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Modern city governance relies heavily on crowdsourcing to identify problemssuch as downed trees and power lines. A major concern is that residents do notreport problems at the same rates, with heterogeneous reporting delays directlytranslating to downstream disparities in how quickly incidents can beaddressed. Here we develop a method to identify reporting delays without usingexternal ground-truth data. Our insight is that the rates at which duplicatereports are made about the same incident can be leveraged to disambiguatewhether an incident has occurred by investigating its reporting rate once ithas occurred. We apply our method to over 100,000 resident reports made in NewYork City and to over 900,000 reports made in Chicago, finding that there aresubstantial spatial and socioeconomic disparities in how quickly incidents arereported. We further validate our methods using external data and demonstratehow estimating reporting delays leads to practical insights and interventionsfor a more equitable, efficient government service.

Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing