NYU News Profiles Civic Analytics’

In “NYU Researchers Devise Tool to Estimate ‘311’ Underreporting of Heat and Hot Water Shortages,” NYU News profiled “Estimating Reporting Bias in 311 Complaint Data,” a study published in Annals of Applied Statistics by PhD Candidate Kate Boxer (NYU Courant Institute of Mathematical Sciences); Civic Analytics Fellow Boyeong Hong; Director of Civic Analytics, Constantine Kontokosta; and Daniel Neill (NYU Center for Urban Science + Progress):

A team of New York University researchers has developed an automated modeling tool to help the New York City government estimate 311 under-reporting by building, neighborhood, and subpopulation. ...the researchers describe a method that, using machine learning, can estimate the potential under-reporting of heat and hot water problems. If adopted, this tool would help the city’s Department of Housing Preservation and Development (HPD) identify which buildings or locales may be placing a lower-than-expected number of 311 calls. The agency could take steps to better ensure that heat and hot water issues are not going unaddressed.

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