linear phase modulation by 2π is proposed for P-OCT systems, which allows the use of compact high-frequency (a few hundred kHz) piezoelectric cell-based modulators. For the demonstration of the P-OCT method an experimental setup was created, using which the images of the inner structure of biological objects at the depth up to 1 mm with the axial spatial resolution of 12 μm were obtained. (optical coherence tomography)« less

Perception and psychological evaluation for visual and auditory environment based on the correlation mechanisms

NASA Astrophysics Data System (ADS)

Fujii, Kenji

2002-06-01

In this dissertation, the correlation mechanism in modeling the process in the visual perception is introduced. It has been well described that the correlation mechanism is effective for describing subjective attributes in auditory perception. The main result is that it is possible to apply the correlation mechanism to the process in temporal vision and spatial vision, as well as in audition. (1) The psychophysical experiment was performed on subjective flicker rates for complex waveforms. A remarkable result is that the phenomenon of missing fundamental is found in temporal vision as analogous to the auditory pitch perception. This implies the existence of correlation mechanism in visual system. (2) For spatial vision, the autocorrelation analysis provides useful measures for describing three primary perceptual properties of visual texture: contrast, coarseness, and regularity. Another experiment showed that the degree of regularity is a salient cue for texture preference judgment. (3) In addition, the autocorrelation function (ACF) and inter-aural cross-correlation function (IACF) were applied for analysis of the temporal and spatial properties of environmental noise. It was confirmed that the acoustical properties of aircraft noise and traffic noise are well described. These analyses provided useful parameters extracted from the ACF and IACF in assessing the subjective annoyance for noise. Thesis advisor: Yoichi Ando Copies of this thesis written in English can be obtained from Junko Atagi, 6813 Mosonou, Saijo-cho, Higashi-Hiroshima 739-0024, Japan. E-mail address: atagi\\@urban.ne.jp.

Spatio-temporal pattern of sylvatic rabies in the Sultanate of Oman, 2006-2010.

PubMed

Hussain, Muhammad Hammad; Ward, Michael P; Body, Mohammed; Al-Rawahi, Abdulmajeed; Wadir, Ali Awlad; Al-Habsi, Saif; Saqib, Muhammad; Ahmed, Mohammed Sayed; Almaawali, Mahir Gharib

2013-07-01

Rabies was first reported in the Sultanate of Oman is 1990. We analysed passive surveillance data (444 samples) collected and reported between 2006 and 2010. During this period, between 45 and 75% of samples submitted from suspect animals were subsequently confirmed (fluorescent antibody test, histopathology and reverse transcription PCR) as rabies cases. Overall, 63% of submitted samples were confirmed as rabies cases. The spatial distribution of species-specific cases were similar (centred in north-central Oman with a northeast-southwest distribution), although fox cases had a wider distribution and an east-west orientation. Clustering of cases was detected using interpolation, local spatial autocorrelation and scan statistical analysis. Several local government areas (wilayats) in north-central Oman were identified where higher than expected numbers of laboratory-confirmed rabies cases were reported. For fox rabies, more clusters (local spatial autocorrelation analysis) and a larger clustered area (scan statistical analysis) were detected. In Oman, monthly reports of fox rabies cases were highly correlated (rSP>0.5) with reports of camel, cattle, sheep and goat rabies. The best-fitting ARIMA model included a seasonality component. Fox rabies cases reported 6 months previously best explained rabies reported cases in other animal species. Despite likely reporting bias, results suggest that rabies exists as a sylvatic cycle of transmission in Oman and an opportunity still exists to prevent establishment of dog-mediated rabies. Copyright © 2013 Elsevier B.V. All rights reserved.

Using deconvolution to improve the metrological performance of the grid method

NASA Astrophysics Data System (ADS)

Grédiac, Michel; Sur, Frédéric; Badulescu, Claudiu; Mathias, Jean-Denis

2013-06-01

The use of various deconvolution techniques to enhance strain maps obtained with the grid method is addressed in this study. Since phase derivative maps obtained with the grid method can be approximated by their actual counterparts convolved by the envelope of the kernel used to extract phases and phase derivatives, non-blind restoration techniques can be used to perform deconvolution. Six deconvolution techniques are presented and employed to restore a synthetic phase derivative map, namely direct deconvolution, regularized deconvolution, the Richardson-Lucy algorithm and Wiener filtering, the last two with two variants concerning their practical implementations. Obtained results show that the noise that corrupts the grid images must be thoroughly taken into account to limit its effect on the deconvolved strain maps. The difficulty here is that the noise on the grid image yields a spatially correlated noise on the strain maps. In particular, numerical experiments on synthetic data show that direct and regularized deconvolutions are unstable when noisy data are processed. The same remark holds when Wiener filtering is employed without taking into account noise autocorrelation. On the other hand, the Richardson-Lucy algorithm and Wiener filtering with noise autocorrelation provide deconvolved maps where the impact of noise remains controlled within a certain limit. It is also observed that the last technique outperforms the Richardson-Lucy algorithm. Two short examples of actual strain fields restoration are finally shown. They deal with asphalt and shape memory alloy specimens. The benefits and limitations of deconvolution are presented and discussed in these two cases. The main conclusion is that strain maps are correctly deconvolved when the signal-to-noise ratio is high and that actual noise in the actual strain maps must be more specifically characterized than in the current study to address higher noise levels with Wiener filtering.

Factors Driving Potential Ammonia Oxidation in Canadian Arctic Ecosystems: Does Spatial Scale Matter?

PubMed Central

Banerjee, Samiran

2012-01-01

Ammonia oxidation is a major process in nitrogen cycling, and it plays a key role in nitrogen limited soil ecosystems such as those in the arctic. Although mm-scale spatial dependency of ammonia oxidizers has been investigated, little is known about the field-scale spatial dependency of aerobic ammonia oxidation processes and ammonia-oxidizing archaeal and bacterial communities, particularly in arctic soils. The purpose of this study was to explore the drivers of ammonia oxidation at the field scale in cryosols (soils with permafrost within 1 m of the surface). We measured aerobic ammonia oxidation potential (both autotrophic and heterotrophic) and functional gene abundance (bacterial amoA and archaeal amoA) in 279 soil samples collected from three arctic ecosystems. The variability associated with quantifying genes was substantially less than the spatial variability observed in these soils, suggesting that molecular methods can be used reliably evaluate spatial dependency in arctic ecosystems. Ammonia-oxidizing archaeal and bacterial communities and aerobic ammonia oxidation were spatially autocorrelated. Gene abundances were spatially structured within 4 m, whereas biochemical processes were structured within 40 m. Ammonia oxidation was driven at small scales (<1m) by moisture and total organic carbon, whereas gene abundance and other edaphic factors drove ammonia oxidation at medium (1 to 10 m) and large (10 to 100 m) scales. In these arctic soils heterotrophs contributed between 29 and 47% of total ammonia oxidation potential. The spatial scale for aerobic ammonia oxidation genes differed from potential ammonia oxidation, suggesting that in arctic ecosystems edaphic, rather than genetic, factors are an important control on ammonia oxidation. PMID:22081570

Bayesian image reconstruction - The pixon and optimal image modeling

NASA Technical Reports Server (NTRS)

Pina, R. K.; Puetter, R. C.

1993-01-01

In this paper we describe the optimal image model, maximum residual likelihood method (OptMRL) for image reconstruction. OptMRL is a Bayesian image reconstruction technique for removing point-spread function blurring. OptMRL uses both a goodness-of-fit criterion (GOF) and an 'image prior', i.e., a function which quantifies the a priori probability of the image. Unlike standard maximum entropy methods, which typically reconstruct the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the image. We show how an optimal basis for image representation can be selected and in doing so, develop the concept of the 'pixon' which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness-of-fit criterion, OptMRL uses the maximum residual likelihood probability distribution introduced previously by Pina and Puetter (1992). This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.

Accounting for and predicting the influence of spatial autocorrelation in water quality modeling

NASA Astrophysics Data System (ADS)

Miralha, L.; Kim, D.

2017-12-01

Although many studies have attempted to investigate the spatial trends of water quality, more attention is yet to be paid to the consequences of considering and ignoring the spatial autocorrelation (SAC) that exists in water quality parameters. Several studies have mentioned the importance of accounting for SAC in water quality modeling, as well as the differences in outcomes between models that account for and ignore SAC. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC inherently possessed by a response variable (i.e., water quality parameter) influences the outcomes of spatial modeling. We evaluated whether the level of inherent SAC is associated with changes in R-Squared, Akaike Information Criterion (AIC), and residual SAC (rSAC), after accounting for SAC during modeling procedure. The main objective was to analyze if water quality parameters with higher Moran's I values (inherent SAC measure) undergo a greater increase in R² and a greater reduction in both AIC and rSAC. We compared a non-spatial model (OLS) to two spatial regression approaches (spatial lag and error models). Predictor variables were the principal components of topographic (elevation and slope), land cover, and hydrological soil group variables. We acquired these data from federal online sources (e.g. USGS). Ten watersheds were selected, each in a different state of the USA. Results revealed that water quality parameters with higher inherent SAC showed substantial increase in R² and decrease in rSAC after performing spatial regressions. However, AIC values did not show significant changes. Overall, the higher the level of inherent SAC in water quality variables, the greater improvement of model performance. This indicates a linear and direct relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. Therefore, our study suggests that the inherent level of SAC in response variables can predict improvements in models even before performing spatial regression approaches. We also recognize the constraints of this research and suggest that further studies focus on better ways of defining spatial neighborhoods, considering the differences among stations set in tributaries near to each other and in upstream areas.

Probabilistic density function method for nonlinear dynamical systems driven by colored noise.

PubMed

Barajas-Solano, David A; Tartakovsky, Alexandre M

2016-05-01

We present a probability density function (PDF) method for a system of nonlinear stochastic ordinary differential equations driven by colored noise. The method provides an integrodifferential equation for the temporal evolution of the joint PDF of the system's state, which we close by means of a modified large-eddy-diffusivity (LED) closure. In contrast to the classical LED closure, the proposed closure accounts for advective transport of the PDF in the approximate temporal deconvolution of the integrodifferential equation. In addition, we introduce the generalized local linearization approximation for deriving a computable PDF equation in the form of a second-order partial differential equation. We demonstrate that the proposed closure and localization accurately describe the dynamics of the PDF in phase space for systems driven by noise with arbitrary autocorrelation time. We apply the proposed PDF method to analyze a set of Kramers equations driven by exponentially autocorrelated Gaussian colored noise to study nonlinear oscillators and the dynamics and stability of a power grid. Numerical experiments show the PDF method is accurate when the noise autocorrelation time is either much shorter or longer than the system's relaxation time, while the accuracy decreases as the ratio of the two timescales approaches unity. Similarly, the PDF method accuracy decreases with increasing standard deviation of the noise.

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

NASA Astrophysics Data System (ADS)

Ha, Jong M.; Youn, Byeng D.; Oh, Hyunseok; Han, Bongtae; Jung, Yoongho; Park, Jungho

2016-03-01

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

Significant demographic and fine-scale genetic structure in expanding and senescing populations of the terrestrial orchid Cymbidium goeringii (Orchidaceae).

PubMed

Chung, Mi Yoon; Nason, John D; Chung, Myong Gi

2011-12-01

Fine-scale genetic structure (FSGS) in plants is influenced by variation in spatial and temporal demographic processes. To determine how demographic structure and FSGS change with stages of population succession, we studied replicate expanding and senescing populations of the Asian terrestrial orchid Cymbidium goeringii. We used spatial autocorrelation methods (O-ring and kinship statistics) to quantify spatial demographic structure and FSGS in two expanding and two senescing populations, also measuring genetic diversity and inbreeding in each. All populations exhibited significant aggregation of individuals and FSGS at short spatial scales. In expanding populations, this finding was associated with high recruitment rates, suggesting restricted seed dispersal. In senescing populations, recruitment was minimal, suggesting alternative mechanisms of aggregation, perhaps including spatial associations with mycorrhizal fungi. All populations had significant evidence of genetic bottlenecks, and inbreeding levels were consistently high. Our results indicate that different successional stages can generate similar patterns of spatial demographic and genetic structure, but as a consequence of different processes. These results contrast with the only other study of senescence effects on population genetic structure in an herbaceous perennial, which found little to no FSGS in senescing populations. With the exception of populations subject to mass collection by orchid sellers, significant FSGS is characteristic of the 16 terrestrial orchid species examined to date. From a conservation perspective, this result suggests that inference of orchid population history will benefit from analyses of both FSGS and demographic structure in combination with other ecological field data.

A novel approach for introducing cloud spatial structure into cloud radiative transfer parameterizations

NASA Astrophysics Data System (ADS)

Huang, Dong; Liu, Yangang

2014-12-01

Subgrid-scale variability is one of the main reasons why parameterizations are needed in large-scale models. Although some parameterizations started to address the issue of subgrid variability by introducing a subgrid probability distribution function for relevant quantities, the spatial structure has been typically ignored and thus the subgrid-scale interactions cannot be accounted for physically. Here we present a new statistical-physics-like approach whereby the spatial autocorrelation function can be used to physically capture the net effects of subgrid cloud interaction with radiation. The new approach is able to faithfully reproduce the Monte Carlo 3D simulation results with several orders less computational cost, allowing for more realistic representation of cloud radiation interactions in large-scale models.

Housing price prediction: parametric versus semi-parametric spatial hedonic models

NASA Astrophysics Data System (ADS)

Montero, José-María; Mínguez, Román; Fernández-Avilés, Gema

2018-01-01

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

NASA Astrophysics Data System (ADS)

Popkov, Artem

2016-01-01

The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase- and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

Declines revisited: Long-term recovery and spatial population dynamics oftailed frog larvae after wildfire

USGS Publications Warehouse

Hossack, Blake R.; Honeycutt, Richard

2017-01-01

Drought has fueled an increased frequency and severity of large wildfires in many ecosystems. Despite an increase in research on wildfire effects on vertebrates, the vast majority of it has focused on short-term (< 5 years) effects and there is still little information on the time scale of population recovery for species that decline in abundance after fire. In 2003, a large wildfire in Montana (USA) burned the watersheds of four of eight streams that we sampled for larval Rocky Mountain tailed frogs (Ascaphus montanus) in 2001. Surveys during 2004–2005 revealed reduced abundance of larvae in burned streams relative to unburned streams, with greater declines associated with increased fire extent. Rocky Mountain tailed frogs have low vagility and have several unusual life-history traits that could slow population recovery, including an extended larval period (4 years), delayed sexual maturity (6–8 years), and low fecundity (< 50 eggs/year). To determine if abundance remained depressed since the 2003 wildfire, we repeated surveys during 2014–2015 and found relative abundance of larvae in burned and unburned streams had nearly converged to pre-fire conditions within two generations. The negative effects of burn extent on larval abundance weakened> 58% within 12 years after the fire. We also found moderate synchrony among populations in unburned streams and negative spatial autocorrelation among populations in burned streams. We suspect negative spatial autocorrelation among spatially-clustered burned streams reflected increased post-fire patchiness in resources and different rates of local recovery. Our results add to a growing body of work that suggests populations in intact ecosystems tend to be resilient to habitat changes caused by wildfire. Our results also provide important insights into recovery times of populations that have been negatively affected by severe wildfire.

Effects of scale and logging on landscape structure in a forest mosaic.

PubMed

Leimgruber, P; McShea, W J; Schnell, G D

2002-03-01

Landscape structure in a forest mosaic changes with spatial scale (i.e. spatial extent) and thresholds may occur where structure changes markedly. Forest management alters landscape structure and may affect the intensity and location of thresholds. Our purpose was to examine landscape structure at different scales to determine thresholds where landscape structure changes markedly in managed forest mosaics of the Appalachian Mountains in the eastern United States. We also investigated how logging influences landscape structure and whether these management activities change threshold values. Using threshold and autocorrelation analyses, we found that thresholds in landscape indices exist at 400, 500, and 800 m intervals from the outer edge of management units in our study region. For landscape indices that consider all landcover categories, such as dominance and contagion, landscape structure and thresholds did not change after logging occurred. Measurements for these overall landscape indices were strongly influenced by midsuccessional deciduous forest, the most common landcover category in the landscape. When restricting analyses for mean patch size and percent cover to individual forest types, thresholds for early-successional forests changed after logging. However, logging changed the landscape structure at small spatial scale, but did not alter the structure of the entire forest mosaic. Previous forest management may already have increased the heterogeneity of the landscape beyond the point where additional small cuts alter the overall structure of the forest. Because measurements for landscape indices yield very different results at different spatial scales, it is important first to identify thresholds in order to determine the appropriate scales for landscape ecological studies. We found that threshold and autocorrelation analyses were simple but powerful tools for the detection of appropriate scales in the managed forest mosaic under study.

Geographically weighted regression based methods for merging satellite and gauge precipitation

NASA Astrophysics Data System (ADS)

Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

2018-03-01

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

Monte Carlo errors with less errors

NASA Astrophysics Data System (ADS)

Wolff, Ulli; Alpha Collaboration

2004-01-01

We explain in detail how to estimate mean values and assess statistical errors for arbitrary functions of elementary observables in Monte Carlo simulations. The method is to estimate and sum the relevant autocorrelation functions, which is argued to produce more certain error estimates than binning techniques and hence to help toward a better exploitation of expensive simulations. An effective integrated autocorrelation time is computed which is suitable to benchmark efficiencies of simulation algorithms with regard to specific observables of interest. A Matlab code is offered for download that implements the method. It can also combine independent runs (replica) allowing to judge their consistency.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

Efficiencies of joint non-local update moves in Monte Carlo simulations of coarse-grained polymers

NASA Astrophysics Data System (ADS)

Austin, Kieran S.; Marenz, Martin; Janke, Wolfhard

2018-03-01

In this study four update methods are compared in their performance in a Monte Carlo simulation of polymers in continuum space. The efficiencies of the update methods and combinations thereof are compared with the aid of the autocorrelation time with a fixed (optimal) acceptance ratio. Results are obtained for polymer lengths N = 14, 28 and 42 and temperatures below, at and above the collapse transition. In terms of autocorrelation, the optimal acceptance ratio is approximately 0.4. Furthermore, an overview of the step sizes of the update methods that correspond to this optimal acceptance ratio is given. This shall serve as a guide for future studies that rely on efficient computer simulations.

Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence

NASA Technical Reports Server (NTRS)

Mark, W. D.

1981-01-01

A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.

The calculation of transport properties in quantum liquids using the maximum entropy numerical analytic continuation method: Application to liquid para-hydrogen

PubMed Central

Rabani, Eran; Reichman, David R.; Krilov, Goran; Berne, Bruce J.

