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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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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. |
format | Online Article Text |
id | pubmed-6488063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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