MGWR

A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale

Taylor M. Oshan, Ziqi Li, Wei Kang, Levi J. Wolf, Stewart Fotheringham

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Article number269
JournalISPRS International Journal of Geo-Information
Volume8
Issue number6
DOIs
StatePublished - Jun 8 2019

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regression
functionality
Demonstrations
intuition
linear model
diagnostic
programming
Bandwidth
software
efficiency
parameter
analysis

Keywords

  • Gwr
  • Heterogeneity
  • Mgwr
  • Multiscale
  • Scale
  • Spatial statistics

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

Cite this

MGWR : A python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. / Oshan, Taylor M.; Li, Ziqi; Kang, Wei; Wolf, Levi J.; Fotheringham, Stewart.

In: ISPRS International Journal of Geo-Information, Vol. 8, No. 6, 269, 08.06.2019.

Research output: Contribution to journalArticle

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