Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR

TL;DR

This paper compiles a comprehensive bibliography of peer-reviewed studies using GWR and MGWR, highlighting their applications across various disciplines and emphasizing their utility in capturing local spatial effects.

Contribution

It provides the first extensive bibliography of GWR and MGWR applications, serving as a valuable resource for researchers in spatial analysis.

Findings

GWR and MGWR are widely used in diverse fields.

The bibliography covers numerous application examples.

These models effectively capture local spatial variations.

Abstract

Local spatial models such as Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) serve as instrumental tools to capture intrinsic contextual effects through the estimates of the local intercepts and behavioral contextual effects through estimates of the local slope parameters. GWR and MGWR provide simple implementation yet powerful frameworks that could be extended to various disciplines that handle spatial data. This bibliography aims to serve as a comprehensive compilation of peer-reviewed papers that have utilized GWR or MGWR as a primary analytical method to conduct spatial analyses and acts as a useful guide to anyone searching the literature for previous examples of local statistical modeling in a wide variety of application fields.