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.
