Ordinary kriging, a spatial interpolation technique, is commonly used in social sciences to estimate neighborhood attributes such as physical disorder. Universal kriging, developed and used in physical sciences, extends ordinary kriging by supplementing the spatial model with additional covariates. We measured physical disorder on 1,826 sampled block faces across four U.S. cities (New York, Philadelphia, Detroit, and San Jose) using Google Street View imagery. We then compared leave-one-out cross-validation accuracy between universal and ordinary kriging and used random subsamples of our observed data to explore whether universal kriging could provide equal measurement accuracy with less spatially dense samples. Universal kriging did not always improve accuracy. However, a measure of housing vacancy did improve estimation accuracy in Philadelphia and Detroit (7.9 percent and 6.8 percent lower root mean square error, respectively) and allowed for equivalent estimation accuracy with half the sampled points in Philadelphia. Universal kriging may improve neighborhood measurement.

中文翻译：

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使用通用克里金法改进社区身体疾病测量

普通克里金法是一种空间插值技术，通常用于社会科学中来估计邻域属性，例如身体障碍。在物理科学中开发和使用的通用克里金法通过用额外的协变量补充空间模型来扩展普通克里金法。我们使用 Google 街景图像测量了美国四个城市（纽约、费城、底特律和圣何塞）的 1,826 个采样街区表面的身体紊乱情况。然后，我们比较了通用克里金法和普通克里金法之间的留一法交叉验证精度，并使用观测数据的随机子样本来探索通用克里金法是否可以使用空间密度较低的样本提供相同的测量精度。通用克里金法并不总能提高准确性。然而，住房空置率的衡量确实提高了费城和底特律的估计准确度（均方根误差分别降低了 7.9% 和 6.8%），并且允许费城一半的采样点获得同等的估计准确度。通用克里金法可以改善邻域测量。