Showing papers on "Spatial analysis published in 2005"

Spatial Analysis A Guide for Ecologists

Abstract: Preface 1. Spatial concepts and notions 2. Ecological and spatial processes 3. Points, lines and graphs 4. Spatial analysis of complete point location data 5. Contiguous units analysis 6. Spatial analysis of sample data 7. Spatial relationship and multiscale analysis 8. Spatial autocorrelation and inferential tests 9. Spatial partitioning: spatial clusters and boundary detection 10. Spatial diversity analysis 11. Spatio-temporal analysis 12. Closing comments and future directions References Index.

Applied Spatial Statistics for Public Health Data

Abstract: (2005). Applied Spatial Statistics for Public Health Data. Journal of the American Statistical Association: Vol. 100, No. 470, pp. 702-703.

Spatial prediction models for landslide hazards: review, comparison and evaluation

Abstract: . The predictive power of logistic regression, support vector machines and bootstrap-aggregated classification trees (bagging, double-bagging) is compared using misclassification error rates on independent test data sets. Based on a resampling approach that takes into account spatial autocorrelation, error rates for predicting "present" and "future" landslides are estimated within and outside the training area. In a case study from the Ecuadorian Andes, logistic regression with stepwise backward variable selection yields lowest error rates and demonstrates the best generalization capabilities. The evaluation outside the training area reveals that tree-based methods tend to overfit the data.

Spatial considerations for linking watershed land cover to ecological indicators in streams

Abstract: Watershed land cover is widely used as a predictor of stream-ecosystem condition. However, numerous spatial factors can confound the interpretation of correlative analyses between land cover and stream indicators, particularly at broad spatial scales. We used a stream-monitoring data set collected from the Coastal Plain of Maryland, USA to address analytical challenges presented by (1) collinearity of land-cover class percentages, (2) spatial autocorrelation of land cover and stream data, (3) intercorrelations among and spatial autocorrelation within abiotic intermediaries that link land cover to stream biota, and (4) spatial arrangement of land cover within watersheds. We focused on two commonly measured stream indicators, nitrate-nitrogen (NO3–N) and macroinvertebrate assemblages, to evaluate how different spatial considerations may influence results. Partial correlation analysis of land-cover percentages revealed that simple correlations described relationships that could not be separated from the effe...

GEO-RBAC: a spatially aware RBAC

Abstract: Securing access to data in location-based services and mobile applications requires the definition of spatially aware access-control systems. Even if some approaches have already been proposed either in the context of geographic database systems or context-aware applications, a comprehensive framework, general and flexible enough to deal with spatial aspects in real mobile applications, is still missing. In this paper, we make one step toward this direction and present GEO-RBAC, an extension of the RBAC model enhanced with spatial-and location-based information. In GEORBAC, spatial entities are used to model objects, user positions, and geographically bounded roles. Roles are activated based on the position of the user. Besides a physical position, obtained from a given mobile terminal or a cellular phone, users are also assigned a logical and device-independent position, representing the feature (the road, the town, the region) in which they are located. To enhance flexibility and reusability, we also introduce the concept of role schema, specifying the name of the role, as well as the type of the role spatial boundary and the granularity of the logical position. We then extend GEO-RBAC to support hierarchies, modeling permission, user, and activation inheritance, and separation of duty constraints. The proposed classes of constraints extend the conventional ones to deal with different granularities (schema/instance level) and spatial information. We conclude the paper with an analysis of several properties concerning the resulting model.

Geographically weighted Poisson regression for disease association mapping.

