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Most existing literature focuses on the exterior temporal rhythm of humanmovement to infer the functional regions in a city, but they neglects theunderlying interdependence between the functional regions and human activitieswhich uncovers more detailed characteristics of regions. In this research, weproposed a novel model based on the low rank approximation (LRA) to detect thefunctional regions using the data from about 15 million check-in records duringa yearlong period in Shanghai, China. We find a series of latent structures,called urban spatial-temporal activity structure (USTAS). While interpretingthese structures, a series of outstanding underlying associations between thespatial and temporal activity patterns can be found. Moreover, we can not onlyreproduce the observed data with a lower dimensional representative but alsosimultaneously project both the spatial and temporal activity patterns in thesame coordinate system. By utilizing the K-means clustering algorithm, fivesignificant types of clusters which are directly annotated with a correspondingcombination of temporal activities can be obtained. This provides a clearpicture of how the groups of regions are associated with different activitiesat different time of day. Besides the commercial and transportation dominantarea, we also detect two kinds of residential areas, the developed residentialareas and the developing residential areas. We further verify the spatialdistribution of these clusters in the view of urban form analysis. The resultsshows a high consistency with the government planning from the same periods,indicating our model is applicable for inferring the functional regions viasocial media check-in data, and can benefit a wide range of fields, such asurban planning, public services and location-based recommender systems andother purposes.

Urban spatial-temporal activity structures: a New Approach to Inferring the Intra-urban Functional Regions via Social Media Check-In Data