Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England
Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England
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DOI：
10.1111/j.1467-9671.2009.01181.x
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发表时间：
2010-02-01
影响因子：
2.4
通讯作者：
Longley, Paul
中科院分区：
文献类型：
--
作者：
Harris, Richard;Singleton, Alex;Longley, Paul
地理加权回归（GWR）是一种空间统计分析方法，用于探索一个或多个预测变量对响应变量的影响的地理差异。然而，作为局部分析的一种形式，由于重复拟合和比较多个回归曲面的过程，它不能很好地扩展到（特别是）大型数据集。一种解决方案是利用正在开发的电网基础设施，比如英国国家电网服务（NGS）提供的基础设施，将GWR视为一个“尴尬的并行”问题，并在现有的软件平台上构建，在GWR的开源实现（in R）和电网系统之间提供一座桥梁。为了证明这一方法，我们将其应用于高等教育参与的案例研究，使用GWR来检测参与的社会、文化和人口指标的空间差异。
Geographically Weighted Regression (GWR) is a method of spatial statistical analysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis, it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an "embarrassing parallel" problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.