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Multiscale Geographically Weighted Regression (MGWR) is an
important method that is used across many disciplines for exploring
spatial heterogeneity and modeling local spatial processes. This
book introduces the concepts behind local spatial modeling and
explains how to model heterogeneous spatial processes within a
regression framework. It starts with the basic ideas and
fundamentals of local spatial modeling followed by a detailed
discussion of scale issues and statistical inference related to
MGWR. A comprehensive guide to free, user-friendly, software for
MGWR is provided, as well as an example of the application of MGWR
to understand voting behaviour in the 2020 US Presidential
election. This book is the definitive guide to local regression
modeling and the analysis of spatially varying processes, a very
cutting-edge, hands-on, and innovative resource. Features Provides
a balance between conceptual and technical introduction to local
models Explains state-of-the-art spatial analysis technique for
multiscale regression modeling Describes best practices and
provides a detailed walkthrough of freely available software,
through examples and comparisons with other common spatial data
modeling techniques Includes a detailed case study to demonstrate
methods and software Takes a new and exciting angle on local
spatial modeling using MGWR, an innovation to the previous local
modeling ‘bible’ GWR Ideal for senior undergraduate and
graduate students in advanced spatial analysis and GIS courses
taught in any spatial science discipline as well as for
researchers, academics, and professionals who want to understand
how location can affect human behavior through local regression
modeling.
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