论文标题
部分可观测时空混沌系统的无模型预测
Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing
论文作者
论文摘要
现代城市的治理在很大程度上依赖众包来识别诸如树木和电力线等问题。一个主要问题是，居民不会以相同的速度报告问题，而异质的报告延误直接转化为下游差距，以解决如何迅速解决。在这里，我们开发了一种识别报告延迟的方法，而无需使用外部地面真实数据。我们的见解是，可以利用有关同一事件的重复报告的比率来消除歧义，以免事件发生在事件发生率后是否发生了事件。我们将我们的方法应用于纽约市的100,000多个居民报告，并在芝加哥的900,000多个报告中，发现在报告速度的速度方面存在很大的空间和社会经济差异。我们使用外部数据进一步验证了我们的方法，并证明了估计报告延迟如何导致实用的见解和干预措施，以实现更公平，更有效的政府服务。
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.
