Street View for Whom? An Initial Examination of Google Street View’s Urban Coverage and Socioeconomic Indicators in the US

Street-level imagery is foundational to modern urban informatics research; however, bias from systematic differences in where and when images are captured can obscure important relationships and impact study findings. We examine how Google Street View (GSV) spatio-temporal capture patterns correlate with ACS socioeconomic indicators and also provide a reproducible, open-source data analysis pipeline and dashboard. To demonstrate and evaluate our approach, we study four US cities computing correlation coefficients between image staleness and social-demographic, mobility, and housing-related variables. Our findings demonstrate systematic spatial disparities in GSV coverage: neighborhoods characterized by urban density and diverse demographics tend to have more current imagery, whereas areas with suburban or higher-income profiles frequently lag behind.