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Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

by Naimat Ullah Khan 1,2,3,*, Wanggen Wan 1,2 and Shui Yu 3
1
School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
2
Institute of Smart City, Shanghai University, Shanghai 200444, China
3
School of Computer Science, University of Technology Sydney, Ultimo NSW 2007, Australia
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 76; https://doi.org/10.3390/ijgi9020076
Received: 15 December 2019 / Revised: 20 January 2020 / Accepted: 27 January 2020 / Published: 29 January 2020
(This article belongs to the Special Issue Geovisualization and Geo Visual Knowledge Discovery)
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. View Full-Text
Keywords: big data; LBSN; data mining; KDE; Weibo; GIS big data; LBSN; data mining; KDE; Weibo; GIS
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Khan, N.U.; Wan, W.; Yu, S. Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China. ISPRS Int. J. Geo-Inf. 2020, 9, 76.

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