Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations
Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations
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DOI：
10.1080/13658816.2018.1521523
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发表时间：
2019-01-01
影响因子：
5.7
通讯作者：
Oshan, Taylor
中科院分区：
文献类型：
--
作者：
Li, Ziqi;Fotheringham, A. Stewart;Oshan, Taylor
地理加权回归（GWR）是一种广泛使用的工具，用于探索地理空间过程的空间异质性。GWR计算特定于位置的参数估计，这使得其校准过程的计算密集型。当前开源GWR软件可以处理的最大数据点数量是标准桌面上的大约15，000个观察结果。在大数据时代，这严重限制了GWR的使用。为了克服这一限制，我们提出了一个高度可扩展的，开源的FastGWR实现基于Python和消息传递接口（MPI），可扩展到数百万个观察的顺序。FastGWR优化了内存使用沿着以显著提高性能。为了说明FastGWR的性能，我们在洛杉矶市Zillow数据集中的大约130万个单户住宅物业上校准了一个享乐房价模型，这是第一次将GWR应用于这种规模的数据集。结果表明，FastGWR随着高性能计算（HPC）环境中核心数量的增加而线性扩展。它还优于目前可用的开源GWR软件包，在标准桌面上大幅降低速度-高达数千倍。
Geographically Weighted Regression (GWR) is a widely used tool for exploring spatial heterogeneity of processes over geographic space. GWR computes location-specific parameter estimates, which makes its calibration process computationally intensive. The maximum number of data points that can be handled by current open-source GWR software is approximately 15,000 observations on a standard desktop. In the era of big data, this places a severe limitation on the use of GWR. To overcome this limitation, we propose a highly scalable, open-source FastGWR implementation based on Python and the Message Passing Interface (MPI) that scales to the order of millions of observations. FastGWR optimizes memory usage along with parallelization to boost performance significantly. To illustrate the performance of FastGWR, a hedonic house price model is calibrated on approximately 1.3 million single-family residential properties from a Zillow dataset for the city of Los Angeles, which is the first effort to apply GWR to a dataset of this size. The results show that FastGWR scales linearly as the number of cores within the High-Performance Computing (HPC) environment increases. It also outperforms currently available open-sourced GWR software packages with drastic speed reductions - up to thousands of times faster - on a standard desktop.