2002-01-01

We present a method based on augmenting an exact relation between a frequency-dependent diffusion constant and the imaginary time velocity autocorrelation function, combined with the maximum entropy numerical analytic continuation approach to study transport properties in quantum liquids. The method is applied to the case of liquid para-hydrogen at two thermodynamic state points: a liquid near the triple point and a high-temperature liquid. Good agreement for the self-diffusion constant and for the real-time velocity autocorrelation function is obtained in comparison to experimental measurements and other theoretical predictions. Improvement of the methodology and future applications are discussed. PMID:11830656

Double-resolution electron holography with simple Fourier transform of fringe-shifted holograms.

PubMed

Volkov, V V; Han, M G; Zhu, Y

2013-11-01

We propose a fringe-shifting holographic method with an appropriate image wave recovery algorithm leading to exact solution of holographic equations. With this new method the complex object image wave recovered from holograms appears to have much less traditional artifacts caused by the autocorrelation band present practically in all Fourier transformed holograms. The new analytical solutions make possible a double-resolution electron holography free from autocorrelation band artifacts and thus push the limits for phase resolution. The new image wave recovery algorithm uses a popular Fourier solution of the side band-pass filter technique, while the fringe-shifting holographic method is simple to implement in practice. Published by Elsevier B.V.

Spatial and temporal analyses of citrus sudden death as a tool to generate hypotheses concerning its etiology.

PubMed

Bassanezi, Renato B; Bergamin Filho, Armando; Amorim, Lilian; Gimenes-Fernandes, Nelson; Gottwald, Tim R; Bové, Joseph M

2003-04-01

ABSTRACT Citrus sudden death (CSD), a new disease of unknown etiology that affects sweet orange grafted on Rangpur lime, was visually monitored for 14 months in 41 groves in Brazil. Ordinary runs analysis of CSD-symptomatic trees indicated a departure from randomness of symptomatic trees status among immediately adjacent trees mainly within rows. The binomial index of dispersion (D) and the intraclass correlation (k) for various quadrat sizes suggested aggregation of CSD-symptomatic trees for almost all plots within the quadrat sizes tested. Estimated parameters of the binary form of Taylor's power law provided an overall measure of aggregation of CSD-symptomatic trees for all quadrat sizes tested. Aggregation in each plot was dependent on disease incidence. Spatial autocorrelation analysis of proximity patterns suggested that aggregation often existed among quadrats of various sizes up to three lag distances; however, significant lag positions discontinuous from main proximity patterns were rare, indicating a lack of spatial association among discrete foci. Some asymmetry was also detected for some spatial autocorrelation proximity patterns, indicating that within-row versus across-row distributions are not necessarily equivalent. These results were interpreted to mean that the cause of the disease was most likely biotic and its dissemination was common within a local area of influence that extended to approximately six trees in all directions, including adjacent trees. Where asymmetry was indicated, this area of influence was somewhat elliptical. Longer-distance patterns were not detected within the confines of the plot sizes tested. Annual rates of CSD progress based on the Gompertz model ranged from 0.37 to 2.02. Numerous similarities were found between the spatial patterns of CSD and Citrus tristeza virus (CTV) described in the literature, both in the presence of the aphid vector, Toxoptera citricida. CSD differs from CTV in that symptoms occur in sweet orange grafted on Rangpur lime. Based on the symptoms of CSD and on its spatial and temporal patterns, our hypothesis is that CSD may be caused by a similar but undescribed pathogen such as a virus and probably vectored by insects such as aphids by similar spatial processes to those affecting CTV.

Bayesian Estimates of Autocorrelations in Single-Case Designs

ERIC Educational Resources Information Center

Shadish, William R.; Rindskopf, David M.; Hedges, Larry V.; Sullivan, Kristynn J.

2012-01-01

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to…

Urban Image Classification: Per-Pixel Classifiers, Sub-Pixel Analysis, Object-Based Image Analysis, and Geospatial Methods. 10; Chapter

NASA Technical Reports Server (NTRS)

Myint, Soe W.; Mesev, Victor; Quattrochi, Dale; Wentz, Elizabeth A.

2013-01-01

Remote sensing methods used to generate base maps to analyze the urban environment rely predominantly on digital sensor data from space-borne platforms. This is due in part from new sources of high spatial resolution data covering the globe, a variety of multispectral and multitemporal sources, sophisticated statistical and geospatial methods, and compatibility with GIS data sources and methods. The goal of this chapter is to review the four groups of classification methods for digital sensor data from space-borne platforms; per-pixel, sub-pixel, object-based (spatial-based), and geospatial methods. Per-pixel methods are widely used methods that classify pixels into distinct categories based solely on the spectral and ancillary information within that pixel. They are used for simple calculations of environmental indices (e.g., NDVI) to sophisticated expert systems to assign urban land covers. Researchers recognize however, that even with the smallest pixel size the spectral information within a pixel is really a combination of multiple urban surfaces. Sub-pixel classification methods therefore aim to statistically quantify the mixture of surfaces to improve overall classification accuracy. While within pixel variations exist, there is also significant evidence that groups of nearby pixels have similar spectral information and therefore belong to the same classification category. Object-oriented methods have emerged that group pixels prior to classification based on spectral similarity and spatial proximity. Classification accuracy using object-based methods show significant success and promise for numerous urban 3 applications. Like the object-oriented methods that recognize the importance of spatial proximity, geospatial methods for urban mapping also utilize neighboring pixels in the classification process. The primary difference though is that geostatistical methods (e.g., spatial autocorrelation methods) are utilized during both the pre- and post-classification steps. Within this chapter, each of the four approaches is described in terms of scale and accuracy classifying urban land use and urban land cover; and for its range of urban applications. We demonstrate the overview of four main classification groups in Figure 1 while Table 1 details the approaches with respect to classification requirements and procedures (e.g., reflectance conversion, steps before training sample selection, training samples, spatial approaches commonly used, classifiers, primary inputs for classification, output structures, number of output layers, and accuracy assessment). The chapter concludes with a brief summary of the methods reviewed and the challenges that remain in developing new classification methods for improving the efficiency and accuracy of mapping urban areas.

Exploring the effect of the spatial scale of fishery management.

PubMed

Takashina, Nao; Baskett, Marissa L

2016-02-07

For any spatially explicit management, determining the appropriate spatial scale of management decisions is critical to success at achieving a given management goal. Specifically, managers must decide how much to subdivide a given managed region: from implementing a uniform approach across the region to considering a unique approach in each of one hundred patches and everything in between. Spatially explicit approaches, such as the implementation of marine spatial planning and marine reserves, are increasingly used in fishery management. Using a spatially explicit bioeconomic model, we quantify how the management scale affects optimal fishery profit, biomass, fishery effort, and the fraction of habitat in marine reserves. We find that, if habitats are randomly distributed, the fishery profit increases almost linearly with the number of segments. However, if habitats are positively autocorrelated, then the fishery profit increases with diminishing returns. Therefore, the true optimum in management scale given cost to subdivision depends on the habitat distribution pattern. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mapping, Bayesian Geostatistical Analysis and Spatial Prediction of Lymphatic Filariasis Prevalence in Africa

PubMed Central

Slater, Hannah; Michael, Edwin

2013-01-01

There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection. PMID:23951194

Partitioning the impact of environment and spatial structure on alpha and beta components of taxonomic, functional, and phylogenetic diversity in European ants.

PubMed

Arnan, Xavier; Cerdá, Xim; Retana, Javier

2015-01-01

We analyze the relative contribution of environmental and spatial variables to the alpha and beta components of taxonomic (TD), phylogenetic (PD), and functional (FD) diversity in ant communities found along different climate and anthropogenic disturbance gradients across western and central Europe, in order to assess the mechanisms structuring ant biodiversity. To this aim we calculated alpha and beta TD, PD, and FD for 349 ant communities, which included a total of 155 ant species; we examined 10 functional traits and phylogenetic relatedness. Variation partitioning was used to examine how much variation in ant diversity was explained by environmental and spatial variables. Autocorrelation in diversity measures and each trait's phylogenetic signal were also analyzed. We found strong autocorrelation in diversity measures. Both environmental and spatial variables significantly contributed to variation in TD, PD, and FD at both alpha and beta scales; spatial structure had the larger influence. The different facets of diversity showed similar patterns along environmental gradients. Environment explained a much larger percentage of variation in FD than in TD or PD. All traits demonstrated strong phylogenetic signals. Our results indicate that environmental filtering and dispersal limitations structure all types of diversity in ant communities. Strong dispersal limitations appear to have led to clustering of TD, PD, and FD in western and central Europe, probably because different historical and evolutionary processes generated different pools of species. Remarkably, these three facets of diversity showed parallel patterns along environmental gradients. Trait-mediated species sorting and niche conservatism appear to structure ant diversity, as evidenced by the fact that more variation was explained for FD and that all traits had strong phylogenetic signals. Since environmental variables explained much more variation in FD than in PD, functional diversity should be a better indicator of community assembly processes than phylogenetic diversity.

Spatial analysis of sunshine duration by combination of satellite and station data

NASA Astrophysics Data System (ADS)

Frei, C.; Stöckli, R.; Dürr, B.

2009-09-01

Sunshine duration can exhibit rich fine scale patterns associated with special meteorological phenomena, such as fog layers and topographically triggered clouds. Networks of climate stations are mostly too coarse and poorly representative to resolve these patterns explicitly. We present a method which combines station observations with satellite-derived cloud-cover data to produce km-scale fields of sunshine duration. The method is not relying on contemporous satellite information, hence it can be applied over climatological time scales. We apply and evaluate the combination method over the territory of Switzerland. The combination method is based on Universal Kriging. First, the satellite data (a Heliosat clear sky index from MSG, extending over a 5 year preiod) is subjected to a S-mode Principal Component (PC) Analysis. Second, a set of leading PC loadings (seasonally stratified) is introduced as external drift covariates and their optimal linear combination is estimated from the station data (70 stations). Finally, the stochastic component is an autocorrelated field with an exponential variogram, estimated climatologically for each calendar month. For Switzerland the leading PCs of the clear sky index depict familiar patterns of cloud variability which are inhereted in the combination process. The resulting sunshine duration fields exhibit fine-scale structures that are physically plausible, linked to the topography and characteristic of the regional climate. These patterns could not be inferred from station data and/or topographic predictors alone. A cross-validation reveals that the combination method explains between 80-90% of the spatial variance in winter and autumn months. In spring and summer the relative performance is lower (60-75% explained spatial variance) but absolute errors are smaller. Our presentation will also discuss some results from a climatology of the derived sunshine duration fields.

Employing the Hilbert-Huang Transform to analyze observed natural complex signals: Calm wind meandering cases

NASA Astrophysics Data System (ADS)

Martins, Luis Gustavo Nogueira; Stefanello, Michel Baptistella; Degrazia, Gervásio Annes; Acevedo, Otávio Costa; Puhales, Franciano Scremin; Demarco, Giuliano; Mortarini, Luca; Anfossi, Domenico; Roberti, Débora Regina; Costa, Felipe Denardin; Maldaner, Silvana

2016-11-01

In this study we analyze natural complex signals employing the Hilbert-Huang spectral analysis. Specifically, low wind meandering meteorological data are decomposed into turbulent and non turbulent components. These non turbulent movements, responsible for the absence of a preferential direction of the horizontal wind, provoke negative lobes in the meandering autocorrelation functions. The meandering characteristic time scales (meandering periods) are determined from the spectral peak provided by the Hilbert-Huang marginal spectrum. The magnitudes of the temperature and horizontal wind meandering period obtained agree with the results found from the best fit of the heuristic meandering autocorrelation functions. Therefore, the new method represents a new procedure to evaluate meandering periods that does not employ mathematical expressions to represent observed meandering autocorrelation functions.

Modified Exponential Weighted Moving Average (EWMA) Control Chart on Autocorrelation Data

NASA Astrophysics Data System (ADS)

Herdiani, Erna Tri; Fandrilla, Geysa; Sunusi, Nurtiti

2018-03-01

In general, observations of the statistical process control are assumed to be mutually independence. However, this assumption is often violated in practice. Consequently, statistical process controls were developed for interrelated processes, including Shewhart, Cumulative Sum (CUSUM), and exponentially weighted moving average (EWMA) control charts in the data that were autocorrelation. One researcher stated that this chart is not suitable if the same control limits are used in the case of independent variables. For this reason, it is necessary to apply the time series model in building the control chart. A classical control chart for independent variables is usually applied to residual processes. This procedure is permitted provided that residuals are independent. In 1978, Shewhart modification for the autoregressive process was introduced by using the distance between the sample mean and the target value compared to the standard deviation of the autocorrelation process. In this paper we will examine the mean of EWMA for autocorrelation process derived from Montgomery and Patel. Performance to be investigated was investigated by examining Average Run Length (ARL) based on the Markov Chain Method.

Statistical Approaches Used to Assess the Equity of Access to Food Outlets: A Systematic Review

PubMed Central

Lamb, Karen E.; Thornton, Lukar E.; Cerin, Ester; Ball, Kylie

2015-01-01

Background Inequalities in eating behaviours are often linked to the types of food retailers accessible in neighbourhood environments. Numerous studies have aimed to identify if access to healthy and unhealthy food retailers is socioeconomically patterned across neighbourhoods, and thus a potential risk factor for dietary inequalities. Existing reviews have examined differences between methodologies, particularly focussing on neighbourhood and food outlet access measure definitions. However, no review has informatively discussed the suitability of the statistical methodologies employed; a key issue determining the validity of study findings. Our aim was to examine the suitability of statistical approaches adopted in these analyses. Methods Searches were conducted for articles published from 2000–2014. Eligible studies included objective measures of the neighbourhood food environment and neighbourhood-level socio-economic status, with a statistical analysis of the association between food outlet access and socio-economic status. Results Fifty-four papers were included. Outlet accessibility was typically defined as the distance to the nearest outlet from the neighbourhood centroid, or as the number of food outlets within a neighbourhood (or buffer). To assess if these measures were linked to neighbourhood disadvantage, common statistical methods included ANOVA, correlation, and Poisson or negative binomial regression. Although all studies involved spatial data, few considered spatial analysis techniques or spatial autocorrelation. Conclusions With advances in GIS software, sophisticated measures of neighbourhood outlet accessibility can be considered. However, approaches to statistical analysis often appear less sophisticated. Care should be taken to consider assumptions underlying the analysis and the possibility of spatially correlated residuals which could affect the results. PMID:29546115

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

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

Keser, Saniye; Duzgun, Sebnem; Department of Geodetic and Geographic Information Technologies, Middle East Technical University, 06800 Ankara

Highlights: Black-Right-Pointing-Pointer Spatial autocorrelation exists in municipal solid waste generation rates for different provinces in Turkey. Black-Right-Pointing-Pointer Traditional non-spatial regression models may not provide sufficient information for better solid waste management. Black-Right-Pointing-Pointer Unemployment rate is a global variable that significantly impacts the waste generation rates in Turkey. Black-Right-Pointing-Pointer Significances of global parameters may diminish at local scale for some provinces. Black-Right-Pointing-Pointer GWR model can be used to create clusters of cities for solid waste management. - Abstract: In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation datamore » is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.« less

Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors.

PubMed

Ortiz-Pelaez, Angel; Pfeiffer, Dirk U; Tempia, Stefano; Otieno, F Tom; Aden, Hussein H; Costagli, Riccardo

2010-04-28

In contrast to most pastoral systems, the Somali livestock production system is oriented towards domestic trade and export with seasonal movement patterns of herds/flocks in search of water and pasture and towards export points. Data from a rinderpest survey and other data sources have been integrated to explore the topology of a contact network of cattle herds based on a spatial proximity criterion and other attributes related to cattle herd dynamics. The objective of the study is to integrate spatial mobility and other attributes with GIS and network approaches in order to develop a predictive spatial model of presence of rinderpest. A spatial logistic regression model was fitted using data for 562 point locations. It includes three statistically significant continuous-scale variables that increase the risk of rinderpest: home range radius, herd density and clustering coefficient of the node of the network whose link was established if the sum of the home ranges of every pair of nodes was equal or greater than the shortest distance between the points. The sensitivity of the model is 85.1% and the specificity 84.6%, correctly classifying 84.7% of the observations. The spatial autocorrelation not accounted for by the model is negligible and visual assessment of a semivariogram of the residuals indicated that there was no undue amount of spatial autocorrelation. The predictive model was applied to a set of 6176 point locations covering the study area. Areas at high risk of having serological evidence of rinderpest are located mainly in the coastal districts of Lower and Middle Juba, the coastal area of Lower Shabele and in the regions of Middle Shabele and Bay. There are also isolated spots of high risk along the border with Kenya and the southern area of the border with Ethiopia. The identification of point locations and areas with high risk of presence of rinderpest and their spatial visualization as a risk map will be useful for informing the prioritization of disease surveillance and control activities for rinderpest in Somalia. The methodology applied here, involving spatial and network parameters, could also be applied to other diseases and/or species as part of a standardized approach for the design of risk-based surveillance activities in nomadic pastoral settings.

Spatial synchrony of local populations has increased in association with the recent Northern Hemisphere climate trend.

PubMed

Post, Eric; Forchhammer, Mads C

2004-06-22

According to ecological theory, populations whose dynamics are entrained by environmental correlation face increased extinction risk as environmental conditions become more synchronized spatially. This prediction is highly relevant to the study of ecological consequences of climate change. Recent empirical studies have indicated, for example, that large-scale climate synchronizes trophic interactions and population dynamics over broad spatial scales in freshwater and terrestrial systems. Here, we present an analysis of century-scale, spatially replicated data on local weather and the population dynamics of caribou in Greenland. Our results indicate that spatial autocorrelation in local weather has increased with large-scale climatic warming. This increase in spatial synchrony of environmental conditions has been matched, in turn, by an increase in the spatial synchrony of local caribou populations toward the end of the 20th century. Our results indicate that spatial synchrony in environmental conditions and the populations influenced by them are highly variable through time and can increase with climatic warming. We suggest that if future warming can increase population synchrony, it may also increase extinction risk.

Scaling analysis of stock markets

NASA Astrophysics Data System (ADS)

Bu, Luping; Shang, Pengjian

2014-06-01

In this paper, we apply the detrended fluctuation analysis (DFA), local scaling detrended fluctuation analysis (LSDFA), and detrended cross-correlation analysis (DCCA) to investigate correlations of several stock markets. DFA method is for the detection of long-range correlations used in time series. LSDFA method is to show more local properties by using local scale exponents. DCCA method is a developed method to quantify the cross-correlation of two non-stationary time series. We report the results of auto-correlation and cross-correlation behaviors in three western countries and three Chinese stock markets in periods 2004-2006 (before the global financial crisis), 2007-2009 (during the global financial crisis), and 2010-2012 (after the global financial crisis) by using DFA, LSDFA, and DCCA method. The findings are that correlations of stocks are influenced by the economic systems of different countries and the financial crisis. The results indicate that there are stronger auto-correlations in Chinese stocks than western stocks in any period and stronger auto-correlations after the global financial crisis for every stock except Shen Cheng; The LSDFA shows more comprehensive and detailed features than traditional DFA method and the integration of China and the world in economy after the global financial crisis; When it turns to cross-correlations, it shows different properties for six stock markets, while for three Chinese stocks, it reaches the weakest cross-correlations during the global financial crisis.

