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Cities across the United States are implementing information communicationtechnologies in an effort to improve government services. One such innovationin e-government is the creation of 311 systems, offering a centralized platformwhere citizens can request services, report non-emergency concerns, and obtaininformation about the city via hotline, mobile, or web-based applications. TheNYC 311 service request system represents one of the most significant linksbetween citizens and city government, accounting for more than 8,000,000requests annually. These systems are generating massive amounts of data that,when properly managed, cleaned, and mined, can yield significant insights intothe real-time condition of the city. Increasingly, these data are being used todevelop predictive models of citizen concerns and problem conditions within thecity. However, predictive models trained on these data can suffer from biasesin the propensity to make a request that can vary based on socio-economic anddemographic characteristics of an area, cultural differences that can affectcitizens' willingness to interact with their government, and differentialaccess to Internet connectivity. Using more than 20,000,000 311 requests -together with building violation data from the NYC Department of Buildings andthe NYC Department of Housing Preservation and Development; property data fromNYC Department of City Planning; and demographic and socioeconomic data fromthe U.S. Census American Community Survey - we develop a two-step methodologyto evaluate the propensity to complain: (1) we predict, using a gradientboosting regression model, the likelihood of heating and hot water violationsfor a given building, and (2) we then compare the actual complaint volume forbuildings with predicted violations to quantify discrepancies across the City.

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