Abstract: This paper describes geographically weighted Poisson regression (GWPR) and its semi-parametric variant as a new statistical tool for analysing disease maps arising from spatially non-stationary processes. The method is a type of conditional kernel regression which uses a spatial weighting function to estimate spatial variations in Poisson regression parameters. It enables us to draw surfaces of local parameter estimates which depict spatial variations in the relationships between disease rates and socio-economic characteristics. The method therefore can be used to test the general assumption made, often without question, in the global modelling of spatial data that the processes being modelled are stationary over space. Equally, it can be used to identify parts of the study region in which 'interesting' relationships might be occurring and where further investigation might be warranted. Such exceptions can easily be missed in traditional global modelling and therefore GWPR provides disease analysts with an important new set of statistical tools. We demonstrate the GWPR approach applied to a data set of working-age deaths in the Tokyo metropolitan area, Japan. The results indicate that there are significant spatial variations (that is, variation beyond that expected from random sampling) in the relationships between working-age mortality and occupational segregation and between working-age mortality and unemployment throughout the Tokyo metropolitan area and that, consequently, the application of traditional 'global' models would yield misleading results.

Do spatial effecfs really matter in regression analysis

Abstract: A substantial hotly of applied statistical and econometric analysis in regional science and geography deals with data collected for aggregate spatial units of observation. These data are typically affected by a variety of measurement problems, resulting in spatial dependence and spatial heterogeneity. However, most of the empirical work fails to take this into account. In this paper, we address the issue of the extent to which spatial effects matter in applied regression analysis. An overview of the formal methodological problems is given, and related to the literature in spatial statistics and spatial econometrics.

Spatial analysis of landscapes: concepts and statistics

Abstract: Species patchiness implies that nearby observations of species abundance tend to be similar or that individual conspecific organisms are more closely spaced than by random chance. This can be caused either by the positive spatial autocorrelation among the locations of individual organisms due to ecological spatial processes (e.g., species dispersal, competition for space and resources) or by spatial dependence due to (positive or negative) species responses to underlying environmental conditions. Both forms of spatial structure pose problems for statistical analysis, as spatial autocorrelation in the residuals violates the assumption of independent observations, while environmental heterogeneity restricts the comparability of replicates. In this paper, we discuss how spatial structure due to ecological spatial processes and spatial dependence affects spatial statistics, landscape metrics, and statistical modeling of the species-environment correlation. For instance, while spatial statistics can quantify spatial pattern due to an endogeneous spatial process, these methods are severely affected by landscape environmental heterogeneity. Therefore, sta- tistical models of species response to the environment not only need to accommodate spatial structure, but need to distinguish between components due to exogeneous and endogeneous processes rather than discarding all spatial variance. To discriminate between different components of spatial structure, we suggest using (multivariate) spatial analysis of residuals or delineating the spatial realms of a stationary spatial process using boundary detection algorithms. We end by identifying conceptual and statistical challenges that need to be addressed for adequate spatial analysis of landscapes.

Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing

Abstract: Customary modeling for continuous point-referenced data assumes a Gaussian process that is often taken to be stationary. When such models are fitted within a Bayesian framework, the unknown parameters of the process are assumed to be random, so a random Gaussian process results. Here we propose a novel spatial Dirichlet process mixture model to produce a random spatial process that is neither Gaussian nor stationary. We first develop a spatial Dirichlet process model for spatial data and discuss its properties. Because of familiar limitations associated with direct use of Dirichlet process models, we introduce mixing by convolving this process with a pure error process. We then examine properties of models created through such Dirichlet process mixing. In the Bayesian framework, we implement posterior inference using Gibbs sampling. Spatial prediction raises interesting questions, but these can be handled. Finally, we illustrate the approach using simulated data, as well as a dataset involving precipitati...

Location-based activity recognition using relational Markov networks

Abstract: In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, spatial information extracted from geographic databases, and global constraints such as the number of homes or workplaces of a person. We develop an efficient inference and learning technique based on MCMC. Using GPS location data collected by multiple people we show that the technique can accurately label a person's activity locations. Furthermore, we show that it is possible to learn good models from less data by using priors extracted from other people's data.

