mgwr: a Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale

Oshan, T. M., Li, Z. , Kang, W., Wolf, L. J. and Fotheringham, A. S. (2019) mgwr: a Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269. (doi: 10.3390/ijgi8060269)

[img] Text
253046.pdf - Published Version
Available under License Creative Commons Attribution.



Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional ‘global’ regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by allowing effects to vary over space. To do this, GWR calibrates an ensemble of local linear models at any number of locations using ‘borrowed’ nearby data. This provides a surface of location-specific parameter estimates for each relationship in the model that is allowed to vary spatially, as well as a single bandwidth parameter that provides intuition about the geographic scale of the processes. A recent extension to this framework allows each relationship to vary according to a distinct spatial scale parameter, and is therefore known as multiscale (M)GWR. This paper introduces mgwr, a Python-based implementation of MGWR that explicitly focuses on the multiscale analysis of spatial heterogeneity. It provides novel functionality for inference and exploratory analysis of local spatial processes, new diagnostics unique to multi-scale local models, and drastic improvements to efficiency in estimation routines. We provide two case studies using mgwr, in addition to reviewing core concepts of local models. We present this in a literate programming style, providing an overview of the primary software functionality and demonstrations of suggested usage alongside the discussion of primary concepts and demonstration of the improvements made in mgwr.

Item Type:Articles
Keywords:Multiscale, gwr, spatial statistics, heterogeneity, scale, mgwr.
Glasgow Author(s) Enlighten ID:Li, Dr Ziqi
Creator Roles:
Li, Z.Methodology, Software, Formal analysis, Writing – review and editing
Authors: Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., and Fotheringham, A. S.
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:ISPRS International Journal of Geo-Information
ISSN (Online):2220-9964
Published Online:08 June 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in ISPRS International Journal of Geo-Information 8(6): 269
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record