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 loc...

Full description

Bibliographic Details
Main Authors: Lingjing Wang, Cheng Qian, Philipp Kats, Constantine Kontokosta, Stanislav Sobolevsky
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5645100?pdf=render
id doaj-4f5657747a474cb084a934fa0b47fdbe
record_format Article
spelling doaj-4f5657747a474cb084a934fa0b47fdbe2020-11-25T01:45:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-011210e018631410.1371/journal.pone.0186314Structure of 311 service requests as a signature of urban location.Lingjing WangCheng QianPhilipp KatsConstantine KontokostaStanislav SobolevskyWhile 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.http://europepmc.org/articles/PMC5645100?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Lingjing Wang
Cheng Qian
Philipp Kats
Constantine Kontokosta
Stanislav Sobolevsky
spellingShingle Lingjing Wang
Cheng Qian
Philipp Kats
Constantine Kontokosta
Stanislav Sobolevsky
Structure of 311 service requests as a signature of urban location.
PLoS ONE
author_facet Lingjing Wang
Cheng Qian
Philipp Kats
Constantine Kontokosta
Stanislav Sobolevsky
author_sort Lingjing Wang
title Structure of 311 service requests as a signature of urban location.
title_short Structure of 311 service requests as a signature of urban location.
title_full Structure of 311 service requests as a signature of urban location.
title_fullStr Structure of 311 service requests as a signature of urban location.
title_full_unstemmed Structure of 311 service requests as a signature of urban location.
title_sort structure of 311 service requests as a signature of urban location.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2017-01-01
description 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.
url http://europepmc.org/articles/PMC5645100?pdf=render
work_keys_str_mv AT lingjingwang structureof311servicerequestsasasignatureofurbanlocation
AT chengqian structureof311servicerequestsasasignatureofurbanlocation
AT philippkats structureof311servicerequestsasasignatureofurbanlocation
AT constantinekontokosta structureof311servicerequestsasasignatureofurbanlocation
AT stanislavsobolevsky structureof311servicerequestsasasignatureofurbanlocation
_version_ 1725023127390388224