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Abstract 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. We find that sensitivity to non-compliance is lower in minority and low-income neighborhoods, as well as in lower density areas, resulting in fewer reported complaints than expected given measured levels of overcrowding. 

Systems such as “311” enable residents of a community to report on their environments and to request non-emergency municipal services. While such systems provide an important link between community and government, resident-generated data suffer from reporting bias, with some subpopulations reporting at lower rates than others. Our research focuses on defining the under-reporting of heating and hot water problems to New York City’s 311 system and developing methods to estimate under-reporting. First, we estimate non-reporting by fitting a latent variable model which estimates both the probability of an underlying heating problem conditional on building characteristics, and the probability of reporting a problem conditional on population characteristics. Second, we analyze “less-than-expected” reporting: buildings with fewer 311 calls than expected as compared to similarly-sized buildings with similar estimated problem durations. Together, these analyses determine neighborhoods and neighborhood-level socioeconomic characteristics that are predictive of under-reporting of heating and hot water problems. Our approaches can aid government agencies wishing to use resident-generated data to assist in constructing fair public policies.