Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on LocationBased Social Networkxe2x80x99s Data from Weibo Abstract: The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from LocationBased Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of locationbased social networkxe2x80x99s data by exploring the patterns in checkins over a period of six months. We acquired the geolocation information from one of the most famous Chinese microblogs called SinaWeibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residentsxe2x80x99 activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

LocationBased Social Networkxe2x80x99s Data Analysis and SpatioTemporal Modeling for the Mega City of Shanghai, China The aim of the current study is to analyze and extract the useful patterns from LocationBased Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific checkin venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of checkins, and venue classes, based on usersxe2x80x99 checkin behavior and the cityxe2x80x99s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geolocation information from one of the most famous Chinese microblogs called SinaWeibo (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 usersxe2x80x99 checkins 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 checkins 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 checkin density all over Shanghai city, are thoroughly demonstrated.