Spatio-Temporal Clustering Approach for Detecting Functional Regions in Cities

• Main Authors: Assem, Haytham, Lei Xu, Buda, Teodora Sandra, O'Sullivan, Declan
• Format: Conference Proceeding
• Language: English
• Subjects: Business; Clustering algorithms; Location-based social networks; Object recognition; Planning; Smart Cities; Twitter; Urban areas; Urban Computing; Urban dynamics
• ISSN: 2375-0197
• DOI: 10.1109/ICTAI.2016.0063

The development of a city gradually forms different functional regions, such as residential districts and shopping areas. Discovering these functional regions in cities can enable new types of valuable applications that can benefit different end users: Urban planners can better identify the proximity of existing functional regions and hence, can contribute a better future planning for the cities. Tourists can differentiate scenic areas from other business and residential areas which will help in reducing effort for trip planning. Moreover, local people can better understand each part of their cities by finding areas with particular functionality. With the rise of Location-Based Social Networks (LBSNs) which attract lots of new users everyday with the potential of bridging the gap between the physical world and digital online social network services, we show in this paper that identifying functional regions taking into account temporal variations of geographic user activity has become possible and is more sensible when identifying functional regions. In this work, we propose a novel approach to modeling functional areas taking into account temporal variation by means of place categories. Our proposed approach compares between three clustering algorithms (Hierarchical, K-means, and Spectral) on areas and users of Manhattan borough in New York City using a dataset from one of the most vibrant LBSN, Foursquare. We demonstrate the impact of different temporal variations splits on the quality of the clustering algorithms comparing it to the default approach with no temporal variation. We believe that this research can not only yield a deeper understanding of a complex city but also can offer finer personalized recommendations based on regions' functionality that changes over space and time.