Most existing literature focuses on the exterior temporal rhythm of human movement to infer the functional regions in a city, but they neglects the underlying interdependence between the functional regions and human activities which uncovers more detailed characteristics of regions. In this research, we proposed a novel model based on the low rank approximation (LRA) to detect the functional regions using the data from about 15 million check-in records during a yearlong period in Shanghai, China. We find a series of latent structures, called urban spatial-temporal activity structure (USTAS). While interpreting these structures, a series of outstanding underlying associations between the spatial and temporal activity patterns can be found. Moreover, we can not only reproduce the observed data with a lower dimensional representative but also simultaneously project both the spatial and temporal activity patterns in the same coordinate system. By utilizing the K-means clustering algorithm, five significant types of clusters which are directly annotated with a corresponding combination of temporal activities can be obtained. This provides a clear picture of how the groups of regions are associated with different activities at different time of day. Besides the commercial and transportation dominant area, we also detect two kinds of residential areas, the developed residential areas and the developing residential areas. We further verify the spatial distribution of these clusters in the view of urban form analysis. The results shows a high consistency with the government planning from the same periods, indicating our model is applicable for inferring the functional regions via social media check-in data, and can benefit a wide range of fields, such as urban planning, public services and location-based recommender systems and other purposes.
Global evidence for a consistent spatial footprint of intra-urban centers
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University（北京大学地球与空间科学学院遥感与地理信息系统研究所）；Ordos Research Institute of Energy, Peking University（北京大学鄂尔多斯能源研究院）
Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico
Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization
Latent patterns of urban mixing in mobility analysis across five global cities
Department of Geography, The University of Hong Kong（香港大学地理系）；Institute of Marine Sustainable Development, Liaoning Normal University（辽宁师范大学海洋可持续发展研究院）；Senseable City Lab, Massachusetts Institute of Technology（麻省理工学院感知城市实验室）；ABC Department, Politecnico di Milano（米兰理工大学ABC部门）；Network Science Institute and Department of Physics, Northeastern University（东北大学网络科学研究院和物理系）；Media Lab, Massachusetts Institute of Technology（麻省理工学院媒体实验室）
