ESTIMATING REPORTING BIAS IN 311 COMPLAINT DATA
成果类型：
Article
署名作者：
Boxer, Kate S.; Hong, Boyeong; Kontokosta, Constantine E.; Neill, Daniel B.
署名单位：
New York University; New York University
刊物名称：
ANNALS OF APPLIED STATISTICS
ISSN/ISSBN：
1932-6157
DOI：
10.1214/24-AOAS2003
发表日期：
2025
页码：
1691-1713
关键词：
摘要：
Systems such as 311 enable residents of a community to report on their environments and to request nonemergency 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 underreporting of heating and hot water problems to New York City's 311 system and developing methods to estimate under-reporting. First, we estimate nonreporting by fitting a latent variable model, which estimates both the probability of an underlying heating problem conditional on building characteristics, and the probability of reporting a problem conditional on population characteristics. Second, we analyze less-than-expected reporting: buildings with fewer 311 calls than expected, as compared to similarly-sized buildings with similar estimated problem durations. Together, these analyses determine neighborhoods and neighborhood-level socioeconomic characteristics that are predictive of underreporting of heating and hot water problems. Our approaches can aid government agencies wishing to use resident-generated data to assist in constructing fair public policies.
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