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

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.

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

“…Because check-ins are executed by a moderately small group of Weibo users, they have been used in other studies as a good pointer of Weibo users' day-to-day activity [ 71 ] However, our findings reveal that the number of locations has not been increased significantly in Shanghai. When compared to prior studies, it can be noted that all of the studies show that female consumers are much more active than males during the various activities in Shanghai [ 69 – 74 , 82 , 83 ].…”

Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

“…Unlike the widespread usage of Facebook, Twitter, and other LBSNs throughout the world, the majority of these LBSNs are outlawed or limited in China. As a result, Chinese residents prefer to utilize national microblog such as Weibo (Sina Weibo) [4,5]. So, check-in data from Weibo may be acceptable for LBSNs statistical study in China.…”