Developing Nationwide Estimates of Built Environment Quality Characteristics Using Street-View Imagery and Computer Vision.

Environmental health studies commonly rely on urban composition measures for built environment exposure assessment. However, quality measures are equally important, as they directly influence health behaviors. We leveraged computer vision and street-view imagery to estimate five components of built environment quality (perceived beauty, relaxation potential, nature quality, safe for walking, and safety from crime) across all U.S. cities, explicitly addressing socio-demographic and temporal biases. We collected 72 516 surveys via Amazon Mechanical Turk, where participants ranked street-view images and provided socio-demographic data. Deep learning models predicted quality metrics at 120 million street locations for 2008, 2012, 2016, and 2020. Cross-validation accuracy ranged from 73% (nature quality) to 59% (safety from crime) compared to 50% expected by random chance. Adjusting sampling weights based on demographics reduced but did not eliminate biases for Hispanic/Latino and Native Hawaiian or Pacific Islander groups (3.5 and 4% lower accuracy, respectively). We also adjusted model predictions for seasonal biases, correcting higher scores from late spring and early summer imagery ( < 0.001). The resulting nationwide estimates of street-level beauty, relaxation, nature quality, and safety for walking (but not safety from crime) can inform epidemiological research, urban planning strategies, and public health interventions.