Structure of 311 service requests as a signature of urban location.

Lingjing Wang,Stanislav Sobolevsky,Philipp Kats,Cheng Qian,Constantine Kontokosta,Yanguang Chen

doi:10.1371/journal.pone.0186314

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.

Highlights

Cities can be seen as a complex system composed of multiple layers of activity and interactions across various urban domains; discovering a parsimonious description of urban function is quite difficult [1,2,3,4]
As we see from the results above, 311 service requests can be used in order to characterize the local context of the urban neighborhood and demonstrate moderate to strong correlations with wealth, education level, unemployment, racial structure of its population, as well as with the housing prices in the area
We provide an approach to quantify local signatures of urban function via 311 service request data collected in various cities across the US

Summary

Introduction

Cities can be seen as a complex system composed of multiple layers of activity and interactions across various urban domains; discovering a parsimonious description of urban function is quite difficult [1,2,3,4]. Urban planners, policy makers and other types of urban stakeholders, including businesses and investors, could benefit from an intuitive proxy of neighborhood conditions across the city and over time [5,6,7]. At the same time, such simple indicators could provide valuable information to support urban decision-making, and to accelerate the scalability of successful approaches and practices across different neighborhood and cities, as urban scaling patterns have become an increasing topic of interest [8,9,10,11,12].

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Discussion

Conclusion