Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors 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 distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density city-wide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities.As of December 17, 2020, there have been 73 million cases of COVID-19 in more than 200 countries, and 1.6 million people have lost their lives to the disease (
1). The COVID-19 pandemic is considered the most severe public health crisis since the 1918 flu pandemic due to its transmission and infection characteristics (
2–
5). Social distancing (also referred to as physical distancing) has been shown to be an effective behavioral nonpharmaceutical intervention to reduce the transmission rate of COVID-19 (
3–
7). Social distancing reduces the probability of contacts between individuals who might be infected, resulting in reduced exposure risk (
7,
8). Governments have implemented a range of social-distancing policies, including travel bans, restrictions on gatherings, school closures, nonessential business closures, and restaurant restrictions. In particularly hard-hit locations, mandatory “stay-at-home” orders have been issued to limit or avoid unnecessary close contacts outside of the home (
7–
9).Studies have found that social-distancing measures help to prevent transmission of the virus and reduce the reproduction (R
0) number (
5–
7,
10–
14). These practices help to avoid overwhelming hospital intensive care units and healthcare systems, control doubling time of infections, and ultimately save lives (
5,
8,
14,
15). Although not without potentially significant hardship to individuals and communities, social distancing is an important public health tool to flatten the epidemic curve and support longer-term economic and public health benefits (
3,
15–
17).However, the impact of, and response to, stay-at-home orders and social-distancing guidelines is not uniform across neighborhoods and communities (
18,
19). In order to maximize the positive effects of social distancing, individuals need to change their typical behavior, often dramatically (
3,
20). Despite government-mandated social-distancing policies (such as New York State’s PAUSE order), socio-behavioral responses vary across neighborhoods, further contributing to disparities in risk of infection (
4,
7,
21). Disparities in social-distancing practices—namely, geographic or population subgroup differences in adopting behavior changes in response to the same policy context—may stem from varying levels of awareness, perception, or belief in the severity of the virus threat; differences in social and cultural norms; or the ability of households and communities to alter normal activity patterns given economic constraints or other existing responsibilities (
7,
20–
23). For example, lower-income households typically do not have the option to work from home, and going to a place of work (often in essential services) is unavoidable, meaning higher risk of exposure to COVID-19 for themselves, as well as their families and communities (
7,
24). Within specific neighborhoods, norms can also be reinforcing; if large numbers of residents are essential workers and not socially distancing, other residents may have similar behavioral responses (
20).A growing number of outbreaks are occurring in densely populated areas (
25), with disproportionate impacts on lower-income and predominantly minority communities (
18,
26–
28). Measuring and understanding social distancing and behavior change across neighborhoods can provide critical insight into the design and implementation of more effective—and equitable—public health policy. Given the potential heterogeneity in localized responses to social-distancing recommendations, quantifying local patterns of activity represents an emerging tool to understand and eventually reduce local exposure risk and limit community outbreaks (
7,
29,
30). Although there has been increasing awareness of the troubling disparities in infection rates and outcomes in vulnerable communities, the effectiveness of behavioral interventions at the scale of individual neighborhoods has not been fully studied. Often, studies that do attempt to observe effects at higher spatial resolutions rely on simulations or are limited to relatively coarse areal units (e.g., county or state) due to data availability and computational constraints (
31–
34). Absent a more complete understanding of neighborhood activity patterns in response to nonpharmaceutical interventions, disaggregating built-environment, behavioral, and social determinants of health in the context of COVID-19 remains a challenge.We develop a method to quantify neighborhood activity at high spatial and temporal resolutions to test whether—and to what extent—behavioral responses to social-distancing policies vary with socioeconomic, demographic, and built-environment 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 nonresidential and outdoor land uses, areas that can be associated with an increased risk of exposure to others that may be infected. We utilize this approach to capture community inflows/outflows of people as a result of the pandemic and changes in mobility behavior for those that remain.Our focus is on New York City (NYC), the first epicenter of the pandemic in the United States, where a statewide stay-at-home order (NY on PAUSE) was introduced on March 22, 2020. By June 30, 2020, NYC had more than 212,000 confirmed cases of COVID-19, accounting for 8% of the nationwide total, resulting in at least 18,492 confirmed deaths and 4,604 probable deaths (
35). Our methodology proceeds in three steps. First, we develop a generalizable method for assessing neighborhood activity levels using smartphone geolocation data over a 3-mo period (February, March, and April) covering more than 12 million unique devices within the Greater New York area, together with land-use classifications at 1-m grid resolution. Second, we measure and analyze disparities in community social distancing by estimating variations in neighborhood activity and associated patterns in community characteristics before and after the stay-at-home order. Finally, we evaluate the effect of exposure density on COVID-19 infection rates associated with localized demographic, socioeconomic, and built-environment characteristics in order to identify disparities in health outcomes related to mobility behavior. Our findings provide insight into the timely evaluation of the effectiveness of social distancing at the scale of individual neighborhoods and support a more equitable allocation of resources to vulnerable and at-risk communities.
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