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.