Multiscale geographically weighted regression theory and practice
Introduction to local modeling -- MGWR : the essentials -- Inference -- Spatial scale and local modeling -- Software for MGWR -- Caveat emptor! -- A local analysis of voting behavior : the 2020 US presidential election -- MGWR and other models incorporating spatial contextual effects.
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Boca Raton, London, New York
CRC Press
2024
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| Ausgabe: | First edition |
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| Online Zugang: | Inhaltsverzeichnis |
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Tag hinzufügen | Zusammenfassung: | Introduction to local modeling -- MGWR : the essentials -- Inference -- Spatial scale and local modeling -- Software for MGWR -- Caveat emptor! -- A local analysis of voting behavior : the 2020 US presidential election -- MGWR and other models incorporating spatial contextual effects. "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 serves as definitive guide to local regression modeling and the analysis of spatially varying processes, a very cutting-edge, hands-on, and innovative resource. The authors start 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 also provided, as well as an analysis of the 2020 US Presidential election"-- |
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| Beschreibung: | Literaturangaben und Index |
| Beschreibung: | xvii, 176 Seiten Diagramme, Karten |
| ISBN: | 9781032564227 978-1-032-56422-7 9781032564234 978-1-032-56423-4 |