Modern city governance relies heavily on crowdsourcing (or "co-production") to identify problems such as downed trees and power-lines. A major concern in these systems is that residents do not report problems at the same rates, leading to an inequitable allocation of government resources. However, measuring such under-reporting is a difficult statistical task, as, almost by definition, we do not observe incidents that are not reported. Thus, distinguishing between low reporting rates and low ground-truth incident rates is challenging. We develop a method to identify (heterogeneous) reporting rates, without using external (proxy) ground truth data. Our insight is that rates on $\textit{duplicate}$ reports about the same incident can be leveraged, to turn the question into a standard Poisson rate estimation task -- even though the full incident reporting interval is also unobserved. We apply our method to over 100,000 resident reports made to the New York City Department of Parks and Recreation, finding that there are substantial spatial and socio-economic disparities in reporting rates, even after controlling for incident characteristics.
翻译:现代城市治理严重依赖众包(或“共同生产”)来找出下层树木和电线等问题。这些系统的一个主要关切是,居民不以同样的速度报告问题,导致政府资源分配的不公平。然而,衡量这种报告不足是一项困难的统计任务,因为从定义上看,我们几乎没有看到没有报告的事件。因此,区分低报告率和低地面真实事件率是困难的。我们开发了一种方法,在不使用外部(代理)地面事实数据的情况下,确定(不同)报告率。我们的了解是,关于同一事件的$(textit{doprecy}$报告率可以被利用,将问题转化为标准的Poisson比率估算任务 -- -- 尽管完全的事件报告间隔也没有被观察到。我们用我们的方法向纽约市公园和娱乐部提交的10万多份居民报告,发现即使在对事件特征进行了控制之后,报告率在空间和社会经济上也存在巨大的差异。