Local Models for Spatial Analysis

In both the physical and social sciences, there are now available large spatial data sets with detailed local information. Global models for analyzing these data are not suitable for investigating local variations; consequently, local models are the subject of much recent research. Collecting a variety of models into a single reference, Local Models for Spatial Analysis explains in detail a variety of approaches for analyzing univariate and multivariate spatial data. Different models make use of data in unique ways, and this book offers perspectives on various definitions of what constitutes “local,” varying spatial scales, and nonstationary models. The author discusses analyses of single variables on grids, multiple variables, deterministic approaches to spatial prediction, geostatistical prediction, and point patterns. He uses numerous worked examples, illustrations, and case studies to shed light on issues involved in implementing the concepts in practice, and makes use of physical and social science data sets. In each chapter, the book follows a consistent format that introduces global approaches followed by corresponding local approaches, providing an assessment of the suitability of various methods in particular situations. Combining a valuable array of tools for GIScience and GISystems, Local Models for Spatial Analysis guides you in selecting and applying the most appropriate model for a given purpose and set of data.

Table of Contents:
Introduction Remit of This Book Local Models and Methods What Is Local? Spatial Dependence Spatial Scale Stationarity Spatial Data Models Data Sets Used for Illustrative Purposes A Note on Notation Overview Local Modeling Approaches to Local Adaptation Stratification or Segmentation of Spatial Data Moving Window/Kernel Methods Locally Varying Model Parameters Transforming and Detrending Spatial Data Overview Grid Data Exploring Spatial Variation in Single Variables Global Univariate Statistics Local Univariate Statistics Analysis of Grid Data Moving Windows for Grid Analysis Wavelets Segmentation Analysis of Digital Elevation Models Overview Spatial Relations Spatial Autocorrelation: Global Measures Spatial Autocorrelation: Local Measures Global Regression Local Regression Regression and Spatial Data Spatial Autoregressive Models Multilevel Modeling Allowing for Local Variation in Model Parameters Moving Window Regression (MWR) Geographically Weighted Regression (GWR) Spatially Weighted Classification Overview Spatial Prediction 1: Deterministic Methods Point Interpolation Global Methods Local Methods Areal Interpolation General Approaches: Overlay Local Models and Local Data Limitations: Point and Areal Interpolation Overview Spatial Prediction 2: Geostatistics Random Function Models Stationarity Global Models Exploring Spatial Variation Kriging Equivalence of Splines and Kriging Conditional Simulation The Change of Support Problem Other Approaches Local Approaches: Nonstationary Models Nonstationary Mean Nonstationary Models for Prediction Nonstationary Variogram Variograms in Texture Analysis Summary Point Patterns Point Patterns Visual Examination of Point Patterns Density and Distance Methods Statistical Tests of Point Patterns Global Methods Distance Methods Other Issues Local Methods Density Methods Accounting for the Population at Risk The Local K Function Point Patterns and Detection of Clusters Overview Summary: Local Models for Spatial Analysis Review Key Issues Software Future Developments Summary References Index