A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

Khan, NU; Wan, W; Yu, S; Muzahid, AAM; Khan, S; Hou, L

A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

Khan, NU Wan, W Yu, S Muzahid, AAM Khan, S Hou, L

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: ISPRS International Journal of Geo-Information, 2020, 9, (12), pp. 733-733
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Khan, NU
dc.contributor.author	Wan, W
dc.contributor.author	Yu, S
dc.contributor.author	Muzahid, AAM
dc.contributor.author	Khan, S
dc.contributor.author	Hou, L
dc.date.accessioned	2021-03-31T14:08:02Z
dc.date.available	2021-03-31T14:08:02Z
dc.date.issued	2020-12-01
dc.identifier.citation	ISPRS International Journal of Geo-Information, 2020, 9, (12), pp. 733-733
dc.identifier.issn	2220-9964
dc.identifier.issn	2220-9964
dc.identifier.uri	http://hdl.handle.net/10453/147744
dc.description.abstract	<jats:p>The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	ISPRS International Journal of Geo-Information
dc.relation.isbasedon	10.3390/ijgi9120733
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.title	A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2021-03-31T14:07:29Z
pubs.issue	12
pubs.publication-status	Published online
pubs.volume	9
utslib.citation.issue	12

Abstract:

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.

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http://hdl.handle.net/10453/147744