While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space.

尽管Google街景（GSV）越来越多地用于城市景观的大规模检查，但对于如何更谨慎，更有效地使用这一有前途的数据源知之甚少。以加利福尼亚州圣安娜的数据为例，本研究对基于GSV的街景测量及其变化模式的敏感性提供了经验评估。结果表明，测量结果会随GSV采集参数设置（尤其是间距和方向）的变化而显着变化。发现对于某些测量目标，包括人，物体和人行道，其灵敏度特别高。其中一些元素（例如建筑物和人行道）也显示出高度相关的变化模式，表明它们在城市空间的马赛克中具有协方差。