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Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data

Data about human trajectories has been widely used to study urban regions that are attractive to researchers and are considered to be hotspots. It is difficult, however, to quantify the function of urban regions based on the varieties of human behavior. In this research, we developed a clustering me...

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Detalles Bibliográficos
Autores principales: Gao, Qingke, Fu, Jianhong, Yu, Yang, Tang, Xuehua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488063/
https://www.ncbi.nlm.nih.gov/pubmed/31034481
http://dx.doi.org/10.1371/journal.pone.0215656
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author Gao, Qingke
Fu, Jianhong
Yu, Yang
Tang, Xuehua
author_facet Gao, Qingke
Fu, Jianhong
Yu, Yang
Tang, Xuehua
author_sort Gao, Qingke
collection PubMed
description Data about human trajectories has been widely used to study urban regions that are attractive to researchers and are considered to be hotspots. It is difficult, however, to quantify the function of urban regions based on the varieties of human behavior. In this research, we developed a clustering method to help discover the specific functions that exist within urban regions. This method applies the Gaussian Mixture Model (GMM) to classify regions’ inflow and trip count characteristics. It regroups these urban regions using the Pearson Correlation Coefficient (PCC) clustering method based on those typical characteristics. Using a large amount of vehicle trajectory data (approximately 1,500,000 data points) in the Chinese city of Chengdu, we demonstrate that the method can discriminate between urban functional regions, by comparing the proportion of surface objects within each region. This research shows that vehicle trajectory data in different functional urban regions possesses different time-series curves, while similar types of functional regions can be identified by these curves. Compared with remote sensing images and other statistical methods which can provide only static results, our research can provide a timely and effective approach to determine an urban region’s functions.
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spelling pubmed-64880632019-05-17 Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data Gao, Qingke Fu, Jianhong Yu, Yang Tang, Xuehua PLoS One Research Article Data about human trajectories has been widely used to study urban regions that are attractive to researchers and are considered to be hotspots. It is difficult, however, to quantify the function of urban regions based on the varieties of human behavior. In this research, we developed a clustering method to help discover the specific functions that exist within urban regions. This method applies the Gaussian Mixture Model (GMM) to classify regions’ inflow and trip count characteristics. It regroups these urban regions using the Pearson Correlation Coefficient (PCC) clustering method based on those typical characteristics. Using a large amount of vehicle trajectory data (approximately 1,500,000 data points) in the Chinese city of Chengdu, we demonstrate that the method can discriminate between urban functional regions, by comparing the proportion of surface objects within each region. This research shows that vehicle trajectory data in different functional urban regions possesses different time-series curves, while similar types of functional regions can be identified by these curves. Compared with remote sensing images and other statistical methods which can provide only static results, our research can provide a timely and effective approach to determine an urban region’s functions. Public Library of Science 2019-04-29 /pmc/articles/PMC6488063/ /pubmed/31034481 http://dx.doi.org/10.1371/journal.pone.0215656 Text en © 2019 Gao et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gao, Qingke
Fu, Jianhong
Yu, Yang
Tang, Xuehua
Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title_full Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title_fullStr Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title_full_unstemmed Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title_short Identification of urban regions’ functions in Chengdu, China, based on vehicle trajectory data
title_sort identification of urban regions’ functions in chengdu, china, based on vehicle trajectory data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6488063/
https://www.ncbi.nlm.nih.gov/pubmed/31034481
http://dx.doi.org/10.1371/journal.pone.0215656
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