Discovering Functional Regions in Modern Cities by Using User Checkin Records and POIs The rapid development of the modern city necessitates the division of different functional regions, such as educational regions and business regions. In this paper, we propose an LDA based topic model to discover regions with different functions in a city using user checkin records and PointofInterests (POIs). We infer the functions of each region using the proposed model, which regards a region as a document, a function as a topic, categories of POIs and checkin frequency as words. As a result, a region is represented by a distribution of functions, and a function is represented by the distribution of POI categories. We further identify the intensity to illustrate that our method considers both the spatial distance and checkin frequency. We evaluate our method by using a large scale dataset, consisting of two user checkin records that occurred in Los Angeles and Lower Manhattan. The experiment results justify the advantages of our proposed method over the baseline method solely using POIs or checkin records.

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.