Abstract
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.
Highlights
The study of extracting valuable information and gaining useful insights from spatio-temporal data has become very important in recent years
The study was carried out to look at three different aspects of analysis: a temporal analysis to reveal the patterns based on time, a check-in venue classification to provide insight into Location-Based Social Network (LBSN) users in each category, and a spatial analysis, resulting in a clear observation of venues and check-ins through mapping
The findings demonstrated that people tend to use LBSNs more in the evening instead of the morning and work day
Summary
The study of extracting valuable information and gaining useful insights from spatio-temporal data has become very important in recent years. Online services encourage different users to share their activities and interests with their social friends, and generate enormous amounts of data, enabling researchers to understand users’ activities, patterns, and preferences more accurately. These online services provide and store the information of users by considering their real-time locations. The data collected through such online services are generally enriched with multimedia, text, geo-location, and metadata, which can be further used to conduct studies about various aspects of human behavior
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