Local Models for Spatial Analysis (2^nd Ed.)

Written in recognition of developments in spatial data analysis that focused on differences between places, the first edition of Local Models for Spatial Analysis broke new ground with its focus on local modelling methods. Reflecting the continued growth and increased interest in this area, the second edition describes a wide range of methods which account for local variations in geographical properties.

What?s new in the Second Edition:

• Additional material on geographically-weighted statistics and local regression approaches
• A better overview of local models with reference to recent critical reviews about the subject area
• Expanded coverage of individual methods and connections between them
• Chapters have been restructured to clarify the distinction between global and local methods
• A new section in each chapter references key studies or other accounts that support the book
• Selected resources provided online to support learning

An introduction to the methods and their underlying concepts, the book uses worked examples and case studies to demonstrate how the algorithms work their practical utility and range of application. It provides an overview of a range of different approaches that have been developed and employed within Geographical Information Science (GIScience). Starting with first principles, the author introduces users of GISystems to the principles and application of some widely used local models for the analysis of spatial data, including methods being developed and employed in geography and cognate disciplines. He discusses the relevant software packages that can aid their implementation and provides a summary list in Appendix A.

Presenting examples from a variety of disciplines, the book demonstrates the importance of local models for all who make use of spatial data. Taking a problem driven approach, it provides extensive guidance on the selection and application of local models.

Introduction. Local Modelling. Grid Data. Spatial Patterning in Single Variables. Spatial Relations. Spatial Prediction 1: Deterministic Methods, Curve Fitting, and Smoothing. Spatial Prediction 2: Geostatistics. Point Patterns and Cluster Detection.
Summary: Local Models for Spatial Analysis. Index.

Christopher D. Lloyd