Structure of 311 service requests as a signature of urban location

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.尽管城市系统具有显著的空间异质性，诸多城市规划、经济及政治决策的制定，均高度依赖对本地邻里街区发展情境的深度认知。本研究表明，311服务请求（311 Service Requests）的结构化数据，为构建本地城市情境的独特特征标识提供了可行路径，可作为面向城市利益相关方的低成本决策支持工具。以纽约市、波士顿与芝加哥为研究案例，我们展示了如何利用各街区按类型记录并分类的311服务请求数据，基于差异化的社会经济特征，对城市全域的不同区位开展具有实际价值的分类。此外，基于311服务请求的城市街区分类结果，可提供充足的信息以建模各类社会经济特征。最后，我们证实上述特征能够用于预测本地相对房地产价格的未来走势。本研究进一步证实，311服务请求数据可用于监测并预判城市街区的社会经济运行状况，助力城市利益相关方量化其干预举措的实际影响。