  This study develops a new method to quantify neighborhood activity levels at
high spatial and temporal resolutions and test whether, and to what extent,
behavioral responses to social distancing policies vary with socioeconomic and
demographic characteristics. We define exposure density as a measure of both
the localized volume of activity in a defined area and the proportion of
activity occurring in non-residential and outdoor land uses. We utilize this
approach to capture inflows/outflows of people as a result of the pandemic and
changes in mobility behavior for those that remain. First, we develop a
generalizable method for assessing neighborhood activity levels by land use
type using smartphone geolocation data over a three-month period covering more
than 12 million unique users within the Greater New York area. Second, we
measure and analyze disparities in community social distancing by identifying
patterns in neighborhood activity levels and characteristics before and after
the stay-at-home order. Finally, we evaluate the effect of social distancing in
neighborhoods on COVID-19 infection rates and outcomes associated with
localized demographic, socioeconomic, and infrastructure characteristics in
order to identify disparities in health outcomes related to exposure risk. Our
findings provide insight into the timely evaluation of the effectiveness of
social distancing for individual neighborhoods and support a more equitable
allocation of resources to support vulnerable and at-risk communities. Our
findings demonstrate distinct patterns of activity pre- and post-COVID across
neighborhoods. The variation in exposure density has a direct and measurable
impact on the risk of infection.
