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This study develops a new method to quantify neighborhood activity levels athigh spatial and temporal resolutions and test whether, and to what extent,behavioral responses to social distancing policies vary with socioeconomic anddemographic characteristics. We define exposure density as a measure of boththe localized volume of activity in a defined area and the proportion ofactivity occurring in non-residential and outdoor land uses. We utilize thisapproach to capture inflows/outflows of people as a result of the pandemic andchanges in mobility behavior for those that remain. First, we develop ageneralizable method for assessing neighborhood activity levels by land usetype using smartphone geolocation data over a three-month period covering morethan 12 million unique users within the Greater New York area. Second, wemeasure and analyze disparities in community social distancing by identifyingpatterns in neighborhood activity levels and characteristics before and afterthe stay-at-home order. Finally, we evaluate the effect of social distancing inneighborhoods on COVID-19 infection rates and outcomes associated withlocalized demographic, socioeconomic, and infrastructure characteristics inorder to identify disparities in health outcomes related to exposure risk. Ourfindings provide insight into the timely evaluation of the effectiveness ofsocial distancing for individual neighborhoods and support a more equitableallocation of resources to support vulnerable and at-risk communities. Ourfindings demonstrate distinct patterns of activity pre- and post-COVID acrossneighborhoods. The variation in exposure density has a direct and measurableimpact on the risk of infection.

Exposure Density and Neighborhood Disparities in COVID-19 Infection  Risk: Using Large-scale Geolocation Data to Understand Burdens on Vulnerable  Communities