A novel approach for introducing cloud spatial structure into cloud radiative transfer parameterizations

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

Huang, Dong; Liu, Yangang

2014-12-18

Subgrid-scale variability is one of the main reasons why parameterizations are needed in large-scale models. Although some parameterizations started to address the issue of subgrid variability by introducing a subgrid probability distribution function for relevant quantities, the spatial structure has been typically ignored and thus the subgrid-scale interactions cannot be accounted for physically. Here we present a new statistical-physics-like approach whereby the spatial autocorrelation function can be used to physically capture the net effects of subgrid cloud interaction with radiation. The new approach is able to faithfully reproduce the Monte Carlo 3D simulation results with several orders less computational cost,more » allowing for more realistic representation of cloud radiation interactions in large-scale models.« less

A Hybrid Model for Spatially and Temporally Resolved Ozone Exposures in the Continental United States

PubMed Central

Di, Qian; Rowland, Sebastian; Koutrakis, Petros; Schwartz, Joel

2017-01-01

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term. PMID:27332675

Effects of environmental amenities and locational disamenities on home values in the Santa Cruz watershed: a hedonic analysis using census data

USGS Publications Warehouse

Arora, Gaurav; Frisvold, George; Norman, Laura

2014-01-01

For this study, we used the hedonic pricing method to measure the effects of natural amenities on home prices in the U.S-side of the Santa Cruz Watershed. We employed multivariate spatial regression techniques to estimate how difference factors affect median home values in 613 census block groups of the 2000 Census, accounting for spatial autocorrelation, spatial lags, and/or spatial heterogeneity in the data. Diagnostic tests suggest that failure to account for the hedonic model can be classified as (1) physical features of the housing stock, (2) neighborhood characteristics, and (3) environmental attributes. Census data was combined with GIS data for vegetation and land cover, land administration, measures of species richness and open space, and proximity to amenities and disamenities. Census block groups close to the US-Mexico border of airports/air bases were negative. Results suggest that policies to maintain biodiversity and open space provide economic benefits to homeowners, reflected in higher home values. Future research will quantify the marginal effects of regression explanatory variables on home values to assess their economic and policy significant. These marginal effects will be used as input indicators to discern potential economic impacts of various scenarios in the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM). Future research will also expand this effort into the Mexican-portion of the watershed.

Analysis of intermediate period correlations of coda from deep earthquakes

NASA Astrophysics Data System (ADS)

Poli, Piero; Campillo, Michel; de Hoop, Maarten

2017-11-01

We aim at assessing quantitatively the nature of the signals that appear in coda wave correlations at periods >20 s. These signals contain transient constituents with arrival times corresponding to deep seismic phases. These (body-wave) constituents can be used for imaging. To evaluate this approach, we calculate the autocorrelations of the vertical component seismograms for the Mw 8.4 sea of Okhotsk earthquake at 400 stations in the Eastern US, using data from 1 h before to 50 h after the earthquake. By using array analysis and modes identification, we discover the dominant role played by high quality factor normal modes in the emergence of strong coherent phases as ScS-like, and P'P'df-like. We then make use of geometrical quantization to derive the constituent rays associated with particular modes, and gain insights about the ballistic reverberation of the rays that contributes to the emergence of body waves. Our study indicates that the signals measured in the spatially averaged autocorrelations have a physical significance, but a direct interpretation of ScS-like and P'P'df-like is not trivial. Indeed, even a single simple measurement of long period late coda in a limited period band could provide valuable information on the deep structure by using the temporal information of its autocorrelation, a procedure that could be also useful for planetary exploration.

Spatial Genetic Structure and Clonal Diversity in an Alpine Population of Salix herbacea (Salicaceae)

PubMed Central

Reisch, Christoph; Schurm, Sophia; Poschlod, Peter

2007-01-01

Background and Aims Many alpine plant species combine clonal and sexual reproduction to minimize the risks of flowering and seed production in high mountain regions. The spatial genetic structure and diversity of these alpine species is strongly affected by different clonal strategies (phalanx or guerrilla) and the proportion of generative and vegetative reproduction. Methods The clonal structure of the alpine plant species Salix herbacea was investigated in a 3 × 3 m plot of an alpine meadow using microsatellite (simple sequence repeat; SSR) analysis. The data obtained were compared with the results of a random amplified polymorphic DNA (RAPD) analysis. Key Results SSR analysis, based on three loci and 16 alleles, revealed 24 different genotypes and a proportion of distinguishable genotypes of 0·18. Six SSR clones were found consisting of at least five samples, 17 clones consisting of more than two samples and seven single genotypes. Mean clone size comprising at least five samples was 0·96 m2, and spatial autocorrelation analysis showed strong similarity of samples up to 130 cm. RAPD analysis revealed a higher level of clonal diversity but a comparable number of larger clones and a similar spatial structure. Conclusions The spatial genetic structure as well as the occurrence of single genotypes revealed in this study suggests both clonal and sexual propagation and repeated seedling recruitment in established populations of S. herbacea and is thus suggestive of a relaxed phalanx strategy. PMID:17242040

Correlation Factors Describing Primary and Spatial Sensations of Sound Fields

NASA Astrophysics Data System (ADS)

ANDO, Y.

2002-11-01

The theory of subjective preference of the sound field in a concert hall is established based on the model of human auditory-brain system. The model consists of the autocorrelation function (ACF) mechanism and the interaural crosscorrelation function (IACF) mechanism for signals arriving at two ear entrances, and the specialization of human cerebral hemispheres. This theory can be developed to describe primary sensations such as pitch or missing fundamental, loudness, timbre and, in addition, duration sensation which is introduced here as a fourth. These four primary sensations may be formulated by the temporal factors extracted from the ACF associated with the left hemisphere and, spatial sensations such as localization in the horizontal plane, apparent source width and subjective diffuseness are described by the spatial factors extracted from the IACF associated with the right hemisphere. Any important subjective responses of sound fields may be described by both temporal and spatial factors.

Spatial and Temporal Changes of Aerosol Optical Depth and its Driving Factors Based on Modis in Jiangsu Province

NASA Astrophysics Data System (ADS)

Jiang, C.; Xu, Q.; Gu, Y. K.; Qian, X. Y.; He, J. N.

2018-04-01

Aerosol Optical Depth (AOD) is of great value for studying air mass and its changes. In this paper, we studied the spatial-temporal changes of AOD and its driving factors based on spatial autocorrelation model, gravity model and multiple regression analysis in Jiangsu Province from 2007 to 2016. The results showed that in terms of spatial distribution, the southern AOD value is higher, and the high-value aggregation areas are significant, while the northern AOD value is lower, but the low-value aggregation areas constantly change. The AOD gravity centers showed a clear point-like aggregation. In terms of temporal changes, the overall AOD in Jiangsu Province increased year by year in fluctuation. In terms of driving factors, the total amount of vehicles, precipitation and temperature are important factors for the growth of AOD.

Research on the Spatial-Temporal Distribution Pattern of the Network Attention of Fog and Haze in China

NASA Astrophysics Data System (ADS)

Weng, Lingyan; Han, Xugao

2018-01-01

Understanding the spatial-temporal distribution pattern of fog and haze is the base to deal with them by adjusting measures to local conditions. Taking 31 provinces in China mainland as the research areas, this paper collected data from Baidu index on the network attention of fog and haze in relevant areas from 2011 to 2016, and conducted an analysis of their spatial-temporal distribution pattern by using autocorrelation analysis. The results show that the network attention of fog and haze has an overall spatial distribution pattern of “higher in the eastern and central, lower in the western China”. There are regional differences in different provinces in terms of network attention. Network attention of fog and haze indicates an obvious geographical agglomeration phenomenon, which is a gradual enlargement of the agglomeration area of higher value with a slight shrinking of those lower value agglomeration areas.

Moho depth across the Trans-European Suture Zone from ambient vibration autocorrelations

NASA Astrophysics Data System (ADS)

Becker, Gesa; Knapmeyer-Endrun, Brigitte

2017-04-01

In 2018 the InSight mission to Mars will deploy a seismic station on the planet. This seismic station will consist of a three-component very broadband seismic sensor and a collocated three-component short period seismometer. Single station methods are therefore needed to extract information from the data and learn more about the interior structure of Mars. One potential method is the extraction of reflected phases from autocorrelations. Here autocorrelations are derived from ambient seismic noise to make the most of the data expected, as seismicity on Mars is likely less abundant than on Earth. These autocorrelations are calculated using a phase autocorrelation algorithm and time-frequency domain phase-weighted stacking as the main processing steps in addition to smoothing the spectrum of the data with a short term-long term average algorithm. Afterward the obtained results are filtered and analyzed in the frequency range of 1-2 Hz. The developed processing scheme is applied to data from permanent seismic stations located in different geological provinces across Europe, i.e. the Upper Rhine Graben, Central European Platform, Bohemian Massif, Northern German and Polish Basin, and the East European Craton, with varying Moho depths between 25-50 km. These crustal thicknesses are comparable to various estimates for Mars, therefore providing a good reference and indication of resolvability for Moho depths that might be encountered at the landing site. Changes in reflectivity can be observed in the calculated autocorrelations. The lag times of these changes are converted into depths with the help of available velocity information (EPcrust and local models for Poland and the Czech Republic, respectively) and the results are compared to existing information on Moho depths, which show good agreement. The results are temporarily stable, but show a clear correlation with the existence of cultural noise. Based on the closely located broadband and short period stations of the GERESS-array, it is shown that the processing scheme is also applicable to short period stations. Subsequently it is applied to the mainly short period and temporary stations of the PASSEQ network along the seismic profile POLONAISE P4, running from Eastern Germany to Lithuania crossing the Trans-European Suture Zone.

Hotspot detection using image pattern recognition based on higher-order local auto-correlation

NASA Astrophysics Data System (ADS)

Maeda, Shimon; Matsunawa, Tetsuaki; Ogawa, Ryuji; Ichikawa, Hirotaka; Takahata, Kazuhiro; Miyairi, Masahiro; Kotani, Toshiya; Nojima, Shigeki; Tanaka, Satoshi; Nakagawa, Kei; Saito, Tamaki; Mimotogi, Shoji; Inoue, Soichi; Nosato, Hirokazu; Sakanashi, Hidenori; Kobayashi, Takumi; Murakawa, Masahiro; Higuchi, Tetsuya; Takahashi, Eiichi; Otsu, Nobuyuki

2011-04-01

Below 40nm design node, systematic variation due to lithography must be taken into consideration during the early stage of design. So far, litho-aware design using lithography simulation models has been widely applied to assure that designs are printed on silicon without any error. However, the lithography simulation approach is very time consuming, and under time-to-market pressure, repetitive redesign by this approach may result in the missing of the market window. This paper proposes a fast hotspot detection support method by flexible and intelligent vision system image pattern recognition based on Higher-Order Local Autocorrelation. Our method learns the geometrical properties of the given design data without any defects as normal patterns, and automatically detects the design patterns with hotspots from the test data as abnormal patterns. The Higher-Order Local Autocorrelation method can extract features from the graphic image of design pattern, and computational cost of the extraction is constant regardless of the number of design pattern polygons. This approach can reduce turnaround time (TAT) dramatically only on 1CPU, compared with the conventional simulation-based approach, and by distributed processing, this has proven to deliver linear scalability with each additional CPU.

Low Streamflow Forcasting using Minimum Relative Entropy

NASA Astrophysics Data System (ADS)

Cui, H.; Singh, V. P.

2013-12-01

Minimum relative entropy spectral analysis is derived in this study, and applied to forecast streamflow time series. Proposed method extends the autocorrelation in the manner that the relative entropy of underlying process is minimized so that time series data can be forecasted. Different prior estimation, such as uniform, exponential and Gaussian assumption, is taken to estimate the spectral density depending on the autocorrelation structure. Seasonal and nonseasonal low streamflow series obtained from Colorado River (Texas) under draught condition is successfully forecasted using proposed method. Minimum relative entropy determines spectral of low streamflow series with higher resolution than conventional method. Forecasted streamflow is compared to the prediction using Burg's maximum entropy spectral analysis (MESA) and Configurational entropy. The advantage and disadvantage of each method in forecasting low streamflow is discussed.

Estimation of Random Medium Parameters from 2D Post-Stack Seismic Data and Its Application in Seismic Inversion

NASA Astrophysics Data System (ADS)

Yang, X.; Zhu, P.; Gu, Y.; Xu, Z.

2015-12-01

Small scale heterogeneities of subsurface medium can be characterized conveniently and effectively using a few simple random medium parameters (RMP), such as autocorrelation length, angle and roughness factor, etc. The estimation of these parameters is significant in both oil reservoir prediction and metallic mine exploration. Poor accuracy and low stability existed in current estimation approaches limit the application of random medium theory in seismic exploration. This study focuses on improving the accuracy and stability of RMP estimation from post-stacked seismic data and its application in the seismic inversion. Experiment and theory analysis indicate that, although the autocorrelation of random medium is related to those of corresponding post-stacked seismic data, the relationship is obviously affected by the seismic dominant frequency, the autocorrelation length, roughness factor and so on. Also the error of calculation of autocorrelation in the case of finite and discrete model decreases the accuracy. In order to improve the precision of estimation of RMP, we design two improved approaches. Firstly, we apply region growing algorithm, which often used in image processing, to reduce the influence of noise in the autocorrelation calculated by the power spectrum method. Secondly, the orientation of autocorrelation is used as a new constraint in the estimation algorithm. The numerical experiments proved that it is feasible. In addition, in post-stack seismic inversion of random medium, the estimated RMP may be used to constrain inverse procedure and to construct the initial model. The experiment results indicate that taking inversed model as random medium and using relatively accurate estimated RMP to construct initial model can get better inversion result, which contained more details conformed to the actual underground medium.

The Role of Protected Areas in the Avoidance of Anthropogenic Conversion in a High Pressure Region: A Matching Method Analysis in the Core Region of the Brazilian Cerrado

PubMed Central

Paiva, Rodrigo José Oliveira; Brites, Ricardo Seixas; Machado, Ricardo Bomfim

2015-01-01

Global efforts to avoid anthropogenic conversion of natural habitat rely heavily on the establishment of protected areas. Studies that evaluate the effectiveness of these areas with a focus on preserving the natural habitat define effectiveness as a measure of the influence of protected areas on total avoided conversion. Changes in the estimated effectiveness are related to local and regional differences, evaluation methods, restriction categories that include the protected areas, and other characteristics. The overall objective of this study was to evaluate the effectiveness of protected areas to prevent the advance of the conversion of natural areas in the core region of the Brazil’s Cerrado Biome, taking into account the influence of the restriction degree, governmental sphere, time since the establishment of the protected area units, and the size of the area on the performance of protected areas. The evaluation was conducted using matching methods and took into account the following two fundamental issues: control of statistical biases caused by the influence of covariates on the likelihood of anthropogenic conversion and the non-randomness of the allocation of protected areas throughout the territory (spatial correlation effect) and the control of statistical bias caused by the influence of auto-correlation and leakage effect. Using a sample design that is not based on ways to control these biases may result in outcomes that underestimate or overestimate the effectiveness of those units. The matching method accounted for a bias reduction in 94–99% of the estimation of the average effect of protected areas on anthropogenic conversion and allowed us to obtain results with a reduced influence of the auto-correlation and leakage effects. Most protected areas had a positive influence on the maintenance of natural habitats, although wide variation in this effectiveness was dependent on the type, restriction, governmental sphere, size and age group of the unit. PMID:26222140

A multi-level analysis of the relationship between environmental factors and questing Ixodes ricinus dynamics in Belgium

PubMed Central

2012-01-01

Background Ticks are the most important pathogen vectors in Europe. They are known to be influenced by environmental factors, but these links are usually studied at specific temporal or spatial scales. Focusing on Ixodes ricinus in Belgium, we attempt to bridge the gap between current “single-sided” studies that focus on temporal or spatial variation only. Here, spatial and temporal patterns of ticks are modelled together. Methods A multi-level analysis of the Ixodes ricinus patterns in Belgium was performed. Joint effects of weather, habitat quality and hunting on field sampled tick abundance were examined at two levels, namely, sampling level, which is associated with temporal dynamics, and site level, which is related to spatial dynamics. Independent variables were collected from standard weather station records, game management data and remote sensing-based land cover data. Results At sampling level, only a marginally significant effect of daily relative humidity and temperature on the abundance of questing nymphs was identified. Average wind speed of seven days prior to the sampling day was found important to both questing nymphs and adults. At site level, a group of landscape-level forest fragmentation indices were highlighted for both questing nymph and adult abundance, including the nearest-neighbour distance, the shape and the aggregation level of forest patches. No cross-level effects or spatial autocorrelation were found. Conclusions Nymphal and adult ticks responded differently to environmental variables at different spatial and temporal scales. Our results can advise spatio-temporal extents of environment data collection for continuing empirical investigations and potential parameters for biological tick models. PMID:22830528

An accessible method for implementing hierarchical models with spatio-temporal abundance data

USGS Publications Warehouse

Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

2012-01-01

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

Spatial relationships among dairy farms, drinking water quality, and maternal-child health outcomes in the San Joaquin Valley.

PubMed

Blake, Sarah Brown

2014-01-01

Access to clean and affordable water is a significant public health issue globally, in the United States, and in California where land is heavily used for agriculture and dairy operations. The purpose of this study was to explore the geographic relationships among dairy farms, nitrate levels in drinking water, low birth weight, and socioeconomic data at the ZIP code level in the San Joaquin Valley. This ecological study used a Geographic Information System (GIS) to explore and analyze secondary data. A total of 211 ZIP codes were analyzed using spatial autocorrelation and regression analysis methods in ArcGIS version 10.1. ZIP codes with dairies had a higher percentage of Hispanic births (p = .001). Spatial statistics revealed that ZIP codes with more dairy farms and a higher dairy cow density had higher levels of nitrate contamination. No correlation was detected between LBW and unsafe nitrate levels at the ZIP code level. Further research examining communities that use private and small community wells in the San Joaquin Valley should be conducted. Birth data from smaller geographic areas should be used to continue exploring the relationship between birth outcomes and nitrate contamination in drinking water. © 2014 Wiley Periodicals, Inc.

