Mapping the results of local statistics: Using geographically weighted regression.
Mapping the results of local statistics: Using geographically weighted regression.
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DOI：
10.4054/demres.2012.26.6
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发表时间：
2012-03-02
影响因子：
2.1
通讯作者：
Yang TC
中科院分区：
文献类型：
--
作者：
Matthews SA;Yang TC
地理加权回归（GWR）——一种用于检验空间非平稳性的局部空间统计技术——在社会、卫生和人口科学领域的应用迅速增长。GWR是一种有用的探索性分析工具，可以生成一组特定于位置的参数估计，这些参数估计可以被映射和分析，以提供预测因子和结果变量之间关系的空间非平稳性信息。然而，GWR用户面临的一个主要挑战是如何最好地映射这些参数估计。本文介绍了一种简单的映射技术，将局部参数估计和局部t值结合在一个映射上。生成的地图有助于探索和解释非平稳性。
The application of geographically weighted regression (GWR) – a local spatial statistical technique used to test for spatial nonstationarity – has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity.