街景图像在城市环境制图中的覆盖度与偏差
Coverage and Bias of Street View Imagery in Mapping the Urban Environment
AI总结：
本研究提出一种结合位置关系与物理遮挡的SVI要素级覆盖度估计方法，以伦敦为例发现谷歌街景仅覆盖62.4%建筑，且存在非住宅过度表征偏差，建议采样间隔50-60米。
摘要：
街景图像（SVI）已成为城市研究中一种有价值的数据形式，为绘制和感知城市环境提供了新途径。然而，关于SVI的代表性、质量和可靠性的基本问题仍未得到充分探索，例如，城市能在多大程度上被此类数据所捕获，以及数据缺口是否会导致偏差。本研究定位于空间数据质量与城市分析的交叉领域，通过提出一种新颖且有效的方法来估计SVI在城市环境中的要素级覆盖度，从而解决上述问题。该方法整合了SVI与目标要素之间的位置关系以及物理遮挡的影响。将数据质量的领域扩展到SVI，我们引入了一套评估覆盖程度的指标体系，重点关注完整性和频率两个维度。以伦敦为案例研究，我们进行了三项实验，以建筑立面为例，识别SVI在覆盖和表征城市环境要素方面可能存在的偏差。研究发现，尽管谷歌街景在城市道路沿线具有较高的可用性，但其仅覆盖了案例研究区域内62.4%的建筑物。每栋建筑的平均立面覆盖率为12.4%。SVI倾向于过度表征非住宅建筑，因此可能导致有偏差的分析，且其对环境要素的覆盖具有位置依赖性。研究还强调了不同数据采集实践下SVI覆盖度的变异性，并提出了SVI采集的最优采样间隔范围为50-60米。研究结果表明，尽管SVI提供了有价值的见解，但它并非万能之策——其在城市研究中的应用需要仔细考虑数据覆盖度和要素级代表性，以确保结果的可靠性。
英文摘要：
Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g. to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel and effective method to estimate SVI's element-level coverage in the urban environment. The method integrates the positional relationships between SVI and target elements, as well as the impact of physical obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Taking London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban environmental elements, using building facades as an example. It is found that despite their high availability along urban road networks, Google Street View covers only 62.4 % of buildings in the case study area. The average facade coverage per building is 12.4 %. SVI tends to over-represent non-residential buildings, thus possibly resulting in biased analyses, and its coverage of environmental elements is position-dependent. The research also highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range of 50-60 m for SVI collection. The findings suggest that while SVI offers valuable insights, it is no panacea - its application in urban research requires careful consideration of data coverage and element-level representativeness to ensure reliable results.
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