量化居民众包中的空间漏报差异
Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing
AI总结：
本文提出利用重复报告速率在无外部真值条件下估计居民事件报告延迟，并基于纽约和芝加哥大规模报告数据揭示显著的空间与社会经济差异。
摘要：
现代城市治理在很大程度上依赖众包来发现倒伏树木和电力线路等问题。一个主要担忧是，居民报告问题的速率并不相同，而异质性的报告延迟会直接转化为下游在事件处理速度方面的差异。本文提出一种无需使用外部真值数据即可识别报告延迟的方法。我们的洞见是，可以利用针对同一事件提交重复报告的速率，通过考察事件发生后的报告速率，来辨明事件是否已经发生。我们将该方法应用于纽约市超过100,000份居民报告以及芝加哥超过900,000份报告，发现事件被报告的速度存在显著的空间和社会经济差异。我们进一步利用外部数据验证了该方法，并展示了估计报告延迟如何带来切实可行的洞见和干预措施，从而实现更公平、更高效的政府服务。
英文摘要：
Modern city governance relies heavily on crowdsourcing to identify problems such as downed trees and power lines. A major concern is that residents do not report problems at the same rates, with heterogeneous reporting delays directly translating to downstream disparities in how quickly incidents can be addressed. Here we develop a method to identify reporting delays without using external ground-truth data. Our insight is that the rates at which duplicate reports are made about the same incident can be leveraged to disambiguate whether an incident has occurred by investigating its reporting rate once it has occurred. We apply our method to over 100,000 resident reports made in New York City and to over 900,000 reports made in Chicago, finding that there are substantial spatial and socioeconomic disparities in how quickly incidents are reported. We further validate our methods using external data and demonstrate how estimating reporting delays leads to practical insights and interventions for a more equitable, efficient government service.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
