[Paper Review] Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR

A. Stewart Fotheringham, Chen-Lun Kao+6 more|arXiv (Cornell University)|Apr 24, 2024

Spatial and Panel Data Analysis5 citations

TL;DR

This work compiles a comprehensive bibliography of peer‑reviewed papers that use Geographically Weighted Regression (GWR) or MGWR as the primary analysis method, across diverse fields. It serves as a guide to literature on local statistical modeling.

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.

Motivation & Objective

• Provide a complete compilation of peer‑reviewed papers that utilize GWR or MGWR as the primary analytical method.

Proposed method

• Assemble and organize a broad bibliography of studies employing GWR or MGWR.
• Highlight the range of application fields and contexts in which GWR/MGWR are used.
• Offer a useful reference guide for researchers seeking prior local statistical modeling work.

Experimental results

Research questions

• RQ1 What peer‑reviewed papers employ GWR or MGWR as the primary analytical method?
• RQ2 Which disciplinary domains and application contexts have utilized GWR/MGWR?
• RQ3 How can researchers leverage this bibliography as a guide to prior work in local spatial modeling.

Key findings

• The bibliography aims to be a comprehensive compilation of papers utilizing GWR or MGWR.
• The work highlights the extensive range of application fields for GWR and MGWR.
• The document functions as a practical guide for researchers seeking previous local statistical modeling studies.
• The bibliography is extensive, noted to comprise 482 pages.