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    Area of Science:

    • Public Health
    • Urban Planning
    • Geospatial Analysis

    Background:

    • Creating environments that support physical activity, including active transportation, is a public health priority.
    • Traditional surveillance methods for active transportation often have time lags and lack geographic specificity.
    • User-generated data from GPS devices offers near real-time information but may face self-selection bias.

    Purpose of the Study:

    • To analyze the association between GPS-based commuting data and population-representative commuting data.
    • To assess the utility of user-generated data for active transportation surveillance.
    • To compare data from a mobile application (Strava) with U.S. Census Bureau data.

    Main Methods:

    • Analysis of GPS-based commuting data from Strava users.
    • Comparison with U.S. Census Bureau's American Community Survey (ACS) data.
    • Level of analysis at the Census block group for four U.S. cities.

    Main Results:

    • A significant association was found between GPS-tracked commuters and ACS active commuters (Spearman's rho = 0.60).
    • Block groups were ranked similarly by both data sources, indicating comparable patterns.
    • The correlation between datasets was stronger in areas with higher population density.

    Conclusions:

    • User-generated active transportation data can complement traditional surveillance systems.
    • This data provides near real-time, location-specific insights into active transportation.
    • Findings support the use of novel data sources for public health efforts in urban planning and active transport promotion.