Mar 21,2025

“Estimating Reporting Bias in 311 Compliant Data”

to be Published in Annals of Applied Statistics

“Estimating Reporting Bias in 311 Compliant Data,” 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) has been accepted for publication in Annals of Applied Statistics. The research was supported by two NSF grants (here and here):

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.

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