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An examination of the spatial coverage and temporal variability of Google Street View (GSV) images in small- and medium-sized cities: A people-based approach

机译:中小城市谷歌街景(GSV)图像的空间覆盖率和时间变异性研究:一种以人为本的方法

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? 2023 Elsevier LtdWe conduct a methodological investigation of the spatial coverage and temporal variability of Google Street View (GSV) images adopting a people-based approach. Specifically, we focus on walk commute trajectories (i.e., home-to-work trips) of 97,505 people from 45 small- and medium-sized cities in the U.S., which have been rarely discussed in previous studies. Our results reveal that 44 of commute routes do not have adequate GSV image spatial coverage. Results also demonstrate the substantial variability in their temporal ranges. We reveal that the average monthly variation in timestamps of GSV images on commute trajectories is approximately seven years, and only about 10 of samples contain GSV images taken within one year. Lastly, we illustrate regional differences in the spatial coverage scores and temporal variability levels of the 45 cities. As our results demonstrate that GSV images are imperfect in their spatial coverage and temporal variability, we recommend researchers be aware of these methodological limitations and potential negative impacts on their conclusions.
机译:?2023 爱思唯尔有限公司我们采用以人为本的方法对谷歌街景 (GSV) 图像的空间覆盖范围和时间变异性进行了方法论调查。具体来说,我们关注了来自美国 45 个中小城市的 97,505 人的步行通勤轨迹(即从家到工作的旅行),这在以前的研究中很少讨论。我们的研究结果表明,44%的通勤路线没有足够的GSV图像空间覆盖。结果还表明,它们的时间范围存在很大差异。我们发现,GSV图像在通勤轨迹上的时间戳平均每月变化约为7年,只有约10%的样本包含一年内拍摄的GSV图像。最后,我们说明了45个城市在空间覆盖得分和时间变异性水平方面的区域差异。由于我们的结果表明,GSV图像在空间覆盖范围和时间变异性方面并不完美,因此我们建议研究人员意识到这些方法学上的局限性以及对其结论的潜在负面影响。

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