Computational improvements to multi-scale geographically weighted regression
Computational improvements to multi-scale geographically weighted regression
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DOI：
10.1080/13658816.2020.1720692
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发表时间：
2020-02-08
影响因子：
5.7
通讯作者：
Fotheringham, A. Stewart
中科院分区：
文献类型：
--
作者：
Li, Ziqi;Fotheringham, A. Stewart
地理加权回归（GWR）已被广泛应用于各个领域的空间非平稳关系建模。多尺度地理加权回归（MGWR）是经典GWR模型的一个新进展。与传统的单尺度GWR模型相比，MGWR模型通过对每个协变量使用不同的带宽，在捕获多尺度过程方面具有上级优势。然而，MGWR的多尺度特性带来了额外的计算成本。MGWR的校准过程涉及在添加剂模型（AM）框架下的迭代后拟合。目前，MGWR只能在可容忍的时间内应用于小型数据集，并且对于中等规模的数据集（超过5，000个观测值）运行非常耗时。在本文中，我们提出了一种并行实现，具有至关重要的计算改进的MGWR校准。这种改进的计算方法减少了内存占用和运行时间，允许MGWR建模应用于中型到大型数据集（高达100，000个观测值）。这些改进已集成到mgwr python包和MGWR 2.0软件中，这两个软件都可以免费下载。
Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.