  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.
