Wang Lingjing, Qian Cheng, Kats Philipp, Kontokosta Constantine, Sobolevsky Stanislav
Center for Urban Science and Progress, New York University, Brooklyn, New York, United States of America.
Tandon School of Engineering, New York University, Brooklyn, New York, United States of America.
PLoS One. 2017 Oct 17;12(10):e0186314. doi: 10.1371/journal.pone.0186314. eCollection 2017.
While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.
虽然城市系统表现出高度的空间异质性，但许多城市规划、经济和政治决策严重依赖于对当地社区环境的深入了解。我们表明，311服务请求的结构提供了一种构建当地城市环境独特特征的可能方法，从而能够作为城市利益相关者的低成本决策支持工具。以纽约市、波士顿和芝加哥为例，我们展示了每个社区按类型记录和分类的311服务请求如何能够用于根据独特的社会经济概况对全市的地点进行有意义的分类。此外，基于311的城市社区分类能够提供足够的信息来模拟各种社会经济特征。最后，我们表明这些特征能够预测比较当地房地产价格的未来趋势。我们证明311服务请求数据可用于监测和预测城市社区的社会经济表现，使城市利益相关者能够量化其干预措施的影响。
