A Route Map for Successful Applications of Geographically Weighted Regression
A Route Map for Successful Applications of Geographically Weighted Regression
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DOI：
10.1111/gean.12316
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发表时间：
2022-01-09
影响因子：
3.6
通讯作者：
Harris, Paul
中科院分区：
文献类型：
--
作者：
Comber, Alexis;Brunsdon, Christopher;Harris, Paul
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。它允许通过一系列的本地回归模型，而不是一个单一的全球的过程和关系的空间异质性进行调查。标准GWR假设响应变量和预测变量之间的关系在相同的空间尺度上运行，但情况往往并非如此。为了解决这个问题，已经提出了几种GWR变体。本文描述了一个路线图，以决定是否使用GWR模型或不，如果是这样的三个核心变量：一个标准的GWR，混合GWR或多尺度GWR（MS-GWR）的应用。路线图包括应始终进行的3个主要步骤：（1）基本线性回归，（2）MS-GWR，以及（3）调查这些结果，以决定是否使用GWR方法，以及如果使用，则确定适当的GWR变体。本文还强调了在全球和局部尺度上调查一些次要问题的重要性，包括共线性、离群值的影响和相关误差项。提供了用于说明路线图的案例研究的代码和数据。
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 single global one. Standard GWR assumes that 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 decide 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 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights 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.