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：地理加权回归（GWR）与多尺度地理加权回归（MGWR）等局部空间模型，通过局部截距估计捕捉内在语境效应，并通过局部斜率参数估计揭示行为语境效应，成为解析空间异质性的关键工具。GWR与MGWR提供了简洁易行且功能强大的分析框架，可拓展至众多处理空间数据的学科领域。本文献综述系统汇编了以GWR或MGWR为核心空间分析方法的同行评议论文，旨在为跨学科研究者提供全面的文献导引，助力其在多样化的应用领域中追溯局部统计建模的先前研究范例。