Towards Human Mobility Detection Scheme for LocationBased Social Network Locationbased Social networks (LBSN) are a promising source for human mobility mining that attracted a lot of attention in the research community. The aim of this research is to discover the patterns and mobility of LBSN such as Twitter by analyzing the spatiotemporal features of userxe2x80x99s tweets. Twitter Streaming Application Program Interface (API) was used to capture Geotagged tweets. The analysis shows that geotagged tweets can obtain valuable information on human mobility, such as the relation between movement flow and the time of the day. It provides evidence that Twitter data can be useful for tracking human movement and understand human behavior pattern.

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.