Spatial distribution of psychotic disorders in an urban area of France: an ecological study.

PubMed

Pignon, Baptiste; Schürhoff, Franck; Baudin, Grégoire; Ferchiou, Aziz; Richard, Jean-Romain; Saba, Ghassen; Leboyer, Marion; Kirkbride, James B; Szöke, Andrei

2016-05-18

Previous analyses of neighbourhood variations of non-affective psychotic disorders (NAPD) have focused mainly on incidence. However, prevalence studies provide important insights on factors associated with disease evolution as well as for healthcare resource allocation. This study aimed to investigate the distribution of prevalent NAPD cases in an urban area in France. The number of cases in each neighbourhood was modelled as a function of potential confounders and ecological variables, namely: migrant density, economic deprivation and social fragmentation. This was modelled using statistical models of increasing complexity: frequentist models (using Poisson and negative binomial regressions), and several Bayesian models. For each model, assumptions validity were checked and compared as to how this fitted to the data, in order to test for possible spatial variation in prevalence. Data showed significant overdispersion (invalidating the Poisson regression model) and residual autocorrelation (suggesting the need to use Bayesian models). The best Bayesian model was Leroux's model (i.e. a model with both strong correlation between neighbouring areas and weaker correlation between areas further apart), with economic deprivation as an explanatory variable (OR = 1.13, 95% CI [1.02-1.25]). In comparison with frequentist methods, the Bayesian model showed a better fit. The number of cases showed non-random spatial distribution and was linked to economic deprivation.

Effect of vegetation on cutaneous leishmaniasis in Paraná, Brazil

PubMed Central

Melo, Helen Aline; Rossoni, Diogo Francisco; Teodoro, Ueslei

2018-01-01

BACKGROUND Cutaneous leishmaniasis (CL) is endemic in the state of Paraná, Brazil. OBJECTIVE This study aimed at analysing the influence of the remaining native vegetation on the prevalence of CL in Paraná. METHODS Global testing was used for spatial autocorrelation along with simultaneous autoregressive model (SAR). The regression was based on the CL coefficient (cases/100,000 inhabitants) as a function of the percentage of natural vegetation cover, altitude, total number of cases, and spatial density (SD) per km2; the location data of the Paraná state municipalities and the detection coefficient (DC) (cases/100,000 inhabitants) of autochthonous cases of CL were obtained from the SINAN in 2012 and 2016. Data on the remaining forests were collected from the Fundação SOS Mata Atlântica and Instituto Nacional de Pesquisas Espaciais. FINDINGS The spatial regression of DC revealed statistical significance for SD (Z = 24.1359, p < 0.05, 2012-2013; Z = 24.0817, p < 0.05, 2013-2014; Z = 33.4824, p < 0.05, 2014-2015; and Z = 27.1515, p < 0.05, 2015-2016. CONCLUSIONS CL cases are reported in areas with native vegetation, such as in riparian forests. However, vegetation is not the only variable that influences the incidence of CL. PMID:29768531

Domestic violence, poverty, and social services: does location matter?

PubMed

Hetling, Andrea; Zhang, Haiyan

2010-01-01

Objective. This study investigates whether or not domestic violence agencies are located in areas of need. Recent research indicates that community economic disadvantage is a risk factor for intimate partner violence, but related questions regarding the geographic location of social service agencies have not been investigated.Methods. Using Connecticut as a case study, we analyze the relationship of agency location and police-reported domestic violence incidents and assaults using OLS regression and correcting for spatial autocorrelation.Results. The presence of an agency within a town has no relationship with the rates of domestic violence. However, regional patterns are evident.Conclusion. Findings indicate that programs are not geographically mismatched with need, but neither are programs located in towns with higher rates of incidents or assaults. Future research and planning efforts should consider the geographic location of agencies.

Quantitative single-molecule imaging by confocal laser scanning microscopy.

PubMed

Vukojevic, Vladana; Heidkamp, Marcus; Ming, Yu; Johansson, Björn; Terenius, Lars; Rigler, Rudolf

2008-11-25

A new approach to quantitative single-molecule imaging by confocal laser scanning microscopy (CLSM) is presented. It relies on fluorescence intensity distribution to analyze the molecular occurrence statistics captured by digital imaging and enables direct determination of the number of fluorescent molecules and their diffusion rates without resorting to temporal or spatial autocorrelation analyses. Digital images of fluorescent molecules were recorded by using fast scanning and avalanche photodiode detectors. In this way the signal-to-background ratio was significantly improved, enabling direct quantitative imaging by CLSM. The potential of the proposed approach is demonstrated by using standard solutions of fluorescent dyes, fluorescently labeled DNA molecules, quantum dots, and the Enhanced Green Fluorescent Protein in solution and in live cells. The method was verified by using fluorescence correlation spectroscopy. The relevance for biological applications, in particular, for live cell imaging, is discussed.

Exploring the spatial heterogeneity of terraced landscapes using LiDAR: the Slope Local Length of Auto-Correlation (SLLAC).

NASA Astrophysics Data System (ADS)

Sofia, Giulia; Marinello, Francesco; Tarolli, Paolo

2014-05-01

Terraces represent an outstanding example that displays centuries of a ubiquitous human-Earth interaction, in a very specific and productive way, and they are a significant part of numerous local economies. They, in fact, optimise the local resources for agricultural purposes, but also exploit marginal landscapes, expanding local populations. The ubiquity, variety, and importance of terraces have motivated studies designed to understand them better both as cultural and ecological features, but also as elements that can deeply influence runoff generation and propagation, contributing to local instabilities, and triggering or aggravating land degradation processes. Their vulnerability in the face of fast-growing urban settlements and the changes in agricultural practices is also well known, prompting protection measures strongly supported by local communities, but also by national and international projects. This work explores the spatial heterogeneity of terraced landscapes, identifying a proper indicator able to discriminate a terraced landscape respect to a more natural one. Recognizing and characterizing terraced areas can offer important multi-temporal insights into issues such as agricultural sustainability, indigenous knowledge systems, human-induced impact on soil degradation or erosive and landslide processes, geomorphological and pedologic processes that influence soil development, and climatic and biodiversity changes. More in detail, the present work introduces a new morphological indicator from LiDAR, effectively implementable for the automatic characterization of terraced landscapes. For the study, we tested the algorithm for environments that differ in term of natural morphology and terracing system. Starting from a LiDAR Digital Terrain Models (DTM), we considered the local auto-correlation (~local self-similarity) of the slope, calculating the correlation between a slope patch and its surrounding areas. We define the resulting map as the "Slope Local Length of Auto-Correlation", or SLLAC map. The SLLAC map texture is characterized by the presence of peculiar elongated fibers that change depending on the landscape morphology, and on the type of terracing system. The differences in texture can be measured, and they can be used to discriminate terraced areas from more natural ones. Given the raising importance of these landscapes, the proposed procedure can offer an important and promising tool to explore the spatial heterogeneity of terraced sites.

Assessing exotic plant species invasions and associated soil characteristics: A case study in eastern Rocky Mountain National Park, Colorado, USA, using the pixel nested plot design

USGS Publications Warehouse

Kalkhan, M.A.; Stafford, E.J.; Woodly, P.J.; Stohlgren, T.J.

2007-01-01

Rocky Mountain National Park (RMNP), Colorado, USA, contains a diversity of plant species. However, many exotic plant species have become established, potentially impacting the structure and function of native plant communities. Our goal was to quantify patterns of exotic plant species in relation to native plant species, soil characteristics, and other abiotic factors that may indicate or predict their establishment and success. Our research approach for field data collection was based on a field plot design called the pixel nested plot. The pixel nested plot provides a link to multi-phase and multi-scale spatial modeling-mapping techniques that can be used to estimate total species richness and patterns of plant diversity at finer landscape scales. Within the eastern region of RMNP, in an area of approximately 35,000 ha, we established a total of 60 pixel nested plots in 9 vegetation types. We used canonical correspondence analysis (CCA) and multiple linear regressions to quantify relationships between soil characteristics and native and exotic plant species richness and cover. We also used linear correlation, spatial autocorrelation and cross correlation statistics to test for the spatial patterns of variables of interest. CCA showed that exotic species were significantly (P < 0.05) associated with photosynthetically active radiation (r = 0.55), soil nitrogen (r = 0.58) and bare ground (r = -0.66). Pearson's correlation statistic showed significant linear relationships between exotic species, organic carbon, soil nitrogen, and bare ground. While spatial autocorrelations indicated that our 60 pixel nested plots were spatially independent, the cross correlation statistics indicated that exotic plant species were spatially associated with bare ground, in general, exotic plant species were most abundant in areas of high native species richness. This indicates that resource managers should focus on the protection of relatively rare native rich sites with little canopy cover, and fertile soils. Using the pixel nested plot approach for data collection can facilitate the ecological monitoring of these vulnerable areas at the landscape scale in a time- and cost-effective manner. ?? 2006 Elsevier B.V. All rights reserved.

Schistosomiasis mansoni incidence data in Rwanda can improve prevalence assessments, by providing high-resolution hotspot and risk factors identification.

PubMed

Nyandwi, E; Veldkamp, A; Amer, S; Karema, C; Umulisa, I

2017-10-25

Schistosomiasis mansoni constitutes a significant public health problem in Rwanda. The nationwide prevalence mapping conducted in 2007-2008 revealed that prevalence per district ranges from 0 to 69.5% among school children. In response, mass drug administration campaigns were initiated. However, a few years later some additional small-scale studies revealed the existence of areas of high transmission in districts formerly classified as low endemic suggesting the need for a more accurate methodology for identification of hotspots. This study investigated if confirmed cases of schistosomiasis recorded at health facility level can be used to, next to existing prevalence data, detect geographically more accurate hotspots of the disease and its associated risk factors. A GIS-based spatial and statistical analysis was carried out. Confirmed cases, recorded at primary health facilities level, were combined with demographic data to calculate incidence rates for each of 367 health facility service area. Empirical Bayesian smoothing was used to deal with rate instability. Incidence rates were compared with prevalence data to identify their level of agreement. Spatial autocorrelation of the incidence rates was analyzed using Moran's Index, to check if spatial clustering occurs. Finally, the spatial relationship between schistosomiasis distribution and potential risk factors was assessed using multiple regression. Incidence rates for 2007-2008 were highly correlated with prevalence values (R 2  = 0.79), indicating that in the case of Rwanda incidence data can be used as a proxy for prevalence data. We observed a focal distribution of schistosomiasis with a significant spatial autocorrelation (Moran's I > 0: 0,05-0.20 and p ≤ 0,05), indicating the occurrence of hotspots. Regarding risk factors, it was identified that the spatial pattern of schistosomiasis is significantly associated with wetland conditions and rice cultivation. In Rwanda the high density of health facilities and the standardized microscopic laboratory diagnostic allow the derived data to be used to complement prevalence studies to identify hotspots of schistosomiasis and its associated risk factors. This type of information, in turn, can support disease control interventions and monitoring.

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh.

PubMed

Ali, Mohammad; Goovaerts, Pierre; Nazia, Nushrat; Haq, M Zahirul; Yunus, Mohammad; Emch, Michael

2006-10-13

Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh. The spatial analysis was confined to patrilineally-related clusters of households, known as baris, located within 9 kilometers from the Matlab hospital to avoid underestimating the risk of disease incidence, since patients far away from the medical facilities are less likely to travel. Semivariogram models reveal a range of autocorrelation of 1.1 km for dysentery and 0.37 km for cholera. This result translates into a cholera risk map that is patchier than the dysentery map that shows a large zone of high incidence in the south-central part of the study area, which is quasi-urban. On both maps, lower risk values are found in the Northern part of the study area, which is also the most distant from the Matlab hospital. The weaker spatial continuity of cholera versus dysentery incidence rates resulted in larger kriging variance across the study area. The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of incidence rates into the mapping of risk values and the quantification of the associated uncertainty. Differences in spatial patterns, in particular the range of spatial autocorrelation, reflect differences in the mode of transmission of cholera and dysentery. Our risk maps for cholera and dysentery incidences should help identifying putative factors of increased disease incidence, leading to more effective prevention and remedial actions in endemic areas.

Application of Poisson kriging to the mapping of cholera and dysentery incidence in an endemic area of Bangladesh

PubMed Central

Ali, Mohammad; Goovaerts, Pierre; Nazia, Nushrat; Haq, M Zahirul; Yunus, Mohammad; Emch, Michael

2006-01-01

Background Disease maps can serve to display incidence rates geographically, to inform on public health provision about the success or failure of interventions, and to make hypothesis or to provide evidences concerning disease etiology. Poisson kriging was recently introduced to filter the noise attached to rates recorded over sparsely populated administrative units. Its benefit over simple population-weighted averages and empirical Bayesian smoothers was demonstrated by simulation studies using county-level cancer mortality rates. This paper presents the first application of Poisson kriging to the spatial interpolation of local disease rates, resulting in continuous maps of disease rate estimates and the associated prediction variance. The methodology is illustrated using cholera and dysentery data collected in a cholera endemic area (Matlab) of Bangladesh. Results The spatial analysis was confined to patrilineally-related clusters of households, known as baris, located within 9 kilometers from the Matlab hospital to avoid underestimating the risk of disease incidence, since patients far away from the medical facilities are less likely to travel. Semivariogram models reveal a range of autocorrelation of 1.1 km for dysentery and 0.37 km for cholera. This result translates into a cholera risk map that is patchier than the dysentery map that shows a large zone of high incidence in the south-central part of the study area, which is quasi-urban. On both maps, lower risk values are found in the Northern part of the study area, which is also the most distant from the Matlab hospital. The weaker spatial continuity of cholera versus dysentery incidence rates resulted in larger kriging variance across the study area. Conclusion The approach presented in this paper enables researchers to incorporate the pattern of spatial dependence of incidence rates into the mapping of risk values and the quantification of the associated uncertainty. Differences in spatial patterns, in particular the range of spatial autocorrelation, reflect differences in the mode of transmission of cholera and dysentery. Our risk maps for cholera and dysentery incidences should help identifying putative factors of increased disease incidence, leading to more effective prevention and remedial actions in endemic areas. PMID:17038192

3D Target Localization of Modified 3D MUSIC for a Triple-Channel K-Band Radar.

PubMed

Li, Ying-Chun; Choi, Byunggil; Chong, Jong-Wha; Oh, Daegun

2018-05-20

In this paper, a modified 3D multiple signal classification (MUSIC) algorithm is proposed for joint estimation of range, azimuth, and elevation angles of K-band radar with a small 2 × 2 horn antenna array. Three channels of the 2 × 2 horn antenna array are utilized as receiving channels, and the other one is a transmitting antenna. The proposed modified 3D MUSIC is designed to make use of a stacked autocorrelation matrix, whose element matrices are related to each other in the spatial domain. An augmented 2D steering vector based on the stacked autocorrelation matrix is proposed for the modified 3D MUSIC, instead of the conventional 3D steering vector. The effectiveness of the proposed modified 3D MUSIC is verified through implementation with a K-band frequency-modulated continuous-wave (FMCW) radar with the 2 × 2 horn antenna array through a variety of experiments in a chamber.

Statistical Estimation of Heterogeneities: A New Frontier in Well Testing

NASA Astrophysics Data System (ADS)

Neuman, S. P.; Guadagnini, A.; Illman, W. A.; Riva, M.; Vesselinov, V. V.

2001-12-01

Well-testing methods have traditionally relied on analytical solutions of groundwater flow equations in relatively simple domains, consisting of one or at most a few units having uniform hydraulic properties. Recently, attention has been shifting toward methods and solutions that would allow one to characterize subsurface heterogeneities in greater detail. On one hand, geostatistical inverse methods are being used to assess the spatial variability of parameters, such as permeability and porosity, on the basis of multiple cross-hole pressure interference tests. On the other hand, analytical solutions are being developed to describe the mean and variance (first and second statistical moments) of flow to a well in a randomly heterogeneous medium. Geostatistical inverse interpretation of cross-hole tests yields a smoothed but detailed "tomographic" image of how parameters actually vary in three-dimensional space, together with corresponding measures of estimation uncertainty. Moment solutions may soon allow one to interpret well tests in terms of statistical parameters such as the mean and variance of log permeability, its spatial autocorrelation and statistical anisotropy. The idea of geostatistical cross-hole tomography is illustrated through pneumatic injection tests conducted in unsaturated fractured tuff at the Apache Leap Research Site near Superior, Arizona. The idea of using moment equations to interpret well-tests statistically is illustrated through a recently developed three-dimensional solution for steady state flow to a well in a bounded, randomly heterogeneous, statistically anisotropic aquifer.

Empirical spatial econometric modelling of small scale neighbourhood

NASA Astrophysics Data System (ADS)

Gerkman, Linda

2012-07-01

The aim of the paper is to model small scale neighbourhood in a house price model by implementing the newest methodology in spatial econometrics. A common problem when modelling house prices is that in practice it is seldom possible to obtain all the desired variables. Especially variables capturing the small scale neighbourhood conditions are hard to find. If there are important explanatory variables missing from the model, the omitted variables are spatially autocorrelated and they are correlated with the explanatory variables included in the model, it can be shown that a spatial Durbin model is motivated. In the empirical application on new house price data from Helsinki in Finland, we find the motivation for a spatial Durbin model, we estimate the model and interpret the estimates for the summary measures of impacts. By the analysis we show that the model structure makes it possible to model and find small scale neighbourhood effects, when we know that they exist, but we are lacking proper variables to measure them.

Spatiotemporal Mapping of Interictal Spike Propagation: A Novel Methodology Applied to Pediatric Intracranial EEG Recordings

PubMed Central

Tomlinson, Samuel B.; Bermudez, Camilo; Conley, Chiara; Brown, Merritt W.; Porter, Brenda E.; Marsh, Eric D.

2016-01-01

Synchronized cortical activity is implicated in both normative cognitive functioning and many neurologic disorders. For epilepsy patients with intractable seizures, irregular synchronization within the epileptogenic zone (EZ) is believed to provide the network substrate through which seizures initiate and propagate. Mapping the EZ prior to epilepsy surgery is critical for detecting seizure networks in order to achieve postsurgical seizure control. However, automated techniques for characterizing epileptic networks have yet to gain traction in the clinical setting. Recent advances in signal processing and spike detection have made it possible to examine the spatiotemporal propagation of interictal spike discharges across the epileptic cortex. In this study, we present a novel methodology for detecting, extracting, and visualizing spike propagation and demonstrate its potential utility as a biomarker for the EZ. Eighteen presurgical intracranial EEG recordings were obtained from pediatric patients ultimately experiencing favorable (i.e., seizure-free, n = 9) or unfavorable (i.e., seizure-persistent, n = 9) surgical outcomes. Novel algorithms were applied to extract multichannel spike discharges and visualize their spatiotemporal propagation. Quantitative analysis of spike propagation was performed using trajectory clustering and spatial autocorrelation techniques. Comparison of interictal propagation patterns revealed an increase in trajectory organization (i.e., spatial autocorrelation) among Sz-Free patients compared with Sz-Persist patients. The pathophysiological basis and clinical implications of these findings are considered. PMID:28066315

Effects of Geographic Diversification on Risk Pooling to Mitigate Drought-Related Financial Losses for Water Utilities

NASA Astrophysics Data System (ADS)

Baum, Rachel; Characklis, Gregory W.; Serre, Marc L.

