
Multiscale Geographically Weighted Regression
Beschreibung
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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
The book is 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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Personen
Taylor Oshan is an Assistant Professor in the Center for Geospatial Information Science in the Department of Geographical Sciences, University of Maryland, as well as an affiliate of the Social Data Science Center, the Maryland Population Research Center, and the Maryland Transportation Institute. His research focuses on developing and applying multiscale methods and local statistical models, particularly of human processes within urban environments, to understand how relationships change across different spatial contexts. He also leads projects to develop open source tools for spatial analysis, including the core algorithms for the Multiscale Geographically Weighted Regression software amongst others. He has published over 25 peer-reviewed manuscripts and has collaborated or lead funded projects totaling over $2.3m. He was elected as a board member in 2021 for the Spatial Analysis and Modeling specialty group of the American Association of Geographers and joined Applied Spatial Analysis and Policy as a co-editor-in-chief in 2023.
Ziqi Li is an Assistant Professor of Quantitative Geography in the Department of Geography at Florida State University. His research focuses on the methodological development of spatially explicit and explainable statistical and machine learning models, and he is one of the primary contributors to the field of Multiscale Geographically Weighted Regression. He has published over 20 peer-reviewed journal articles in these areas. He is a winner of multiple prestigious international awards including the Nystrom Award by the American Association of Geographers (AAG) in 2021 and the John Odland Award by the Spatial Analysis and Modeling Group of AAG in 2020.
Inhalt
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