ABSTRACT The physical and social processes in urban systems are inherently spatial and hence data describing them contain spatial autocorrelation (a proximity-based interdependency on a variable) that need to be accounted for. Standard k-fold cross-validation (KCV) techniques that attempt to measure the generalisation performance of machine learning and statistical algorithms are inappropriate in this setting due to their inherent i.i.d assumption, which is violated by spatial dependency. As such, more appropriate validation methods have been considered, notably blocking and spatial k-fold cross-validation (SKCV). However, the physical barriers and complex network structures which make up a city’s landscape mean that these methods are also inappropriate, largely because the travel patterns (and hence Spatial Autocorrelation (SAC)) in most urban spaces are rarely Euclidean in nature. To overcome this problem, we propose a new road distance and travel time k-fold cross-validation method, RT-KCV. We show how this outperforms the prior art in providing better estimates of the true generalisation performance to unseen data.

中文翻译：

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用于城市建模的道路和旅行时间交叉验证

摘要 城市系统中的物理和社会过程本质上是空间性的，因此描述它们的数据包含需要考虑的空间自相关性（基于邻近性的对变量的相互依赖性）。尝试测量机器学习和统计算法的泛化性能的标准 k 折交叉验证 (KCV) 技术在此设置中是不合适的，因为它们固有的 iid 假设被空间依赖性所违反。因此，已经考虑了更合适的验证方法，特别是分块和空间 k 折交叉验证 (SKCV)。然而，构成城市景观的物理障碍和复杂的网络结构意味着这些方法也不合适，主要是因为大多数城市空间中的出行模式（以及因此的空间自相关 (SAC)）本质上很少是欧几里得的。为了克服这个问题，我们提出了一种新的道路距离和旅行时间 k 折交叉验证方法，RT-KCV。我们展示了它如何在对未知数据的真实泛化性能提供更好的估计方面优于现有技术。