Location‐based social network’s data analysis and spatio‐temporal modeling for the mega city of Shanghai, China

Khan, NU; Wan, W; Yu, S

Location‐based social network’s data analysis and spatio‐temporal modeling for the mega city of Shanghai, China

Khan, NU Wan, W Yu, S

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: ISPRS International Journal of Geo-Information, 2020, 9, (2)
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Khan, NU
dc.contributor.author	Wan, W
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2021-02-26T20:56:00Z
dc.date.available	2021-02-26T20:56:00Z
dc.date.issued	2020-01-01
dc.identifier.citation	ISPRS International Journal of Geo-Information, 2020, 9, (2)
dc.identifier.issn	2220-9964
dc.identifier.issn	2220-9964
dc.identifier.uri	http://hdl.handle.net/10453/146501
dc.description.abstract	© 2020 by the authors. The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	ISPRS International Journal of Geo-Information
dc.relation.isbasedon	10.3390/ijgi9020076
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	Location‐based social network’s data analysis and spatio‐temporal modeling for the mega city of Shanghai, China
dc.type	Journal Article
utslib.citation.volume	9
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-02-26T20:55:51Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	2

Abstract:

© 2020 by the authors. The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

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http://hdl.handle.net/10453/146501