Coverage and Bias of Street View Imagery in Mapping the Urban Environment

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 workflow to estimate SVI's feature-level coverage on urban environment. The workflow integrates the positional relationships between SVI and target features, as well as the impact of environmental 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. Using London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban features, with a focus on building facades. The research highlights the limitations of traditional spatial data quality metrics in assessing SVI, and variability of SVI coverage under different data acquisition practices. Tailored approaches that consider the unique metadata and horizontal perspective of SVI are also underscored. 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 feature-level representativeness to ensure reliable results.