Structure of 311 service requests as a signature of urban location

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.

311 service requests as indicators of neighborhood distress and opioid use disorder

“…Georeferencing for these issues involves automated location referencing in the smartphone app, and users reporting street addresses or closest intersection. For most cities including Columbus, the accurate street address or geographical coordinates of the reported issue are recorded into a database 36,37 . The City of Columbus Department of Neighborhoods maintains a retrospective geocoded dataset of 311 service requests that contains 21 different categories of non-emergency public issues.…”

“…We use 311 data as proxies for socioeconomic conditions of urban neighborhoods 37 , we compare these data with administrative data to census data to confirm that the types of 311 requests that are spatially associated with OOEs also associate with other indicators of socioeconomic distress. We use American Community Survey (ACS) 5-year estimates data for the years of 2010 and 2015.…”

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

“…While possibly influenced by the self-reported nature of demands and criticisms, these data provide a comparable metric of perceived local quality of life across space and time. These data, therefore, serve as a helpful tool for knowing the provision of critical urban services and circumstances in the neighborhood [20].…”

“…At the start of the algorithm, scout bees were placed randomly in the search space. On the onset the finesses of the sites which were visited by the scout bees were evaluated using the fitness function [20]. Next, bees that possess the highest fitness values were chosen as "selected bees" and sites seen by them were preferred for neighborhood search.…”

“…Wang et al developed a method for classifying urban locations based on the categorical and temporal structure of 311 Service Requests. They provided an urban classifier to predict the socioeconomic and demographic features of a neighborhood and estimate the economic performance and prosperity of defined spatial units [20]. Xu et al used a locally adaptive space-time kernel approach to model 311 requests.…”