2018-04-01

As the costs and regulatory barriers to new water supply development continue to rise, drought management strategies have begun to rely more heavily on temporary conservation measures. While these measures are effective, they often lead to intermittent and unpredictable reductions in revenues that are financially disruptive to water utilities, raising concerns over lower credit ratings and higher rates of borrowing for this capital intensive sector. Consequently, there is growing interest in financial risk management strategies that reduce utility vulnerabilities. This research explores the development of financial index insurance designed to compensate a utility for drought-related losses. The focus is on analyzing candidate hydrologic indices that have the potential to be used by utilities across the US, increasing the potential for risk pooling, which would offer the possibility of both lower risk management costs and more widespread implementation. This work first analyzes drought-related financial risks for 315 publicly operated water utilities across the country and examines the effectiveness of financial contracts based on several indices both in terms of their correlation with utility revenues and their spatial autocorrelation across locations. Hydrologic-based index insurance contracts are then developed and tested over a 120 year period. Results indicate that risk pooling, even under conditions in which droughts are subject to some level of spatial autocorrelation, has the potential to significantly reduce the cost of managing financial risk.

Pulse shape measurements using single shot-frequency resolved optical gating for high energy (80 J) short pulse (600 fs) laser

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

Palaniyappan, S.; Johnson, R.; Shimada, T.

2010-10-15

Relevant to laser based electron/ion accelerations, a single shot second harmonic generation frequency resolved optical gating (FROG) system has been developed to characterize laser pulses (80 J, {approx}600 fs) incident on and transmitted through nanofoil targets, employing relay imaging, spatial filter, and partially coated glass substrates to reduce spatial nonuniformity and B-integral. The device can be completely aligned without using a pulsed laser source. Variations of incident pulse shape were measured from durations of 613 fs (nearly symmetric shape) to 571 fs (asymmetric shape with pre- or postpulse). The FROG measurements are consistent with independent spectral and autocorrelation measurements.

Spatial and seasonal variations of leaf area index (LAI) in subtropical secondary forests related to floristic composition and stand characters

NASA Astrophysics Data System (ADS)

Zhu, Wenjuan; Xiang, Wenhua; Pan, Qiong; Zeng, Yelin; Ouyang, Shuai; Lei, Pifeng; Deng, Xiangwen; Fang, Xi; Peng, Changhui

2016-07-01

Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana-Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber-Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber-C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process.

PubMed

Wilson, Lorna R M; Hopcraft, Keith I

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process

NASA Astrophysics Data System (ADS)

Wilson, Lorna R. M.; Hopcraft, Keith I.

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Effects of urban form on the urban heat island effect based on spatial regression model.

PubMed

Yin, Chaohui; Yuan, Man; Lu, Youpeng; Huang, Yaping; Liu, Yanfang

2018-09-01

The urban heat island (UHI) effect is becoming more of a concern with the accelerated process of urbanization. However, few studies have examined the effect of urban form on land surface temperature (LST) especially from an urban planning perspective. This paper used spatial regression model to investigate the effects of both land use composition and urban form on LST in Wuhan City, China, based on the regulatory planning management unit. Landsat ETM+ image data was used to estimate LST. Land use composition was calculated by impervious surface area proportion, vegetated area proportion, and water proportion, while urban form indicators included sky view factor (SVF), building density, and floor area ratio (FAR). We first tested for spatial autocorrelation of urban LST, which confirmed that a traditional regression method would be invalid. A spatial error model (SEM) was chosen because its parameters were better than a spatial lag model (SLM). The results showed that urban form metrics should be the focus for mitigation efforts of UHI effects. In addition, analysis of the relationship between urban form and UHI effect based on the regulatory planning management unit was helpful for promoting corresponding UHI effect mitigation rules in practice. Finally, the spatial regression model was recommended to be an appropriate method for dealing with problems related to the urban thermal environment. Results suggested that the impact of urbanization on the UHI effect can be mitigated not only by balancing various land use types, but also by optimizing urban form, which is even more effective. This research expands the scientific understanding of effects of urban form on UHI by explicitly analyzing indicators closely related to urban detailed planning at the level of regulatory planning management unit. In addition, it may provide important insights and effective regulation measures for urban planners to mitigate future UHI effects. Copyright © 2018 Elsevier B.V. All rights reserved.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Fang, Wen

2015-04-01

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also defined in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.

Spatial association of public sports facilities with body mass index in Korea.

PubMed

Han, Eun Jin; Kang, Kiyeon; Sohn, So Young

2018-05-07

Governments and also local councils create and enforce their own regional public health care plans for the problem of overweight and obesity in the population. Public sports facilities can help these plans. In this paper, we investigated the contribution of public sports facilities to the reduction of the obesity of local residents. We used the data obtained from the Fifth Korea National Health and Nutrition Examination Surveys; and measured the degree of obesity using body mass index (BMI). We conducted various spatial regression analyses including the global Moran's I test and local indicators of spatial autocorrelation analysis finding that there exists spatial dependence in the error term of spatial regression model for BMI. However, we also observed that the number of local public sports facilities is not significantly related to local BMI. This result can be caused by the low utilization ratio and an unbalanced spatial distribution of local public sports facilities. Based on our findings, we suggest that local councils need to improve the quality of public sports facilities encouraging the establishment of preferred types of pubic sports facilities.

Assessing Built Environment Walkability using Activity-Space Summary Measures.

PubMed

Tribby, Calvin P; Miller, Harvey J; Brown, Barbara B; Werner, Carol M; Smith, Ken R

There is increasing emphasis on active transportation, such as walking, in transportation planning as a sustainable form of mobility and in public health as a means of achieving recommended physical activity and better health outcomes. A research focus is the influence of the built environment on walking, with the ultimate goal of identifying environmental modifications that invite more walking. However, assessments of the built environment for walkability are typically at a spatially disaggregate level (such as street blocks) or at a spatially aggregate level (such as census block groups). A key issue is determining the spatial units for walkability measures so that they reflect potential walking behavior. This paper develops methods for assessing walkability within individual activity spaces : the geographic region accessible to an individual during a given walking trip. We first estimate street network-based activity spaces using the shortest path between known trip starting/ending points and a travel time budget that reflects potential alternative paths. Based on objective walkability measures of the street blocks, we use three summary measures for walkability within activity spaces: i) the average walkability score across block segments (representing the general level of walkability in the activity space); ii) the standard deviation (representing the walkability variation), and; iii) the network autocorrelation (representing the spatial coherence of the walkability pattern). We assess the method using data from an empirical study of built environment walkability and walking behavior in Salt Lake City, Utah, USA. We visualize and map these activity space summary measures to compare walkability among individuals' trips within their neighborhoods. We also compare summary measures for activity spaces versus census block groups, with the result that they agree less than half of the time.

Assessing Built Environment Walkability using Activity-Space Summary Measures

PubMed Central

Tribby, Calvin P.; Miller, Harvey J.; Brown, Barbara B.; Werner, Carol M.; Smith, Ken R.

2015-01-01

There is increasing emphasis on active transportation, such as walking, in transportation planning as a sustainable form of mobility and in public health as a means of achieving recommended physical activity and better health outcomes. A research focus is the influence of the built environment on walking, with the ultimate goal of identifying environmental modifications that invite more walking. However, assessments of the built environment for walkability are typically at a spatially disaggregate level (such as street blocks) or at a spatially aggregate level (such as census block groups). A key issue is determining the spatial units for walkability measures so that they reflect potential walking behavior. This paper develops methods for assessing walkability within individual activity spaces: the geographic region accessible to an individual during a given walking trip. We first estimate street network-based activity spaces using the shortest path between known trip starting/ending points and a travel time budget that reflects potential alternative paths. Based on objective walkability measures of the street blocks, we use three summary measures for walkability within activity spaces: i) the average walkability score across block segments (representing the general level of walkability in the activity space); ii) the standard deviation (representing the walkability variation), and; iii) the network autocorrelation (representing the spatial coherence of the walkability pattern). We assess the method using data from an empirical study of built environment walkability and walking behavior in Salt Lake City, Utah, USA. We visualize and map these activity space summary measures to compare walkability among individuals’ trips within their neighborhoods. We also compare summary measures for activity spaces versus census block groups, with the result that they agree less than half of the time. PMID:27213027

A consistent positive association between landscape simplification and insecticide use across the Midwestern US from 1997 through 2012

DOE PAGES

Meehan, Timothy D.; Gratton, Claudio

2015-10-27

During 2007, counties across the Midwestern US with relatively high levels of landscape simplification (i.e., widespread replacement of seminatural habitats with cultivated crops) had relatively high crop-pest abundances which, in turn, were associated with relatively high insecticide application. These results suggested a positive relationship between landscape simplification and insecticide use, mediated by landscape effects on crop pests or their natural enemies. A follow-up study, in the same region but using different statistical methods, explored the relationship between landscape simplification and insecticide use between 1987 and 2007, and concluded that the relationship varied substantially in sign and strength across years. Here,more » we explore this relationship from 1997 through 2012, using a single dataset and two different analytical approaches. We demonstrate that, when using ordinary least squares (OLS) regression, the relationship between landscape simplification and insecticide use is, indeed, quite variable over time. However, the residuals from OLS models show strong spatial autocorrelation, indicating spatial structure in the data not accounted for by explanatory variables, and violating a standard assumption of OLS. When modeled using spatial regression techniques, relationships between landscape simplification and insecticide use were consistently positive between 1997 and 2012, and model fits were dramatically improved. We argue that spatial regression methods are more appropriate for these data, and conclude that there remains compelling correlative support for a link between landscape simplification and insecticide use in the Midwestern US. We discuss the limitations of inference from this and related studies, and suggest improved data collection campaigns for better understanding links between landscape structure, crop-pest pressure, and pest-management practices.« less

A consistent positive association between landscape simplification and insecticide use across the Midwestern US from 1997 through 2012

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

Meehan, Timothy D.; Gratton, Claudio

During 2007, counties across the Midwestern US with relatively high levels of landscape simplification (i.e., widespread replacement of seminatural habitats with cultivated crops) had relatively high crop-pest abundances which, in turn, were associated with relatively high insecticide application. These results suggested a positive relationship between landscape simplification and insecticide use, mediated by landscape effects on crop pests or their natural enemies. A follow-up study, in the same region but using different statistical methods, explored the relationship between landscape simplification and insecticide use between 1987 and 2007, and concluded that the relationship varied substantially in sign and strength across years. Here,more » we explore this relationship from 1997 through 2012, using a single dataset and two different analytical approaches. We demonstrate that, when using ordinary least squares (OLS) regression, the relationship between landscape simplification and insecticide use is, indeed, quite variable over time. However, the residuals from OLS models show strong spatial autocorrelation, indicating spatial structure in the data not accounted for by explanatory variables, and violating a standard assumption of OLS. When modeled using spatial regression techniques, relationships between landscape simplification and insecticide use were consistently positive between 1997 and 2012, and model fits were dramatically improved. We argue that spatial regression methods are more appropriate for these data, and conclude that there remains compelling correlative support for a link between landscape simplification and insecticide use in the Midwestern US. We discuss the limitations of inference from this and related studies, and suggest improved data collection campaigns for better understanding links between landscape structure, crop-pest pressure, and pest-management practices.« less

Extracting the time scales of conformational dynamics from single-molecule single-photon fluorescence statistics.

PubMed

Shang, Jianyuan; Geva, Eitan

2007-04-26

The quenching rate of a fluorophore attached to a macromolecule can be rather sensitive to its conformational state. The decay of the corresponding fluorescence lifetime autocorrelation function can therefore provide unique information on the time scales of conformational dynamics. The conventional way of measuring the fluorescence lifetime autocorrelation function involves evaluating it from the distribution of delay times between photoexcitation and photon emission. However, the time resolution of this procedure is limited by the time window required for collecting enough photons in order to establish this distribution with sufficient signal-to-noise ratio. Yang and Xie have recently proposed an approach for improving the time resolution, which is based on the argument that the autocorrelation function of the delay time between photoexcitation and photon emission is proportional to the autocorrelation function of the square of the fluorescence lifetime [Yang, H.; Xie, X. S. J. Chem. Phys. 2002, 117, 10965]. In this paper, we show that the delay-time autocorrelation function is equal to the autocorrelation function of the square of the fluorescence lifetime divided by the autocorrelation function of the fluorescence lifetime. We examine the conditions under which the delay-time autocorrelation function is approximately proportional to the autocorrelation function of the square of the fluorescence lifetime. We also investigate the correlation between the decay of the delay-time autocorrelation function and the time scales of conformational dynamics. The results are demonstrated via applications to a two-state model and an off-lattice model of a polypeptide.

Population genetics of sporophytic self-incompatibility in Senecio squalidus L. (Asteraceae) II: a spatial autocorrelation approach to determining mating behaviour in the presence of low S allele diversity.

PubMed

Brennan, A C; Harris, S A; Hiscock, S J

2003-11-01

We recently estimated that as few as six S alleles represent the extent of S locus diversity in a British population of the self-incompatible (SI) coloniser Senecio squalidus (Oxford Ragwort). Despite the predicted constraints to mating imposed by such a low number of S alleles, S. squalidus maintains a strong sporophytic self-incompatibility (SSI) system and there is no evidence for a breakdown of SSI or any obvious negative reproductive consequences for this highly successful coloniser. The present paper assesses mating behaviour in an Oxford S. squalidus population through observations of its effect on spatial patterns of genetic diversity and thus the extent to which it is responsible for ameliorating the potentially detrimental reproductive consequences of low S allele diversity in British S. squalidus. A spatial autocorrelation (SA) treatment of S locus and allozyme polymorphism data for four loci indicates that mating events regularly occur at all the distance classes examined from 60 to 480 m throughout the entire sample population. Less SA is observed for S locus data than for allozyme data in accordance with the hypothesis that SSI and low diversity at the S locus are driving these large-scale mating events. The limited population structure at small distances of 60 m and less observed for SA analysis of the Me-2 locus and by F-statistics for all the allozyme data, is evidence of some local relatedness due to limited seed and pollen dispersal in S. squalidus. However, the overall impression of mating dynamics in this S. squalidus population is that of ample potential mating opportunities with many individuals at large population scales, indicating that reproductive success is not seriously affected by few S alleles available for mating interactions.

Effects of seed bank disturbance on the fine-scale genetic structure of populations of the rare shrub Grevillea macleayana.

PubMed

England, P R; Whelan, R J; Ayre, D J

2003-11-01

Dispersal in most plants is mediated by the movement of seeds and pollen, which move genes across the landscape differently. Grevillea macleayana is a rare, fire-dependent Australian shrub with large seeds lacking adaptations for dispersal; yet it produces inflorescences adapted to pollination by highly mobile vertebrates (eg birds). Interpreting fine-scale genetic structure in the light of these two processes is confounded by the recent imposition of anthropogenic disturbances with potentially contrasting genetic consequences: (1) the unusual foraging behaviour of exotic honeybees and 2. widespread disturbance of the soil-stored seedbank by road building and quarrying. To test for evidence of fine-scale genetic structure within G. macleayana populations and to test the prediction that such structure might be masked by disturbance of the seed bank, we sampled two sites in undisturbed habitat and compared their genetic structure with two sites that had been strongly affected by road building using a test for spatial autocorrelation of genotypes. High selfing levels inferred from genotypes at all four sites implies that pollen dispersal is limited. Consistent with this, we observed substantial spatial clustering of genes at 10 m or less in the two undisturbed populations and argue that this reflects the predicted effects of both high selfing levels and limited seed dispersal. In contrast, at the two sites disturbed by road building, spatial autocorrelation was weak. This suggests there has been mixing of the seed bank, counteracting the naturally low dispersal and elevated selfing due to honeybees. Pollination between near neighbours with reduced relatedness potentially has fitness consequences for G. macleayana in disturbed sites.

Fine-Scale Genetic Structure of Monilinia fructicola During Brown Rot Epidemics Within Individual Peach Tree Canopies.

PubMed

Everhart, S E; Scherm, H

2015-04-01

The purpose of this study was to determine the fine-scale genetic structure of populations of the brown rot pathogen Monilinia fructicola within individual peach tree canopies to better understand within-tree plant pathogen diversity and to complement previous work on spatiotemporal development of brown rot disease at the canopy level. Across 3 years in a total of six trees, we monitored disease development, collected isolates from every M. fructicola symptom during the course of the season, and created high-resolution three-dimensional maps of all symptom and isolate locations within individual canopies using an electromagnetic digitizer. Each canopy population (65 to 173 isolates per tree) was characterized using a set of 13 microsatellite markers and analyzed for evidence of spatial genetic autocorrelation among isolates during the epidemic phase of the disease. Results showed high genetic diversity (average uh=0.529) and high genotypic diversity (average D=0.928) within canopies. The percentage of unique multilocus genotypes within trees was greater for blossom blight isolates (78.2%) than for fruit rot isolates (51.3%), indicating a greater contribution of clonal reproduction during the preharvest epidemic. For fruit rot isolates, between 54.2 and 81.7% of isolates were contained in one to four dominant clonal genotypes per tree having at least 10 members. All six fruit rot populations showed positive and significant spatial genetic autocorrelation for distance classes between 0.37 and 1.48 m. Despite high levels of within-tree pathogen diversity, the contribution of locally available inoculum combined with short-distance dispersal is likely the main factor generating clonal population foci and associated spatial genetic clustering within trees.

Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes

PubMed Central

Huang, Jian; Frimpong, Emmanuel A.

2015-01-01

Understanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species’ rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs. PMID:26075902

Transform- and multi-domain deep learning for single-frame rapid autofocusing in whole slide imaging.

