Structure of 311 Service Requests as a Signature of Urban Location

TL;DR

This paper demonstrates that the structure of 311 Service Requests can serve as a low-cost, data-driven method to classify urban neighborhoods, predict socioeconomic trends, and inform urban planning decisions.

Contribution

It introduces a novel approach using 311 Service Requests to classify neighborhoods and predict socioeconomic outcomes, enhancing urban decision support tools.

Findings

311 request patterns classify neighborhoods effectively

311 data can predict socioeconomic features

Neighborhood classifications forecast real estate trends

Abstract

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.