Measuring sensitivity to social distancing behavior during the COVID-19 pandemic.

Social distancing remains an effective nonpharmaceutical behavioral interventions to limit the spread of COVID-19 and other airborne diseases, but monitoring and enforcement create nontrivial challenges. Several jurisdictions have turned to "311" resident complaint platforms to engage the public in reporting social distancing non-compliance, but differences in sensitivity to social distancing behaviors can lead to a mis-allocation of resources and increased health risks for vulnerable communities. Using hourly visit data to designated establishments and more than 71,000 social distancing complaints in New York City during the first wave of the pandemic, we develop a method, derived from the Weber-Fechner law, to quantify neighborhood sensitivity and assess how tolerance to social distancing infractions and complaint reporting behaviors vary with neighborhood characteristics.

Measuring inequality in community resilience to natural disasters using large-scale mobility data.

While conceptual definitions provide a foundation for the study of disasters and their impacts, the challenge for researchers and practitioners alike has been to develop objective and rigorous measures of resilience that are generalizable and scalable, taking into account spatiotemporal dynamics in the response and recovery of localized communities. In this paper, we analyze mobility patterns of more than 800,000 anonymized mobile devices in Houston, Texas, representing approximately 35% of the local population, in response to Hurricane Harvey in 2017. Using changes in mobility behavior before, during, and after the disaster, we empirically define community resilience capacity as a function of the magnitude of impact and time-to-recovery.

Exposure density and neighborhood disparities in COVID-19 infection risk.

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 ([Formula: see text]) 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.

The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data.

Urban trees play a key role in reducing greenhouse gas emissions, cleaning air, promoting physical activity, and improving mental health. However, it is still largely unknown how the density and species of urban street trees may impact local public health. This study demonstrates how open data mining and large-scale spatial data integration can contribute to deeper insights into the effects of urban forestry.

Energy disclosure, market behavior, and the building data ecosystem.

Energy disclosure laws represent one of the most promising public policy tools to accelerate market transformation around building energy efficiency. For this type of information to have an impact on market behavior, it must be collected, analyzed, and disseminated to support the decision-making processes of each end user and influence both the producers and consumers of building performance data. This paper explores the significance of energy disclosure requirements and outlines a framework for utilizing these new sources of transparent, publicly available information.