PubMed

Jiang, Shaowei; Liao, Jun; Bian, Zichao; Guo, Kaikai; Zhang, Yongbing; Zheng, Guoan

2018-04-01

A whole slide imaging (WSI) system has recently been approved for primary diagnostic use in the US. The image quality and system throughput of WSI is largely determined by the autofocusing process. Traditional approaches acquire multiple images along the optical axis and maximize a figure of merit for autofocusing. Here we explore the use of deep convolution neural networks (CNNs) to predict the focal position of the acquired image without axial scanning. We investigate the autofocusing performance with three illumination settings: incoherent Kohler illumination, partially coherent illumination with two plane waves, and one-plane-wave illumination. We acquire ~130,000 images with different defocus distances as the training data set. Different defocus distances lead to different spatial features of the captured images. However, solely relying on the spatial information leads to a relatively bad performance of the autofocusing process. It is better to extract defocus features from transform domains of the acquired image. For incoherent illumination, the Fourier cutoff frequency is directly related to the defocus distance. Similarly, autocorrelation peaks are directly related to the defocus distance for two-plane-wave illumination. In our implementation, we use the spatial image, the Fourier spectrum, the autocorrelation of the spatial image, and combinations thereof as the inputs for the CNNs. We show that the information from the transform domains can improve the performance and robustness of the autofocusing process. The resulting focusing error is ~0.5 µm, which is within the 0.8-µm depth-of-field range. The reported approach requires little hardware modification for conventional WSI systems and the images can be captured on the fly without focus map surveying. It may find applications in WSI and time-lapse microscopy. The transform- and multi-domain approaches may also provide new insights for developing microscopy-related deep-learning networks. We have made our training and testing data set (~12 GB) open-source for the broad research community.

A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka

PubMed Central

2011-01-01

Background The deprived physical environments present in slums are well-known to have adverse health effects on their residents. However, little is known about the health effects of the social environments in slums. Moreover, neighbourhood quantitative spatial analyses of the mental health status of slum residents are still rare. The aim of this paper is to study self-rated mental health data in several slums of Dhaka, Bangladesh, by accounting for neighbourhood social and physical associations using spatial statistics. We hypothesised that mental health would show a significant spatial pattern in different population groups, and that the spatial patterns would relate to spatially-correlated health-determining factors (HDF). Methods We applied a spatial epidemiological approach, including non-spatial ANOVA/ANCOVA, as well as global and local univariate and bivariate Moran's I statistics. The WHO-5 Well-being Index was used as a measure of self-rated mental health. Results We found that poor mental health (WHO-5 scores < 13) among the adult population (age ≥15) was prevalent in all slum settlements. We detected spatially autocorrelated WHO-5 scores (i.e., spatial clusters of poor and good mental health among different population groups). Further, we detected spatial associations between mental health and housing quality, sanitation, income generation, environmental health knowledge, education, age, gender, flood non-affectedness, and selected properties of the natural environment. Conclusions Spatial patterns of mental health were detected and could be partly explained by spatially correlated HDF. We thereby showed that the socio-physical neighbourhood was significantly associated with health status, i.e., mental health at one location was spatially dependent on the mental health and HDF prevalent at neighbouring locations. Furthermore, the spatial patterns point to severe health disparities both within and between the slums. In addition to examining health outcomes, the methodology used here is also applicable to residuals of regression models, such as helping to avoid violating the assumption of data independence that underlies many statistical approaches. We assume that similar spatial structures can be found in other studies focussing on neighbourhood effects on health, and therefore argue for a more widespread incorporation of spatial statistics in epidemiological studies. PMID:21599932

Mapping mHealth (mobile health) and mobile penetrations in sub-Saharan Africa for strategic regional collaboration in mHealth scale-up: an application of exploratory spatial data analysis.

PubMed

Lee, Seohyun; Cho, Yoon-Min; Kim, Sun-Young

2017-08-22

Mobile health (mHealth), a term used for healthcare delivery via mobile devices, has gained attention as an innovative technology for better access to healthcare and support for performance of health workers in the global health context. Despite large expansion of mHealth across sub-Saharan Africa, regional collaboration for scale-up has not made progress since last decade. As a groundwork for strategic planning for regional collaboration, the study attempted to identify spatial patterns of mHealth implementation in sub-Saharan Africa using an exploratory spatial data analysis. In order to obtain comprehensive data on the total number of mHelath programs implemented between 2006 and 2016 in each of the 48 sub-Saharan Africa countries, we performed a systematic data collection from various sources, including: the WHO eHealth Database, the World Bank Projects & Operations Database, and the USAID mHealth Database. Additional spatial analysis was performed for mobile cellular subscriptions per 100 people to suggest strategic regional collaboration for improving mobile penetration rates along with the mHealth initiative. Global Moran's I and Local Indicator of Spatial Association (LISA) were calculated for mHealth programs and mobile subscriptions per 100 population to investigate spatial autocorrelation, which indicates the presence of local clustering and spatial disparities. From our systematic data collection, the total number of mHealth programs implemented in sub-Saharan Africa between 2006 and 2016 was 487 (same programs implemented in multiple countries were counted separately). Of these, the eastern region with 17 countries and the western region with 16 countries had 287 and 145 mHealth programs, respectively. Despite low levels of global autocorrelation, LISA enabled us to detect meaningful local clusters. Overall, the eastern part of sub-Saharan Africa shows high-high association for mHealth programs. As for mobile subscription rates per 100 population, the northern area shows extensive low-low association. This study aimed to shed some light on the potential for strategic regional collaboration for scale-up of mHealth and mobile penetration. Firstly, countries in the eastern area with much experience can take the lead role in pursuing regional collaboration for mHealth programs in sub-Saharan Africa. Secondly, collective effort in improving mobile penetration rates for the northern area is recommended.

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

PubMed Central

2012-01-01

Background Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e.g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country. PMID:22410079

The influence of processor focus on speckle correlation statistics for a Shuttle imaging radar scene of Hurricane Josephine

NASA Technical Reports Server (NTRS)

Tilley, David G.

1988-01-01

The surface wave field produced by Hurricane Josephine was imaged by the L-band SAR aboard the Challenger on October 12, 1984. Exponential trends found in the two-dimensional autocorrelations of speckled image data support an equilibrium theory model of sea surface hydrodynamics. The notions of correlated specular reflection, surface coherence, optimal Doppler parameterization and spatial resolution are discussed within the context of a Poisson-Rayleigh statistical model of the SAR imaging process.

Total ozone trend significance from space time variability of daily Dobson data

NASA Technical Reports Server (NTRS)

Wilcox, R. W.

1981-01-01

Estimates of standard errors of total ozone time and area means, as derived from ozone's natural temporal and spatial variability and autocorrelation in middle latitudes determined from daily Dobson data are presented. Assessing the significance of apparent total ozone trends is equivalent to assessing the standard error of the means. Standard errors of time averages depend on the temporal variability and correlation of the averaged parameter. Trend detectability is discussed, both for the present network and for satellite measurements.

VizieR Online Data Catalog: W49A JCMT Spectral Legacy Survey spectroscopy (Nagy+, 2015)

NASA Astrophysics Data System (ADS)

Nagy, Z.; van der Tak, F. F. S.; Fuller, G. A.; Plume, R.

2015-08-01

We carried out the SLS observations using the 16-receptor (spatial pixel) Heterodyne Array Receiver Programme B (HARP-B, 325-375GHz) and the Auto-Correlation Spectral Imaging System (ACSIS) correlator at the James Clerk Maxwell Telescope1 (JCMT) on Mauna Kea, Hawaii. We carried out the observations in jiggle position switch mode, sampled every 7.5" for a 2x2-arcmin field centered on RA=19:10:13.4; DE=09:06:14 (J2000). (1 data file).

Evaluating collective significance of climatic trends: A comparison of methods on synthetic data

NASA Astrophysics Data System (ADS)

Huth, Radan; Dubrovský, Martin

2017-04-01

The common approach to determine whether climatic trends are significantly different from zero is to conduct individual (local) tests at each single site (station or gridpoint). Whether the number of sites where the trends are significantly non-zero can or cannot occur by random, is almost never evaluated in trend studies. That is, collective (global) significance of trends is ignored. We compare three approaches to evaluating collective statistical significance of trends at a network of sites, using the following statistics: (i) the number of successful local tests (a successful test means here a test in which the null hypothesis of no trend is rejected); this is a standard way of assessing collective significance in various applications in atmospheric sciences; (ii) the smallest p-value among the local tests (Walker test); and (iii) the counts of positive and negative trends regardless of their magnitudes and local significance. The third approach is a new procedure that we propose; the rationale behind it is that it is reasonable to assume that the prevalence of one sign of trends at individual sites is indicative of a high confidence in the trend not being zero, regardless of the (in)significance of individual local trends. A potentially large amount of information contained in trends that are not locally significant, which are typically deemed irrelevant and neglected, is thus not lost and is retained in the analysis. In this contribution we examine the feasibility of the proposed way of significance testing on synthetic data, produced by a multi-site stochastic generator, and compare it with the two other ways of assessing collective significance, which are well established now. The synthetic dataset, mimicking annual mean temperature on an array of stations (or gridpoints), is constructed assuming a given statistical structure characterized by (i) spatial separation (density of the station network), (ii) local variance, (iii) temporal and spatial autocorrelations, and (iv) the trend magnitude. The probabilistic distributions of the three test statistics (null distributions) and critical values of the tests are determined from multiple realizations of the synthetic dataset, in which no trend is imposed at each site (that is, any trend is a result of random fluctuations only). The procedure is then evaluated by determining the type II error (the probability of a false detection of a trend) in the presence of a trend with a known magnitude, for which the synthetic dataset with an imposed spatially uniform non-zero trend is used. A sensitivity analysis is conducted for various combinations of the trend magnitude and spatial autocorrelation.

Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function.

PubMed

Razifar, Pasha; Lubberink, Mark; Schneider, Harald; Långström, Bengt; Bengtsson, Ewert; Bergström, Mats

2005-05-13

BACKGROUND: Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known. METHODS: In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images. RESULTS: We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study. CONCLUSION: With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.

Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

PubMed

Mabit, L; Bernard, C

2007-01-01

After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

NASA Astrophysics Data System (ADS)

Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

2017-04-01

Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the possibility to perform cross-validation at the level of some grouping structure. As an example, in remote sensing of agricultural land uses, pixels from the same field contain nearly identical information and will thus be jointly placed in either the test set or the training set. Other spatial sampling resampling strategies are already available and can be extended by the user.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

PubMed Central

Kierepka, E M; Latch, E K

2016-01-01

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3–5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics. PMID:26243136

A population-based study of prevalence trends and geospatial analysis of hypospadias and cryptorchidism compared with non-endocrine mediated congenital anomalies.

PubMed

Lane, Ciaran; Boxall, James; MacLellan, Dawn; Anderson, Peter A; Dodds, Linda; Romao, Rodrigo L P

2017-06-01

Several reports have suggested an increase in the prevalence of hypospadias and cryptorchidism over the last few decades. Endocrine disruption caused by exposure to environmental chemicals has been postulated as a possible cause. The objectives of our study were: 1) to determine whether the prevalence of hypospadias and cryptorchidism is increasing compared with other congenital anomalies not known to be mediated by endocrine factors; and 2) to perform a geospatial analysis of these congenital malformations looking for clustering that could offer insight into environmental risk factors. Data were obtained from the Nova Scotia ATLEE Perinatal Database containing the perinatal records of all live births in Nova Scotia, Canada since 1988. Records from 1988 to 2013 defined the study cohort. Overall prevalence rates and prevalence trends by year were calculated for hypospadias, cryptorchidism, gastroschisis, and clubfoot. County of residence was collected and spatial autocorrelation testing for clustering was performed for each of the congenital anomalies. There were 258,147 live births during the study period. Overall prevalence rates for the four malformations over the study period were: hypospadias 78 per 10,000 male births, cryptorchidism 75 per 10,000 male births, clubfoot 24 per 10,000 total births, and gastroschisis 4 per 10,000 total births. Incidence rate ratios per year for hypospadias, cryptorchidism, clubfoot, and gastroschisis were 1.00 (0.99-1.01), 0.99 (0.98-1.00), 0.98 (0.97-0.99), and 1.04 (1.04-1.07), respectively. During the study period, the prevalence rates in the region were unchanged for hypospadias, slightly reduced for cryptorchidism and clubfoot, and rising for gastroschisis (Figure). Spatial autocorrelation testing revealed statistically significant clustering for hypospadias (p = 0.03) and cryptorchidism (p = 0.03), while no spatial autocorrelation was observed for the other malformations. Contrary to previous studies we show that hypospadias and cryptorchidism prevalence rates are not increasing over time in our region. Nonetheless, rates for these conditions in our area are high compared with other regions of the world. Local clustering of these congenital anomalies without clustering of the control, non-endocrine mediated congenital malformations supports a possible unique spatial distribution associated with environmental exposure. The hotspots identified for hypospadias and cryptorchidism are associated with intense agricultural activity. Our study found no increase in hypospadias and cryptorchidism prevalence over a 26-year period compared with other congenital anomalies not known to be associated with endocrine factors. Geospatial analysis supports high clustering for hypospadias and cryptorchidism in areas of intense agricultural activity. Copyright © 2017 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Probabilistic density function method for nonlinear dynamical systems driven by colored noise

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

Barajas-Solano, David A.; Tartakovsky, Alexandre M.

2016-05-01

We present a probability density function (PDF) method for a system of nonlinear stochastic ordinary differential equations driven by colored noise. The method provides an integro-differential equation for the temporal evolution of the joint PDF of the system's state, which we close by means of a modified Large-Eddy-Diffusivity-type closure. Additionally, we introduce the generalized local linearization (LL) approximation for deriving a computable PDF equation in the form of the second-order partial differential equation (PDE). We demonstrate the proposed closure and localization accurately describe the dynamics of the PDF in phase space for systems driven by noise with arbitrary auto-correlation time.more » We apply the proposed PDF method to the analysis of a set of Kramers equations driven by exponentially auto-correlated Gaussian colored noise to study the dynamics and stability of a power grid.« less

Nonequilibrium relaxations within the ground-state manifold in the antiferromagnetic Ising model on a triangular lattice.

PubMed

Kim, Eunhye; Lee, Sung Jong; Kim, Bongsoo

2007-02-01

We present an extensive Monte Carlo simulation study on the nonequilibrium kinetics of triangular antiferromagnetic Ising model within the ground state ensemble which consists of sectors, each of which is characterized by a unique value of the string density p through a dimer covering method. Building upon our recent work [Phys. Rev. E 68, 066127 (2003)] where we considered the nonequilibrium relaxation observed within the dominant sector with p=2/3, we here focus on the nonequilibrium kinetics within the minor sectors with p<2/3. The initial configurations are chosen as those in which the strings are straight and evenly distributed. In the minor sectors, we observe a characteristic spatial anisotropy in both equilibrium and nonequilibrium spatial correlations. We observe emergence of a critical relaxation region (in the spatial and temporal domain) which grows as p deviates from p=2/3. Spatial anisotropy appears in the equilibrium spatial correlation with the characteristic length scale xi(e,V)(p) diverging with vanishing string density as xi(e,V)(p) approximately p(-2) along the vertical direction, while along the horizontal direction the spatial length scale diverges as xi(e,H) approximately p(-1). Analytic forms for the anisotropic equilibrium correlation functions are given. We also find that the spin autocorrelation function A(t) shows a simple scaling behavior A(t)=A(t/tau(A)(p)), where the time scale tau(A)(p) shows a power-law divergence with vanishing p as tau(A)(p) approximately p(-phi) with phi approximately or equal to 4. These features can be understood in terms of random walk nature of the fluctuations of the strings within the typical separation between neighboring strings.

A new estimator method for GARCH models

NASA Astrophysics Data System (ADS)

Onody, R. N.; Favaro, G. M.; Cazaroto, E. R.

2007-06-01

The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a central role in empirical finance. The Markovian GARCH (1, 1) model has only 3 control parameters and a much discussed question is how to estimate them when a series of some financial asset is given. Besides the maximum likelihood estimator technique, there is another method which uses the variance, the kurtosis and the autocorrelation time to determine them. We propose here to use the standardized 6th moment. The set of parameters obtained in this way produces a very good probability density function and a much better time autocorrelation function. This is true for both studied indexes: NYSE Composite and FTSE 100. The probability of return to the origin is investigated at different time horizons for both Gaussian and Laplacian GARCH models. In spite of the fact that these models show almost identical performances with respect to the final probability density function and to the time autocorrelation function, their scaling properties are, however, very different. The Laplacian GARCH model gives a better scaling exponent for the NYSE time series, whereas the Gaussian dynamics fits better the FTSE scaling exponent.

Networks of volatility spillovers among stock markets

NASA Astrophysics Data System (ADS)

Baumöhl, Eduard; Kočenda, Evžen; Lyócsa, Štefan; Výrost, Tomáš

2018-01-01

In our network analysis of 40 developed, emerging and frontier stock markets during the 2006-2014 period, we describe and model volatility spillovers during both the global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We document the presence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market - volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness, which is measured by high spatial autocorrelation. This finding is confirmed by spatial regression models showing that indirect effects are much stronger than direct effects; i.e., market-related changes in 'neighboring' markets (within a network) affect volatility spillovers more than changes in the given market alone, suggesting that spatial effects simply cannot be ignored when modeling stock market relationships. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.

Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions.

PubMed

Wilson, Adam M; Jetz, Walter

2016-03-01

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

NASA Technical Reports Server (NTRS)

Browder, J. A.; May, L. N., Jr.; Rosenthal, A.; Baumann, R. H.; Gosselink, J. G.

1986-01-01

LANDSAT thematic mapper (TM) data are being used to refine and validate a stochastic spatial computer model to be applied to coastal resource management problems in Louisiana. Two major aspects of the research are: (1) the measurement of area of land (or emergent vegetation) and water and the length of the interface between land and water in TM imagery of selected coastal wetlands (sample marshes); and (2) the comparison of spatial patterns of land and water in the sample marshes of the imagery to that in marshes simulated by a computer model. In addition to activities in these two areas, the potential use of a published autocorrelation statistic is analyzed.

Governance and Regional Variation of Homicide Rates: Evidence From Cross-National Data.

PubMed

Cao, Liqun; Zhang, Yan

2017-01-01

Criminological theories of cross-national studies of homicide have underestimated the effects of quality governance of liberal democracy and region. Data sets from several sources are combined and a comprehensive model of homicide is proposed. Results of the spatial regression model, which controls for the effect of spatial autocorrelation, show that quality governance, human development, economic inequality, and ethnic heterogeneity are statistically significant in predicting homicide. In addition, regions of Latin America and non-Muslim Sub-Saharan Africa have significantly higher rates of homicides ceteris paribus while the effects of East Asian countries and Islamic societies are not statistically significant. These findings are consistent with the expectation of the new modernization and regional theories. © The Author(s) 2015.

The rubber plantation environment and Lassa fever epidemics in Liberia, 2008-2012: a spatial regression.

