Exploring Spatial Context: A Comprehensive Bibliography of GWR and MGWR

2024-04-24

AI Summary

Geographic Weighted Regression (GWR) and Multiscale GWR (MGWR) lack comprehensive, systematic literature reviews across disciplines. Method: We construct the first open, cross-disciplinary bibliography of empirical GWR/MGWR studies, curating over 1,000 peer-reviewed articles through bibliometric analysis, topic modeling, and metadata standardization—enabling interdisciplinary classification, methodological annotation, and structured provenance tracking. Contribution/Results: We innovatively map the methodological evolution of GWR/MGWR and identify discipline-specific adaptation patterns, proposing a unified application taxonomy. This work fills a critical gap in systematic syntheses of local spatial statistical methods, delivering a searchable, reusable academic infrastructure to support method selection, pedagogy, and interdisciplinary spatial modeling research.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Summarize peer-reviewed papers using GWR and MGWR
• Provide a guide for local statistical modeling examples
• Compile spatial analyses applications across disciplines

Innovation

• Uses Geographically Weighted Regression (GWR)
• Implements Multiscale GWR (MGWR)
• Captures spatial contextual effects locally