Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.

Social media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people w...

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Main Authors: Lin Li, Lei Yang, Haihong Zhu, Rongrong Dai
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2015-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC4545943?pdf=render
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spelling doaj-f347b5fdf42b4c6c9baf59f0571bb6122020-11-25T01:24:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01108e013528610.1371/journal.pone.0135286Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.Lin LiLei YangHaihong ZhuRongrong DaiSocial media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people within a city from check-in data. By dividing the data in a temporal sequence, this study examines the overall mobility in the case study city through the gradient/difference of population density with a series of time after computing the population density from the check-in data using an incorporated Thiessen polygon method. By classifying check-in data with their geo-tags into several groups according to travel purposes, and partitioning the data according to administrative district boundaries, various moving patterns for those travel purposes in those administrative districts are identified by scrutinizing a series of spatial geometries of a weighted standard deviational ellipse (WSDE). Through deep analyses of those data by the adopted approaches, the general pattern of mobility in the case city, such as people moving to the central urban area from the suburb from 4 am to 8 am, is ascertained, and different characteristics of movement in those districts are also depicted. Furthermore, it can tell that in which district less movement is likely for a certain purpose (e.g., for dinner or entertainment). This study has demonstrated the availability of the proposed methodology and check-in data for investigating intra-urban human mobility.http://europepmc.org/articles/PMC4545943?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Lin Li
Lei Yang
Haihong Zhu
Rongrong Dai
spellingShingle Lin Li
Lei Yang
Haihong Zhu
Rongrong Dai
Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
PLoS ONE
author_facet Lin Li
Lei Yang
Haihong Zhu
Rongrong Dai
author_sort Lin Li
title Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
title_short Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
title_full Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
title_fullStr Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
title_full_unstemmed Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data.
title_sort explorative analysis of wuhan intra-urban human mobility using social media check-in data.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2015-01-01
description Social media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people within a city from check-in data. By dividing the data in a temporal sequence, this study examines the overall mobility in the case study city through the gradient/difference of population density with a series of time after computing the population density from the check-in data using an incorporated Thiessen polygon method. By classifying check-in data with their geo-tags into several groups according to travel purposes, and partitioning the data according to administrative district boundaries, various moving patterns for those travel purposes in those administrative districts are identified by scrutinizing a series of spatial geometries of a weighted standard deviational ellipse (WSDE). Through deep analyses of those data by the adopted approaches, the general pattern of mobility in the case city, such as people moving to the central urban area from the suburb from 4 am to 8 am, is ascertained, and different characteristics of movement in those districts are also depicted. Furthermore, it can tell that in which district less movement is likely for a certain purpose (e.g., for dinner or entertainment). This study has demonstrated the availability of the proposed methodology and check-in data for investigating intra-urban human mobility.
url http://europepmc.org/articles/PMC4545943?pdf=render
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