Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain

Cities across the United States are implementing information communication technologies in an effort to improve government services. One such innovation in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile, or web-based applications. The NYC 311 service request system represents one of the most significant links between citizens and city government, accounting for more than 8,000,000 requests annually. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to make a request that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens’ willingness to interact with their government, and differential access to Internet connectivity. Using more than 20,000,000 311 requests together with building violation data from the NYC Department of Buildings and the NYC Department of Housing Preservation and Development; property data from NYC Department of City Planning; and demographic and socioeconomic data from the U.S. Census American Community Survey we develop a two-step methodology to evaluate the propensity to complain: (1) we predict, using a gradient boosting regression model, the likelihood of heating and hot water violations for a given building, and (2) we then compare the actual complaint volume for buildings with predicted violations to quantify discrepancies across the City. Our model predicting service request volumes over time will contribute to the efficiency of the 311 system by informing shortand long-term resource allocation strategy and improving the agency’s performance in responding to requests. For instance, the outcome of our longitudinal pattern analysis allows the city to predict building safety hazards early and take action, leading to anticipatory safety and inspection actions. Furthermore, findings will provide novel insight into equity and community engagement through 311, and provide the basis for acknowledging and accounting for Bloomberg Data for Good Exchange Conference. 24-Sep-2017, Chicago, IL, USA. bias in machine learning applications trained on 311 data.