Modelling geographical patterns in species richness using eigenvector-based spatial filters

Abstract: Aim To test the mechanisms driving bird species richness at broad spatial scales using eigenvector-based spatial filtering. Location South America. Methods An eigenvector-based spatial filtering was applied to evaluate spatial patterns in South American bird species richness, taking into account spatial autocorrelation in the data. The method consists of using the geographical coordinates of a region, based on eigenanalyses of geographical distances, to establish a set of spatial filters (eigenvectors) expressing the spatial structure of the region at different spatial scales. These filters can then be used as predictors in multiple and partial regression analyses, taking into account spatial autocorrelation. Autocorrelation in filters and in the regression residuals can be used as stopping rules to define which filters will be used in the analyses. Results Environmental component alone explained 8% of variation in richness, whereas 77% of the variation could be attributed to an interaction between environment and geography expressed by the filters (which include mainly broad-scale climatic factors). Regression coefficients of environmental component were highest for AET. These results were unbiased by short-scale spatial autocorrelation. Also, there was a significant interaction between topographic heterogeneity and minimum temperature. Conclusion Eigenvector-based spatial filtering is a simple and suitable statistical protocol that can be used to analyse patterns in species richness taking into account spatial autocorrelation at different spatial scales. The results for South American birds are consistent with the climatic hypothesis, in general, and energy hypothesis, in particular. Habitat heterogeneity also has a significant effect on variation in species richness in warm tropical regions.

Spatial process and data models: Toward integration of agent-based models and GIS

Abstract: The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.

Statistical Analysis of Geographic Information with ArcView GIS And ArcGIS

Abstract: PREFACE. ACKNOWLEDGMENTS. 1 INTRODUCTION. 1.1 Why Statistics and Sampling? 1.2 What Are Special about Spatial Data? 1.3 Spatial Data and the Need for Spatial Analysis/ Statistics. 1.4 Fundamentals of Spatial Analysis and Statistics. 1.5 ArcView Notes-Data Model and Examples. PART I: CLASSICAL STATISTICS. 2 DISTRIBUTION DESCRIPTORS: ONE VARIABLE (UNIVARIATE). 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 ArcView Examples. 2.4 Higher Moment Statistics. 2.5 ArcView Examples. 2.6 Application Example. 2.7 Summary. 3 RELATIONSHIP DESCRIPTORS: TWO VARIABLES (BIVARIATE). 3.1 Correlation Analysis. 3.2 Correlation: Nominal Scale. 3.3 Correlation: Ordinal Scale. 3.4 Correlation: Interval /Ratio Scale. 3.5 Trend Analysis. 3.6 ArcView Notes. 3.7 Application Examples. 4 HYPOTHESIS TESTERS. 4.1 Probability Concepts. 4.2 Probability Functions. 4.3 Central Limit Theorem and Confidence Intervals. 4.4 Hypothesis Testing. 4.5 Parametric Test Statistics. 4.6 Difference in Means. 4.7 Difference Between a Mean and a Fixed Value. 4.8 Significance of Pearson's Correlation Coefficient. 4.9 Significance of Regression Parameters. 4.10 Testing Nonparametric Statistics. 4.11 Summary. PART II: SPATIAL STATISTICS. 5 POINT PATTERN DESCRIPTORS. 5.1 The Nature of Point Features. 5.2 Central Tendency of Point Distributions. 5.3 Dispersion and Orientation of Point Distributions. 5.4 ArcView Notes. 5.5 Application Examples. 6 POINT PATTERN ANALYZERS. 6.1 Scale and Extent. 6.2 Quadrat Analysis. 6.3 Ordered Neighbor Analysis. 6.4 K-Function. 6.5 Spatial Autocorrelation of Points. 6.6 Application Examples. 7 LINE PATTERN ANALYZERS. 7.1 The Nature of Linear Features: Vectors and Networks. 7.2 Characteristics and Attributes of Linear Features. 7.3 Directional Statistics. 7.4 Network Analysis. 7.5 Application Examples. 8 POLYGON PATTERN ANALYZERS. 8.1 Introduction. 8.2 Spatial Relationships. 8.3 Spatial Dependency. 8.4 Spatial Weights Matrices. 8.5 Spatial Autocorrelation Statistics and Notations. 8.6 Joint Count Statistics. 8.7 Spatial Autocorrelation Global Statistics. 8.8 Local Spatial Autocorrelation Statistics. 8.9 Moran Scatterplot. 8.10 Bivariate Spatial Autocorrelation. 8.11 Application Examples. 8.12 Summary. APPENDIX: ArcGIS Spatial Statistics Tools. ABOUT THE CD-ROM. INDEX.