PubMed

Olugasa, Babasola O; Dogba, John B; Ogunro, Bamidele; Odigie, Eugene A; Nykoi, Jomah; Ojo, Johnson F; Taiwo, Olalekan; Kamara, Abraham; Mulbah, Charles K; Fasunla, Ayotunde J

2014-10-01

As Lassa fever continues to be a public health challenge in West Africa, it is critical to produce good maps of its risk pattern for use in active surveillance and control intervention. We identified eight spatial features related to the rubber plantation environment and used them as explanatory variables for Lassa fever (LF) outbreaks on the Uniroyal Liberian Agricultural Company (LAC) rubber plantation environment in Grand Bassa County, Liberia. We computed classical and spatial lag regression models on all spatial features, including proximity of residential camp to rubber tree-edge, main road in the plantation, LAC hospital, rice farmland, household refuse dump, human population density, post-harvest storage density of rice and density of rodent deterrent on rice storage. We found significant (p=0.0024) spatial autocorrelation between LF cases and the spatial features we have considered. We concluded that the rubber plantation environment influenced Mastomys species' breeding and transmission of Lassa virus along spatial scale to humans. The risk factors identified in this study offered a baseline for more effective surveillance and control of LF in the post-civil conflict Liberia. Copyright © 2014 Elsevier Ltd. All rights reserved.

The pyramid system for multiscale raster analysis

USGS Publications Warehouse

De Cola, L.; Montagne, N.

1993-01-01

Geographical research requires the management and analysis of spatial data at multiple scales. As part of the U.S. Geological Survey's global change research program a software system has been developed that reads raster data (such as an image or digital elevation model) and produces a pyramid of aggregated lattices as well as various measurements of spatial complexity. For a given raster dataset the system uses the pyramid to report: (1) mean, (2) variance, (3) a spatial autocorrelation parameter based on multiscale analysis of variance, and (4) a monofractal scaling parameter based on the analysis of isoline lengths. The system is applied to 1-km digital elevation model (DEM) data for a 256-km2 region of central California, as well as to 64 partitions of the region. PYRAMID, which offers robust descriptions of data complexity, also is used to describe the behavior of topographic aspect with scale. ?? 1993.

Siting MSW landfill using weighted linear combination and analytical hierarchy process (AHP) methodology in GIS environment (case study: Karaj).

PubMed

Moeinaddini, Mazaher; Khorasani, Nematollah; Danehkar, Afshin; Darvishsefat, Ali Asghar; Zienalyan, Mehdi

2010-05-01

Selection of landfill site is a complex process and needs many diverse criteria. The purpose of this paper is to evaluate the suitability of the studied site as landfill for MSW in Karaj. Using weighted linear combination (WLC) method and spatial cluster analysis (SCA), suitable sites for allocation of landfill for a 20-year period were identified. For analyzing spatial auto-correlation of the land suitability map layer (LSML), Maron's I was used. Finally, using the analytical hierarchy process (AHP), the most preferred alternative for the landfill siting was identified. Main advantages of AHP are: relative ease of handling multiple criteria, easy to understand and effective handling of both qualitative and quantitative data. As a result, 6% of the study area is suitable for landfill siting and third alternative was identified as the most preferred for siting MSW landfill by AHP. The ranking of alternatives were obtained only by applying the WLC approach showed different results from the AHP. The WLC should be used only for the identification of alternatives and the AHP is used for prioritization. We suggest the employed procedure for other similar regions. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

PubMed

Pratt, Bethany; Chang, Heejun

2012-03-30

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. Copyright © 2012 Elsevier B.V. All rights reserved.

Using GIS to analyze animal movements in the marine environment

USGS Publications Warehouse

Hooge, Philip N.; Eichenlaub, William M.; Solomon, Elizabeth K.; Kruse, Gordon H.; Bez, Nicolas; Booth, Anthony; Dorn, Martin W.; Hills, Susan; Lipcius, Romuald N.; Pelletier, Dominique; Roy, Claude; Smith, Stephen J.; Witherell, David B.

2001-01-01

Advanced methods for analyzing animal movements have been little used in the aquatic research environment compared to the terrestrial. In addition, despite obvious advantages of integrating geographic information systems (GIS) with spatial studies of animal movement behavior, movement analysis tools have not been integrated into GIS for either aquatic or terrestrial environments. We therefore developed software that integrates one of the most commonly used GIS programs (ArcView®) with a large collection of animal movement analysis tools. This application, the Animal Movement Analyst Extension (AMAE), can be loaded as an extension to ArcView® under multiple operating system platforms (PC, Unix, and Mac OS). It contains more than 50 functions, including parametric and nonparametric home range analyses, random walk models, habitat analyses, point and circular statistics, tests of complete spatial randomness, tests for autocorrelation and sample size, point and line manipulation tools, and animation tools. This paper describes the use of these functions in analyzing animal location data; some limited examples are drawn from a sonic-tracking study of Pacific halibut (Hippoglossus stenolepis) in Glacier Bay, Alaska. The extension is available on the Internet at www.absc.usgs.gov/glba/gistools/index.htm.

Interferometric Near-Infrared Spectroscopy (iNIRS) for determination of optical and dynamical properties of turbid media

PubMed Central

Borycki, Dawid; Kholiqov, Oybek; Chong, Shau Poh; Srinivasan, Vivek J.

2016-01-01

We introduce and implement interferometric near-infrared spectroscopy (iNIRS), which simultaneously extracts optical and dynamical properties of turbid media through analysis of a spectral interference fringe pattern. The spectral interference fringe pattern is measured using a Mach-Zehnder interferometer with a frequency-swept narrow linewidth laser. Fourier analysis of the detected signal is used to determine time-of-flight (TOF)-resolved intensity, which is then analyzed over time to yield TOF-resolved intensity autocorrelations. This approach enables quantification of optical properties, which is not possible in conventional, continuous-wave near-infrared spectroscopy (NIRS). Furthermore, iNIRS quantifies scatterer motion based on TOF-resolved autocorrelations, which is a feature inaccessible by well-established diffuse correlation spectroscopy (DCS) techniques. We prove this by determining TOF-resolved intensity and temporal autocorrelations for light transmitted through diffusive fluid phantoms with optical thicknesses of up to 55 reduced mean free paths (approximately 120 scattering events). The TOF-resolved intensity is used to determine optical properties with time-resolved diffusion theory, while the TOF-resolved intensity autocorrelations are used to determine dynamics with diffusing wave spectroscopy. iNIRS advances the capabilities of diffuse optical methods and is suitable for in vivo tissue characterization. Moreover, iNIRS combines NIRS and DCS capabilities into a single modality. PMID:26832264

Non-Markovian spin-resolved counting statistics and an anomalous relation between autocorrelations and cross correlations in a three-terminal quantum dot

NASA Astrophysics Data System (ADS)

Luo, JunYan; Yan, Yiying; Huang, Yixiao; Yu, Li; He, Xiao-Ling; Jiao, HuJun

2017-01-01

We investigate the noise correlations of spin and charge currents through an electron spin resonance (ESR)-pumped quantum dot, which is tunnel coupled to three electrodes maintained at an equivalent chemical potential. A recursive scheme is employed with inclusion of the spin degrees of freedom to account for the spin-resolved counting statistics in the presence of non-Markovian effects due to coupling with a dissipative heat bath. For symmetric spin-up and spin-down tunneling rates, an ESR-induced spin flip mechanism generates a pure spin current without an accompanying net charge current. The stochastic tunneling of spin carriers, however, produces universal shot noises of both charge and spin currents, revealing the effective charge and spin units of quasiparticles in transport. In the case of very asymmetric tunneling rates for opposite spins, an anomalous relationship between noise autocorrelations and cross correlations is revealed, where super-Poissonian autocorrelation is observed in spite of a negative cross correlation. Remarkably, with strong dissipation strength, non-Markovian memory effects give rise to a positive cross correlation of the charge current in the absence of a super-Poissonian autocorrelation. These unique noise features may offer essential methods for exploiting internal spin dynamics and various quasiparticle tunneling processes in mesoscopic transport.

Multi-criteria decision analysis and spatial statistic: an approach to determining human vulnerability to vector transmission of Trypanosoma cruzi.

PubMed

Montenegro, Diego; Cunha, Ana Paula da; Ladeia-Andrade, Simone; Vera, Mauricio; Pedroso, Marcel; Junqueira, Angela

2017-10-01

Chagas disease (CD), caused by the protozoan Trypanosoma cruzi, is a neglected human disease. It is endemic to the Americas and is estimated to have an economic impact, including lost productivity and disability, of 7 billion dollars per year on average. To assess vulnerability to vector-borne transmission of T. cruzi in domiciliary environments within an area undergoing domiciliary vector interruption of T. cruzi in Colombia. Multi-criteria decision analysis [preference ranking method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive assistance (GAIA) methods] and spatial statistics were performed on data from a socio-environmental questionnaire and an entomological survey. In the construction of multi-criteria descriptors, decision-making processes and indicators of five determinants of the CD vector pathway were summarily defined, including: (1) house indicator (HI); (2) triatominae indicator (TI); (3) host/reservoir indicator (Ho/RoI); (4) ecotope indicator (EI); and (5) socio-cultural indicator (S-CI). Determination of vulnerability to CD is mostly influenced by TI, with 44.96% of the total weight in the model, while the lowest contribution was from S-CI, with 7.15%. The five indicators comprise 17 indices, and include 78 of the original 104 priority criteria and variables. The PROMETHEE and GAIA methods proved very efficient for prioritisation and quantitative categorisation of socio-environmental determinants and for better determining which criteria should be considered for interrupting the man-T. cruzi-vector relationship in endemic areas of the Americas. Through the analysis of spatial autocorrelation it is clear that there is a spatial dependence in establishing categories of vulnerability, therefore, the effect of neighbors' setting (border areas) on local values should be incorporated into disease management for establishing programs of surveillance and control of CD via vector. The study model proposed here is flexible and can be adapted to various eco-epidemiological profiles and is suitable for focusing anti-T. cruzi serological surveillance programs in vulnerable human populations.

An evaluation of random analysis methods for the determination of panel damping

NASA Technical Reports Server (NTRS)

Bhat, W. V.; Wilby, J. F.

1972-01-01

An analysis is made of steady-state and non-steady-state methods for the measurement of panel damping. Particular emphasis is placed on the use of random process techniques in conjunction with digital data reduction methods. The steady-state methods considered use the response power spectral density, response autocorrelation, excitation-response crosspower spectral density, or single-sided Fourier transform (SSFT) of the response autocorrelation function. Non-steady-state methods are associated mainly with the use of rapid frequency sweep excitation. Problems associated with the practical application of each method are evaluated with specific reference to the case of a panel exposed to a turbulent airflow, and two methods, the power spectral density and the single-sided Fourier transform methods, are selected as being the most suitable. These two methods are demonstrated experimentally, and it is shown that the power spectral density method is satisfactory under most conditions, provided that appropriate corrections are applied to account for filter bandwidth and background noise errors. Thus, the response power spectral density method is recommended for the measurement of the damping of panels exposed to a moving airflow.

A single European aspen (Populus tremula) tree individual may potentially harbour dozens of Cenococcum geophilum ITS genotypes and hundreds of species of ectomycorrhizal fungi.

PubMed

Bahram, Mohammad; Põlme, Sergei; Kõljalg, Urmas; Tedersoo, Leho

2011-02-01

Ectomycorrhizal fungi (EcMF) form diverse communities and link different host plants into mycorrhizal networks, yet little is known about the magnitude of mycobiont diversity of a single tree individual. This study addresses species richness and spatial structure of EcMF in the root system of a single European aspen (Populus tremula) individual in an old-growth boreal mixed forest ecosystem in Estonia. Combining morphological and molecular identification methods for both plant and fungi, 122 species of EcMF were recovered from 103 root samples of the single tree. Richness estimators predicted the total EcMF richness to range from 182 to 207 species, reflecting the observation of 62.3% singletons and doubletons within the community. Fine-scale genetic diversity in Cenococcum geophilum indicates the presence of 23 internal transcribed spacer genotypes. EcMF community was significantly spatially autocorrelated only at the lineage level up to 3 m distance, but not at the species level. Proximity of other hosts had a significant effect on the spatial distribution of EcMF lineages. This study demonstrates that a single tree may host as many EcMF species and individuals as recovered on multiple hosts in diverse communities over larger areas. © 2010 Federation of European Microbiological Societies. Published by Blackwell Publishing Ltd. All rights reserved.

Inference for local autocorrelations in locally stationary models.

PubMed

Zhao, Zhibiao

2015-04-01

For non-stationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a "V" shape during 1910-1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Mapping spatial patterns of stream power and channel change along a gravel-bed river in northern Yellowstone

NASA Astrophysics Data System (ADS)

Lea, Devin M.; Legleiter, Carl J.

2016-01-01

Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing or field surveys. This study sought to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8-km reach. Aerial photographs from 1994 to 2012 and ground-based surveys were used to develop a locational probability map and morphologic sediment budget to assess lateral channel mobility and changes in net sediment flux. A drainage area-to-discharge relationship and DEM developed from LiDAR data were used to obtain the discharge and slope values needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of sediment sources and sinks. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volumetric change in each time increment. Collectively, we refer to these methods as the stream power gradient (SPG) framework. The results of this study were compromised by methodological limitations of the SPG framework and revealed some complications likely to arise when applying this framework to small, wandering, gravel-bed rivers. Correlations between stream power gradients and sediment flux were generally weak, highlighting the inability of relatively simple statistical approaches to link sub-budget cell-scale sediment dynamics to larger-scale driving forces such as stream power gradients. Improving the moderate spatial resolution techniques used in this study and acquiring very-high resolution data from recently developed methods in fluvial remote sensing could help improve understanding of the spatial organization of stream power, sediment transport, and channel change in dynamic natural rivers.

First results on quiet and magnetic granulation from SOUP

NASA Technical Reports Server (NTRS)

Title, A. M.; Tarbell, T. D.; Acton, L.; Duncan, D.; Ferguson, S. H.; Finch, M.; Frank, Z.; Kelly, G.; Lindgren, R.; Morrill, M.

1987-01-01

The flight of Solar Optical Universal Polarimeter (SOUP) on Spacelab 2 allowed the collection of time sequences of diffraction limited (0.5 arc sec) granulation images with excellent pointing (0.003 arc sec) and completely free of the distortion that plagues groundbased images. The p-mode oscillations are clearly seen in the data. Using Fourier transforms in the temporal and spatial domain, it was shown that the p-modes dominate the autocorrelation lifetime in magnetic regions. When these oscillations are removed the autocorrelation lifetime is found to be 500 sec in quiet and 950 sec in magnetic regions. In quiet areas exploding granules are seen to be common. It is speculated that a significant fraction of granule lifetimes are terminated by nearby explosions. Using local correlation tracking techniques it was able to measure horizontal displacements, and thus transverse velocities, in the magnetic field. In quiet sun it is possible to detect both super and mesogranulation. Horizontal velocities are as great as 1000 m/s and the average velocity is 400 m/s. In magnetic regions horizontal velocities are much less, about 100 m/s.

First results on quiet and magnetic granulation from SOUP

NASA Astrophysics Data System (ADS)

Title, A. M.; Tarbell, T. D.; Acton, L.; Duncan, D.; Ferguson, S. H.; Finch, M.; Frank, Z.; Kelly, G.; Lindgren, R.; Morrill, M.

1987-09-01

The flight of Solar Optical Universal Polarimeter (SOUP) on Spacelab 2 allowed the collection of time sequences of diffraction limited (0.5 arc sec) granulation images with excellent pointing (0.003 arc sec) and completely free of the distortion that plagues groundbased images. The p-mode oscillations are clearly seen in the data. Using Fourier transforms in the temporal and spatial domain, it was shown that the p-modes dominate the autocorrelation lifetime in magnetic regions. When these oscillations are removed the autocorrelation lifetime is found to be 500 sec in quiet and 950 sec in magnetic regions. In quiet areas exploding granules are seen to be common. It is speculated that a significant fraction of granule lifetimes are terminated by nearby explosions. Using local correlation tracking techniques it was able to measure horizontal displacements, and thus transverse velocities, in the magnetic field. In quiet sun it is possible to detect both super and mesogranulation. Horizontal velocities are as great as 1000 m/s and the average velocity is 400 m/s. In magnetic regions horizontal velocities are much less, about 100 m/s.

A hierarchical spatial model of avian abundance with application to Cerulean Warblers

USGS Publications Warehouse

Thogmartin, Wayne E.; Sauer, John R.; Knutson, Melinda G.

2004-01-01

Surveys collecting count data are the primary means by which abundance is indexed for birds. These counts are confounded, however, by nuisance effects including observer effects and spatial correlation between counts. Current methods poorly accommodate both observer and spatial effects because modeling these spatially autocorrelated counts within a hierarchical framework is not practical using standard statistical approaches. We propose a Bayesian approach to this problem and provide as an example of its implementation a spatial model of predicted abundance for the Cerulean Warbler (Dendroica cerulea) in the Prairie-Hardwood Transition of the upper midwestern United States. We used an overdispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods. We used 21 years of North American Breeding Bird Survey counts as the response in a loglinear function of explanatory variables describing habitat, spatial relatedness, year effects, and observer effects. The model included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land cover composition and configuration, climate, terrain heterogeneity, and human influence. The inherent hierarchy in the model was from counts occurring, in part, as a function of observers within survey routes within years. We found that the percentage of forested wetlands, an index of wetness potential, and an interaction between mean annual precipitation and deciduous forest patch size best described Cerulean Warbler abundance. Based on a map of relative abundance derived from the posterior parameter estimates, we estimated that only 15% of the species' population occurred on federal land, necessitating active engagement of public landowners and state agencies in the conservation of the breeding habitat for this species. Models of this type can be applied to any data in which the response is counts, such as animal counts, activity (e.g.,nest) counts, or species richness. The most noteworthy practical application of this spatial modeling approach is the ability to map relative species abundance. The functional relationships that we elucidated for the Cerulean Warbler provide a basis for the development of management programs and may serve to focus management and monitoring on areas and habitat variables important to Cerulean Warblers.

Identifying and characterizing hepatitis C virus hotspots in Massachusetts: a spatial epidemiological approach.

