Equity in 311 Reporting

Understanding Sociospatial Differentials in the Propensity to Complain

+ Constantine Kontokosta


Constantine Kontokosta, Boyeong Hong, and Kristi Korsberg have have submitted a working paper titled "Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain" to the arXiv repository.


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.


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