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地理位置加权的随机森林（GRF）是一种空间分析方法，它适合随机森林算法的局部范围，用于研究空间非平稳性，在依赖性变量和一组自变量之间的关系中。可以考虑到相邻的观测值，可以通过为空间中的每个观测值拟合子模型来实现后者。这项技术采用了地理位置加权回归的想法，Kalogirou（2003）。它以灵活的非线性方法对非平稳性进行建模，从而弥合机器学习和地理模型之间的差距。The main difference between a tradition (linear) GWR and GRF is that we can model non-stationarity coupled with a flexible non-linear model which is very hard to overfit due to its bootstrapping nature, thus relaxing the assumptions of traditional Gaussian statistics.GRF is suitable for datasets with numerous predictors due to the robustness of the random forest algo- rithm in high dimensionality.
spatialml：空间机器学习
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