The ESRI Guide to GIS analysis, Volume 2: Spartial measurements and statistics

Abstract: In The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, Mitchell (the author) takes users deeper, showing how an emerging set of tools that rely on spatial statistics provides GIS users the capability to conduct detailed, mathematical analysis of geographic information, This second volume introduces statistical tools, geared specifically for geographic analysis, that are relatively new to GIS software packages and thus to most GIS users. It shows the tools in use in many different applications, explains which tools are best with which situations, and provides guidance on interpreting the results you get. The ESRI Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics focuses on four fundamental tasks of statistical analysis. These are: calculating the center, dispersion, and trend, Identifying patterns, Identifying clusters and Analyzing geographic relationships.

Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models.

Abstract: SUMMARY. Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models-3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models-3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models-3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models-3 output. We apply our methods to data on SO2 concentrations, and we obtain high-resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.

Generalized Hierarchical Multivariate CAR Models for Areal Data

Abstract: In the fields of medicine and public health, a common application of areal data models is the study of geographical patterns of disease. When we have several measurements recorded at each spatial location (for example, information on p>/= 2 diseases from the same population groups or regions), we need to consider multivariate areal data models in order to handle the dependence among the multivariate components as well as the spatial dependence between sites. In this article, we propose a flexible new class of generalized multivariate conditionally autoregressive (GMCAR) models for areal data, and show how it enriches the MCAR class. Our approach differs from earlier ones in that it directly specifies the joint distribution for a multivariate Markov random field (MRF) through the specification of simpler conditional and marginal models. This in turn leads to a significant reduction in the computational burden in hierarchical spatial random effect modeling, where posterior summaries are computed using Markov chain Monte Carlo (MCMC). We compare our approach with existing MCAR models in the literature via simulation, using average mean square error (AMSE) and a convenient hierarchical model selection criterion, the deviance information criterion (DIC; Spiegelhalter et al., 2002, Journal of the Royal Statistical Society, Series B64, 583-639). Finally, we offer a real-data application of our proposed GMCAR approach that models lung and esophagus cancer death rates during 1991-1998 in Minnesota counties.

Analyzing Nonstationary Spatial Data Using Piecewise Gaussian Processes

Abstract: In many problems in geostatistics the response variable of interest is strongly related to the underlying geology of the spatial location. In these situations there is often little correlation in the responses found in different rock strata, so the underlying covariance structure shows sharp changes at the boundaries of the rock types. Conventional stationary and nonstationary spatial methods are inappropriate, because they typically assume that the covariance between points is a smooth function of distance. In this article we propose a generic method for the analysis of spatial data with sharp changes in the underlying covariance structure. Our method works by automatically decomposing the spatial domain into disjoint regions within which the process is assumed to be stationary, but the data are assumed independent across regions. Uncertainty in the number of disjoint regions, their shapes, and the model within regions is dealt with in a fully Bayesian fashion. We illustrate our approach on a previously ...

Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems

Abstract: Aim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non-stationarity in the parameters estimated for forest ecosystems in China. Location We used data across China. Methods We e xamine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time-integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods. Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AIC c ) and R 2 . GWR provided a value of 4891 for AIC c and 0.66 for R 2 , compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially nonstationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AIC c and 0.60 for R 2 ), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals. Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.

