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

Bibliographic Data

ID22033774
AuthorsNaimat Khan (0000-0001-5224-1381, Shanghai University, corresponding author), Wanggen Wan (0000-0002-5065-9650, Shanghai University), Shui Yu (0000-0003-3775-5960, University of Technology Sydney), A A M Muzahid (0000-0003-2001-1922, Shanghai University), Sajid Khan (0000-0001-5613-3687, Huazhong University of Science and Technology), Li Hou (0000-0001-7336-804X, Huangshan University)
Year2020
Volume9
Issue12
Pages733
Publication date2020-12-07
Open AccessYes
TypeARTICLE
VenueISPRS International Journal of Geo-Information (JOURNAL)
Journal identifiersISSN: 2220-9964 • E-ISSN: 2220-9964
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi9120733
OpenAlexW3110667453
LanguageEN
Citations received2
References cited47

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

Big data · Categorization · Data mining · Data science · Information retrieval · Kernel density estimation · Microblogging · Social media · Social network analysis · Statistics · World Wide Web · Computer Science · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis · Mathematics · Urban Transport and Accessibility · Artificial Intelligence

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