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.

翻译：诸如地理加权回归（GWR）和多尺度地理加权回归（MGWR）等局部空间模型，通过估计局部截距捕捉固有上下文效应，并借助局部斜率参数估计描述行为上下文效应，成为重要的分析工具。GWR和MGWR提供了易于实现但功能强大的框架，可扩展至处理空间数据的多个学科领域。本文献目录旨在系统汇编将GWR或MGWR作为主要分析方法进行空间分析的相关同行评审论文，为各类应用领域中搜索局部统计建模文献的研究者提供实用指南。