311报告中的公平性：理解投诉倾向中的社会空间差异
Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain
摘要：
美国各地的城市正在实施信息通信技术，以改善政府服务。电子政务的创新之一是创建311系统，提供一个集中平台，公民可以通过热线、移动或基于网络的应用程序请求服务、报告非紧急问题并获取有关城市的信息。纽约市311服务请求系统是市民和市政府之间最重要的联系之一，每年有超过800万个请求。这些系统正在生成大量的数据，如果得到适当的管理、清理和挖掘，可以对城市的实时状况产生重要的见解。这些数据越来越多地被用于开发城市内市民关注和问题状况的预测模型。然而，根据这些数据训练的预测模型可能会在提出请求的倾向方面存在偏差，这种偏差可能会因地区的社会经济和人口特征、可能影响公民与政府互动意愿的文化差异以及访问权限的差异而有所不同。互联网连接。使用超过20，000，000个311请求-连同来自纽约市建筑部和纽约市住房保护和发展部的建筑违规数据;来自纽约市城市规划部的财产数据;以及来自美国人口普查美国社区调查的人口和社会经济数据-我们开发了一个两步方法来评估投诉倾向：（1）我们使用梯度提升回归模型预测给定建筑物的供暖和热水违规的可能性，以及（2）然后我们将建筑物的实际投诉量与预测的违规行为进行比较，以量化整个城市的差异。
英文摘要：
Cities across the United States are implementing information communication technologies in an effort to improve government services. One such innovation in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile, or web-based applications. The NYC 311 service request system represents one of the most significant links between citizens and city government, accounting for more than 8,000,000 requests annually. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to make a request that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens' willingness to interact with their government, and differential access to Internet connectivity. Using more than 20,000,000 311 requests - together with building violation data from the NYC Department of Buildings and the NYC Department of Housing Preservation and Development; property data from NYC Department of City Planning; and demographic and socioeconomic data from the U.S. Census American Community Survey - we develop a two-step methodology to evaluate the propensity to complain: (1) we predict, using a gradient boosting regression model, the likelihood of heating and hot water violations for a given building, and (2) we then compare the actual complaint volume for buildings with predicted violations to quantify discrepancies across the City.
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