The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets
The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets
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DOI：
10.1007/s11004-010-9284-7
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发表时间：
2010-08-01
影响因子：
2.6
通讯作者：
Charlton, M.
中科院分区：
文献类型：
--
作者：
Harris, P.;Fotheringham, A. S.;Charlton, M.
地理加权回归（GWR）模型越来越多地被用于空间预测而不是推理。我们的研究比较了GWR作为预测因子与(a)其全球对应的多元线性回归（MLR）；(b)以MLR为平均分量的传统地质统计模型，如普通克里格模型（OK）和通用克里格模型（UK）；(c)混合，其中克里格模型指定GWR作为平均分量。为此，我们测试了每个模型在不同水平的空间异质性（相对于平均过程中的数据关系）和空间自相关（残差过程）模拟数据上的性能。我们的结果表明，克里格（在英国形式）应该是首选的预测，反映其最佳的统计特性。然而，GWR-kriging混合动力车表现优异，因此，当英国模型无法可靠校准时，这种形式的预测器可能为英国提供特殊（非平稳关系）情况的有价值的替代方案。GWR预测器往往比其更复杂的GWR-kriging对应物表现得更差，但是两个基于GWR的模型都很有用，因为它们提供了关于生成被预测数据的空间过程的额外信息。
Increasingly, the geographically weighted regression (GWR) model is being used for spatial prediction rather than for inference. Our study compares GWR as a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) traditional geostatistical models such as ordinary kriging (OK) and universal kriging (UK), with MLR as a mean component; and (c) hybrids, where kriging models are specified with GWR as a mean component. For this purpose, we test the performance of each model on data simulated with differing levels of spatial heterogeneity (with respect to data relationships in the mean process) and spatial autocorrelation (in the residual process). Our results demonstrate that kriging (in a UK form) should be the preferred predictor, reflecting its optimal statistical properties. However the GWR-kriging hybrids perform with merit and, as such, a predictor of this form may provide a worthy alternative to UK for particular (non-stationary relationship) situations when UK models cannot be reliably calibrated. GWR predictors tend to perform more poorly than their more complex GWR-kriging counterparts, but both GWR-based models are useful in that they provide extra information on the spatial processes generating the data that are being predicted.