Outdoor social distancing behaviors changed during a pandemic: A longitudinal analysis using street view imagery

• Published in: PloS one Vol. 19; no. 12; p. e0315132
• Main Authors: Martell, Matthew, Salazar, Chris, Errett, Nicole A., Miles, Scott B., Wartman, Joseph, Choe, John Y.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.12.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Censuses; Cities; Compliance; Computer vision; Control; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 - psychology; COVID-19 - transmission; Data collection; Datasets; Disease control; Disease transmission; Engineering and Technology; Epidemics; Health risks; Human subjects; Humans; Imagery; Infectious diseases; Influence; Longitudinal Studies; Medicine and Health Sciences; Museums; Outdoors; Pandemics; Pandemics - prevention & control; Pedestrians; Pedestrians - psychology; Physical Distancing; Physical Sciences; Public health; Quantitative analysis; Quantitative research; Research and Analysis Methods; SARS-CoV-2 - isolation & purification; Schools; Seasonal variations; Social behavior; Social distancing; Socioeconomic factors; Streets; Trends; United States; United States; United States > US; Washington (state)
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0315132

Social distancing, defined as maintaining a minimum interpersonal distance (often 6 ft or 1.83 m), is a non-pharmaceutical intervention to reduce infectious disease transmission. While numerous quantitative studies have examined people’s social distancing behaviors using mobile phone data, large-scale quantitative analyses of adherence to suggested minimum interpersonal distances are lacking. We analyzed pedestrians’ social distancing behaviors of using 3 years of street view imagery collected in a metropolitan city (Seattle, WA, USA) during the COVID-19 pandemic. We employed computer vision techniques to locate pedestrians in images, and a geometry-based algorithm to estimate physical distance between them. Our results indicate that social distancing behaviors correlated with key factors such as vaccine availability, seasonality, and local socioeconomic data. We also identified behavioral differences at various points of interest within the city (e.g., parks, schools, faith-based organizations, museums). This work represents a first of its kind longitudinal study of outdoor social distancing behaviors using computer vision. Our findings provide key insights for policymakers to understand and mitigate infectious disease transmission risks in outdoor environments.