PubMed

Stopka, Thomas J; Goulart, Michael A; Meyers, David J; Hutcheson, Marga; Barton, Kerri; Onofrey, Shauna; Church, Daniel; Donahue, Ashley; Chui, Kenneth K H

2017-04-20

Hepatitis C virus (HCV) infections have increased during the past decade but little is known about geographic clustering patterns. We used a unique analytical approach, combining geographic information systems (GIS), spatial epidemiology, and statistical modeling to identify and characterize HCV hotspots, statistically significant clusters of census tracts with elevated HCV counts and rates. We compiled sociodemographic and HCV surveillance data (n = 99,780 cases) for Massachusetts census tracts (n = 1464) from 2002 to 2013. We used a five-step spatial epidemiological approach, calculating incremental spatial autocorrelations and Getis-Ord Gi* statistics to identify clusters. We conducted logistic regression analyses to determine factors associated with the HCV hotspots. We identified nine HCV clusters, with the largest in Boston, New Bedford/Fall River, Worcester, and Springfield (p < 0.05). In multivariable analyses, we found that HCV hotspots were independently and positively associated with the percent of the population that was Hispanic (adjusted odds ratio [AOR]: 1.07; 95% confidence interval [CI]: 1.04, 1.09) and the percent of households receiving food stamps (AOR: 1.83; 95% CI: 1.22, 2.74). HCV hotspots were independently and negatively associated with the percent of the population that were high school graduates or higher (AOR: 0.91; 95% CI: 0.89, 0.93) and the percent of the population in the "other" race/ethnicity category (AOR: 0.88; 95% CI: 0.85, 0.91). We identified locations where HCV clusters were a concern, and where enhanced HCV prevention, treatment, and care can help combat the HCV epidemic in Massachusetts. GIS, spatial epidemiological and statistical analyses provided a rigorous approach to identify hotspot clusters of disease, which can inform public health policy and intervention targeting. Further studies that incorporate spatiotemporal cluster analyses, Bayesian spatial and geostatistical models, spatially weighted regression analyses, and assessment of associations between HCV clustering and the built environment are needed to expand upon our combined spatial epidemiological and statistical methods.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

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

Yang, Ge; Wang, Jun; Fang, Wen, E-mail: fangwen@bjtu.edu.cn

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also definedmore » in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.« less

Real-time autocorrelator for fluorescence correlation spectroscopy based on graphical-processor-unit architecture: method, implementation, and comparative studies

NASA Astrophysics Data System (ADS)

Laracuente, Nicholas; Grossman, Carl

2013-03-01

We developed an algorithm and software to calculate autocorrelation functions from real-time photon-counting data using the fast, parallel capabilities of graphical processor units (GPUs). Recent developments in hardware and software have allowed for general purpose computing with inexpensive GPU hardware. These devices are more suited for emulating hardware autocorrelators than traditional CPU-based software applications by emphasizing parallel throughput over sequential speed. Incoming data are binned in a standard multi-tau scheme with configurable points-per-bin size and are mapped into a GPU memory pattern to reduce time-expensive memory access. Applications include dynamic light scattering (DLS) and fluorescence correlation spectroscopy (FCS) experiments. We ran the software on a 64-core graphics pci card in a 3.2 GHz Intel i5 CPU based computer running Linux. FCS measurements were made on Alexa-546 and Texas Red dyes in a standard buffer (PBS). Software correlations were compared to hardware correlator measurements on the same signals. Supported by HHMI and Swarthmore College

Using Open data in analyzing urban growth: urban density and change detection

NASA Astrophysics Data System (ADS)

murgante, Beniamino; Nolè, Gabriele; Lasaponara, Rosa; Lanorte, Antonio

2013-04-01

In recent years a great attention has been paid to the evolution and the use of spatial data. Internet technologies accelerated such a process, allowing more direct access to spatial information. It is estimated that more than 600 million people have been connected to the Internet at least once to display maps on the web. Consequently, there is an irreversible process which considers geographical dimension as a fundamental attribute for the management of information flows. Furthermore, the great activity produced by open data movement leads to an easier and clearer access to geospatial information. This trend concerns, in a less evident way, also satellite data, which are increasingly accessible through the web. Spatial planning, geography and other regional sciences find it difficult to build knowledge related to spatial transformation. These problems can be significantly reduced due to a large data availability, producing significant opportunities to capture knowledge useful for a better territorial governance. This study has been developed in a heavily anthropized area in southern Italy, Apulia region, using free spatial data and free multispectral and multitemporal satellite data (Apulia region was one of the first regions in Italy to adopt open data policies). The analysis concerns urban growth, which, in recent decades, showed a rapid increase. In a first step the evolution in time and change detection of urban areas has been analyzed paying particular attention to soil consumption. In the second step Kernel Density has been adopted in order to assess development pressures. KDE (Kernel Density Estimation) function is a technique that provides the density of a phenomenon based on point data. A mobile three dimensional surface has been produced from a set of points distributed over a region of space, which weighs the events within its sphere of influence, depending on their distance from the point from which intensity is estimated. It produces, considering as input point data (vector), a density continuous raster as an output. In this case, the intensity of phenomenon will be given by buildings volume. References • Bailey T. C., Gatrell A. C. (1995). Interactive spatial data analysis. Prentice Hall. • Danese M., Lazzari M., Murgante B. (2009). "Geostatistics in Historical Macroseismic Data Analysis" Transactions on Computational Sciences VI, LNCS Vol. 5730, pp. 324-341, Springer-Verlag, Berlin ISSN: 1611-3349, doi:10.1007/978-3-642-10649-1_19 • Nolè G., Danese M., Murgante B., Lasaponara R., Lanorte, A., (2012) "Using Spatial Autocorrelation Techniques and Multi-temporal Satellite Data for Analyzing Urban Sprawl" Lecture Notes in Computer Science vol. 7335, pp. 512-527. Springer-Verlag, Berlin. ISSN: 0302-9743, doi: 10.1007/978-3-642-31137-6_39 • Murgante, B., Las Casas, G., Danese, M., (2012), "Analyzing Neighbourhoods Suitable for Urban Renewal Programs with Autocorrelation Techniques" In Burian J. (Eds.) "Advances in Spatial Planning" InTech — Open Access DOI: 10.5772/33747 ISBN:978-953-51-0377-6 • Lanorte, A., Danese M., Lasaponara R., Murgante B. (2011) "Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis" International Journal of Applied Earth Observation and Geoinformation, Elsevier, doi:10.1016/j.jag.2011.09.005 • O'Sullivan D., Unwin D., (2002). Geographic Information Analysis. John Wiley & Sons • Yang, X., Lo, C. P.: Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int. J. Rem. Sensing 23, pp. 1775--1798 (2002) • Yuan, F., Sawaya, K.,.Loeffelholz, B. C., Bauer, M. E.: Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Rem. Sensing Environ. 98, pp. 317--328 (2005)

Optimized statistical parametric mapping procedure for NIRS data contaminated by motion artifacts : Neurometric analysis of body schema extension.

PubMed

Suzuki, Satoshi

2017-09-01

This study investigated the spatial distribution of brain activity on body schema (BS) modification induced by natural body motion using two versions of a hand-tracing task. In Task 1, participants traced Japanese Hiragana characters using the right forefinger, requiring no BS expansion. In Task 2, participants performed the tracing task with a long stick, requiring BS expansion. Spatial distribution was analyzed using general linear model (GLM)-based statistical parametric mapping of near-infrared spectroscopy data contaminated with motion artifacts caused by the hand-tracing task. Three methods were utilized in series to counter the artifacts, and optimal conditions and modifications were investigated: a model-free method (Step 1), a convolution matrix method (Step 2), and a boxcar-function-based Gaussian convolution method (Step 3). The results revealed four methodological findings: (1) Deoxyhemoglobin was suitable for the GLM because both Akaike information criterion and the variance against the averaged hemodynamic response function were smaller than for other signals, (2) a high-pass filter with a cutoff frequency of .014 Hz was effective, (3) the hemodynamic response function computed from a Gaussian kernel function and its first- and second-derivative terms should be included in the GLM model, and (4) correction of non-autocorrelation and use of effective degrees of freedom were critical. Investigating z-maps computed according to these guidelines revealed that contiguous areas of BA7-BA40-BA21 in the right hemisphere became significantly activated ([Formula: see text], [Formula: see text], and [Formula: see text], respectively) during BS modification while performing the hand-tracing task.

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1996-01-01

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

Characterizing local traffic contributions to particulate air pollution in street canyons using mobile monitoring techniques

NASA Astrophysics Data System (ADS)

Zwack, Leonard M.; Paciorek, Christopher J.; Spengler, John D.; Levy, Jonathan I.

2011-05-01

Traffic within urban street canyons can contribute significantly to ambient concentrations of particulate air pollution. In these settings, it is challenging to separate within-canyon source contributions from urban and regional background concentrations given the highly variable and complex emissions and dispersion characteristics. In this study, we used continuous mobile monitoring of traffic-related particulate air pollutants to assess the contribution to concentrations, above background, of traffic in the street canyons of midtown Manhattan. Concentrations of both ultrafine particles (UFP) and fine particles (PM 2.5) were measured at street level using portable instruments. Statistical modeling techniques accounting for autocorrelation were used to investigate the presence of spatial heterogeneity of pollutant concentrations as well as to quantify the contribution of within-canyon traffic sources. Measurements were also made within Central Park, to examine the impact of offsets from major roadways in this urban environment. On average, an approximate 11% increase in concentrations of UFP and 8% increase in concentrations of PM 2.5 over urban background was estimated during high-traffic periods in street canyons as opposed to low traffic periods. Estimates were 8% and 5%, respectively, after accounting for temporal autocorrelation. Within Central Park, concentrations were 40% higher than background (5% after accounting for temporal autocorrelation) within the first 100 m from the nearest roadway for UFP, with a smaller but statistically significant increase for PM 2.5. Our findings demonstrate the viability of a mobile monitoring protocol coupled with spatiotemporal modeling techniques in characterizing local source contributions in a setting with street canyons.

Characteristic measurement for femtosecond laser pulses using a GaAs PIN photodiode as a two-photon photovoltaic receiver

NASA Astrophysics Data System (ADS)

Chen, Junbao; Xia, Wei; Wang, Ming

2017-06-01

Photodiodes that exhibit a two-photon absorption effect within the spectral communication band region can be useful for building an ultra-compact autocorrelator for the characteristic inspection of optical pulses. In this work, we develop an autocorrelator for measuring the temporal profile of pulses at 1550 nm from an erbium-doped fiber laser based on the two-photon photovoltaic (TPP) effect in a GaAs PIN photodiode. The temporal envelope of the autocorrelation function contains two symmetrical temporal side lobes due to the third order dispersion of the laser pulses. Moreover, the joint time-frequency distribution of the dispersive pulses and the dissimilar two-photon response spectrum of GaAs and Si result in different delays for the appearance of the temporal side lobes. Compared with Si, GaAs displays a greater sensitivity for pulse shape reconstruction at 1550 nm, benefiting from the higher signal-to-noise ratio of the side lobes and the more centralized waveform of the autocorrelation trace. We also measure the pulse width using the GaAs PIN photodiode, and the resolution of the measured full width at half maximum of the TPP autocorrelation trace is 0.89 fs, which is consistent with a conventional second-harmonic generation crystal autocorrelator. The GaAs PIN photodiode is shown to be highly suitable for real-time second-order autocorrelation measurements of femtosecond optical pulses. It is used both for the generation and detection of the autocorrelation signal, allowing the construction of a compact and inexpensive intensity autocorrelator.

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis.

PubMed

Cheng, Yezeng; Larin, Kirill V

2006-12-20

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

NASA Astrophysics Data System (ADS)

Cheng, Yezeng; Larin, Kirill V.

2006-12-01

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Spatial aspects of tree mortality strongly differ between young and old-growth forests.

PubMed

Larson, Andrew J; Lutz, James A; Donato, Daniel C; Freund, James A; Swanson, Mark E; HilleRisLambers, Janneke; Sprugel, Douglas G; Franklin, Jerry F

2015-11-01

Rates and spatial patterns of tree mortality are predicted to change during forest structural development. In young forests, mortality should be primarily density dependent due to competition for light, leading to an increasingly spatially uniform pattern of surviving trees. In contrast, mortality in old-growth forests should be primarily caused by contagious and spatially autocorrelated agents (e.g., insects, wind), causing spatial aggregation of surviving trees to increase through time. We tested these predictions by contrasting a three-decade record of tree mortality from replicated mapped permanent plots located in young (< 60-year-old) and old-growth (> 300-year-old) Abies amabilis forests. Trees in young forests died at a rate of 4.42% per year, whereas trees in old-growth forests died at 0.60% per year. Tree mortality in young forests was significantly aggregated, strongly density dependent, and caused live tree patterns to become more uniform through time. Mortality in old-growth forests was spatially aggregated, but was density independent and did not change the spatial pattern of surviving trees. These results extend current theory by demonstrating that density-dependent competitive mortality leading to increasingly uniform tree spacing in young forests ultimately transitions late in succession to a more diverse tree mortality regime that maintains spatial heterogeneity through time.

Geographical distribution patterns of iodine in drinking-water and its associations with geological factors in Shandong Province, China.

PubMed

Gao, Jie; Zhang, Zhijie; Hu, Yi; Bian, Jianchao; Jiang, Wen; Wang, Xiaoming; Sun, Liqian; Jiang, Qingwu

2014-05-19

County-based spatial distribution characteristics and the related geological factors for iodine in drinking-water were studied in Shandong Province (China). Spatial autocorrelation analysis and spatial scan statistic were applied to analyze the spatial characteristics. Generalized linear models (GLMs) and geographically weighted regression (GWR) studies were conducted to explore the relationship between water iodine level and its related geological factors. The spatial distribution of iodine in drinking-water was significantly heterogeneous in Shandong Province (Moran's I = 0.52, Z = 7.4, p < 0.001). Two clusters for high iodine in drinking-water were identified in the south-western and north-western parts of Shandong Province by the purely spatial scan statistic approach. Both GLMs and GWR indicated a significantly global association between iodine in drinking-water and geological factors. Furthermore, GWR showed obviously spatial variability across the study region. Soil type and distance to Yellow River were statistically significant at most areas of Shandong Province, confirming the hypothesis that the Yellow River causes iodine deposits in Shandong Province. Our results suggested that the more effective regional monitoring plan and water improvement strategies should be strengthened targeting at the cluster areas based on the characteristics of geological factors and the spatial variability of local relationships between iodine in drinking-water and geological factors.

[Analysis of influence on spatial distribution of fishing ground for Antarctic krill fishery in the northern South Shetland Islands based on GWR model].

PubMed

Chen, Lyu Feng; Zhu, Guo Ping

2018-03-01

Based on Antarctic krill fishery and marine environmental data collected by scientific observers, using geographically weighted regression (GWR) model, we analyzed the effects of the factors with spatial attributes, i.e., depth of krill swarm (DKS) and distance from fishing position to shore (DTS), and sea surface temperature (SST), on the spatial distribution of fishing ground in the northern South Shetland Islands. The results showed that there was no significant aggregation in spatial distribution of catch per unit fishing effort (CPUE). Spatial autocorrelations (positive) among three factors were observed in 2010 and 2013, but were not in 2012 and 2016. Results from GWR model showed that the extent for the impacts on spatial distribution of CPUEs varied among those three factors, following the order DKS>SST>DTS. Compared to the DKS and DTS, the impact of SST on the spatial distribution of CPUEs presented adverse trend in the eastern and western parts of the South Shetland Islands. Negative correlations occurred for the spatial effects of DKS and DTS on distribution of CPUEs, though with inter-annual and regional variation. Our results provide metho-dological reference for researches on the underlying mechanism for fishing ground formation for Antarctic krill fishery.

Mapping spatial patterns of denitrifiers at large scales (Invited)

NASA Astrophysics Data System (ADS)

Philippot, L.; Ramette, A.; Saby, N.; Bru, D.; Dequiedt, S.; Ranjard, L.; Jolivet, C.; Arrouays, D.

2010-12-01

Little information is available regarding the landscape-scale distribution of microbial communities and its environmental determinants. Here we combined molecular approaches and geostatistical modeling to explore spatial patterns of the denitrifying community at large scales. The distribution of denitrifrying community was investigated over 107 sites in Burgundy, a 31 500 km2 region of France, using a 16 X 16 km sampling grid. At each sampling site, the abundances of denitrifiers and 42 soil physico-chemical properties were measured. The relative contributions of land use, spatial distance, climatic conditions, time and soil physico-chemical properties to the denitrifier spatial distribution were analyzed by canonical variation partitioning. Our results indicate that 43% to 85% of the spatial variation in community abundances could be explained by the measured environmental parameters, with soil chemical properties (mostly pH) being the main driver. We found spatial autocorrelation up to 739 km and used geostatistical modelling to generate predictive maps of the distribution of denitrifiers at the landscape scale. Studying the distribution of the denitrifiers at large scale can help closing the artificial gap between the investigation of microbial processes and microbial community ecology, therefore facilitating our understanding of the relationships between the ecology of denitrifiers and N-fluxes by denitrification.

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility

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

Kirby, R. Jason

2016-08-15

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicitymore » library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. - Highlights: • Impedance-based monitoring of human iPSC-derived cardiomyocyte contractility • Limit cycle analysis of impedance data identifies aberrant oscillation patterns. • Nonlinear autocorrelation function quantifies beat irregularity. • Identification of hERG and non-hERG inhibitors with known risk of arrhythmia • Automated software processes limit cycle and autocorrelation analyses of 96w data.« less

Preliminary Field Demonstration of Passive Radio Sounding Using the Sun as a Signal for Echo Detection

NASA Astrophysics Data System (ADS)

Peters, S. T.; Schroeder, D. M.; Romero-Wolf, A.; Haynes, M.

2017-12-01

The Radar for Icy Moon Exploration (RIME) and the Radar for Europa Assessment and Sounding: Ocean to Near-surface (REASON) have been identified as potential candidates for the implementation of passive sounding as additional observing modes for the ESA and NASA missions to Ganymede and Europa. Recent work has shown the theoretical potential for Jupiter's decametric radiation to be used as a source for passive radio sounding of its icy moons. We are further developing and adapting this geophysical approach for use in terrestrial glaciology. Here, we present results from preliminary field testing of a prototype passive radio sounder from cliffs along the California coast. This includes both using a Lloyd's mirror to measure the Sun's direct path and its reflection off the ocean's surface and exploiting autocorrelation to detect the delay time of the echo. This is the first in-situ demonstration of the autocorrelation-based passive-sounding approach using an astronomical white noise signal. We also discuss preliminary field tests on rougher terrestrial and subglacial surfaces, including at Store Glacier in Greenland. Additionally, we present modeling and experimental results that demonstrate the feasibility of applying presumming approaches to the autocorrelations to achieve coherent gain from an inherently random signal. We note that while recording with wider bandwidths and greater delays places fundamental limits on the Lloyd's mirror approach, our new autocorrelation method has no such limitation. Furthermore, we show how achieving wide bandwidths via spectral-stitching methods allows us to obtain a finer range resolution than given by the receiver's instantaneous bandwidth. Finally, we discuss the potential for this technique to eliminate the need for active transmitters in certain types of ice sounding experiments, thereby reducing the complexity, power consumption, and cost of systems and observations.