论文标题
GWR路线图：地理加权回归的知情应用指南
The GWR route map: a guide to the informed application of Geographically Weighted Regression
论文作者
论文摘要
地理加权回归（GWR）越来越多地用于社会和环境数据的空间分析。它允许通过一系列局部回归模型而不是全球回归模型来研究过程和关系中的空间异质性。标准GWR假定响应和预测变量之间的关系在相同的空间尺度上运行，这通常不是这种情况。为了解决这个问题，已经提出了几种GWR变体。本文介绍了一个路由图，以告知是否使用GWR模型的选择，如果是这样，则要应用三个核心变体中的哪一个：标准的GWR，混合GWR或多尺度GWR（MS-GWR）。路由图包括主要步骤：基本线性回归，MS-GWR以及对这些结果的结果的研究。本文提供了决定是否使用GWR方法的指导，如果是使用GWR方法，则提供了指导。它描述了研究全球和本地量表的许多次要问题的重要性，包括共线性，异常值的影响以及依赖错误术语。提供了用于说明路线图的案例研究的代码和数据，并在广泛的附录中描述了进一步的考虑。
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a global one. Standard GWR assumes that the relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to inform the choice of whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises primary steps: a basic linear regression, a MS-GWR, and investigations of the results of these. The paper provides guidance for deciding whether to use a GWR approach, and if so for determining the appropriate GWR variant. It describes the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided, and further considerations are described in an extensive Appendix.