Spatial process modelling for univariate and multivariate dynamic spatial data

Abstract: There is a considerable literature in spatiotemporal modelling The approach adopted here applies to the setting where space is viewed as continuous but time is taken to be discrete We view the data as a time series of spatial processes and work in the setting of dynamic models, achieving a class of dynamic models for such data We seek rich, flexible, easy-to-specify, easy-to-interpret, computationally tractable specifications which allow very general mean structures and also non-stationary association structures Our modelling contributions are as follows In the case where univariate data are collected at the spatial locations, we propose the use of a spatiotemporally varying coefficient form In the case where multivariate data are collected at the locations, we need to capture associations among measurements at a given location and time as well as dependence across space and time We propose the use of suitable multivariate spatial process models developed through coregionalization We adopt a Bayesian inference framework The resulting posterior and predictive inference enables summaries in the form of tables and maps, which help to reveal the nature of the spatiotemporal behaviour as well as the associated uncertainty We illuminate various computational issues and then apply our models to the analysis of climate data obtained from the National Center for Atmospheric Research to analyze precipitation and temperature measurements obtained in Colorado in 1997 Copyright © 2005 John Wiley & Sons, Ltd

Comparison of a spatial approach with the multilevel approach for investigating place effects on health: the example of healthcare utilisation in France

Abstract: Study objective: Most studies of place effects on health have followed the multilevel analytical approach that investigates geographical variations of health phenomena by fragmenting space into arbitrary areas. This study examined whether analysing geographical variations across continuous space with spatial modelling techniques and contextual indicators that capture space as a continuous dimension surrounding individual residences provided more relevant information on the spatial distribution of outcomes. Healthcare utilisation in France was taken as an illustrative example in comparing the spatial approach with the multilevel approach. Design: Multilevel and spatial analyses of cross sectional data. Participants: 10 955 beneficiaries of the three principal national health insurance funds, surveyed in 1998 and 2000 on continental France. Main results: Multilevel models showed significant geographical variations in healthcare utilisation. However, the Moran's I statistic showed spatial autocorrelation unaccounted for by multilevel models. Modelling the correlation between people as a decreasing function of the spatial distance between them, spatial mixed models gave information not only on the magnitude, but also on the scale of spatial variations, and provided more accurate standard errors for risk factors effects. The socioeconomic level of the residential context and the supply of physicians were independently associated with healthcare utilisation. Place indicators better explained spatial variations in healthcare utilisation when measured across continuous space, rather than within administrative areas. Conclusions: The kind of conceptualisation of space during analysis influences the understanding of place effects on health. In many contextual studies, viewing space as a continuum may yield more relevant information on the spatial distribution of outcomes.

Optimization approaches for generalization and data abstraction

Abstract: The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least‐Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self‐Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scen...

Effective Geographic Sample Size in the Presence of Spatial Autocorrelation

Abstract: As spatial autocorrelation latent in georeferenced data increases, the amount of duplicate information contained in these data also increases. This property suggests the research question asking what the number of independent observations, say , is that is equivalent to the sample size, n, of a data set. This is the notion of effective sample size. Intuitively speaking, when zero spatial autocorrelation prevails, ; when perfect positive spatial autocorrelation prevails in a univariate regional mean problem, . Equations are presented for estimating based on the sampling distribution of a sample mean or sample correlation coefficient with the goal of obtaining some predetermined level of precision, using the following spatial statistical model specifications: (1) simultaneous autoregressive, (2) geostatistical semivariogram, and (3) spatial filter. These equations are evaluated with simulation experiments and are illustrated with selected empirical examples found in the literature.

On geodetic distance computations in spatial modeling.

Abstract: Summary. Statisticians analyzing spatial data often need to detect and model associations based upon distances on the Earth’s surface. Accurate computation of distances are sought for exploratory and interpretation purposes, as well as for developing numerically stable estimation algorithms. When the data come from locations on the spherical Earth, application of Euclidean or planar metrics for computing distances is not straightforward. Yet, planar metrics are desirable because of their easier interpretability, easy availability in software packages, and well-established theoretical properties. While distance computations are indispensable in spatial modeling, their importance and impact upon statistical estimation and prediction have gone largely unaddressed. This article explores the different options in using planar metrics and investigates their impact upon spatial modeling.