It is expected that by 2050, more than 2.5 billion people would reside in cities. While the urbanization has modernized many peoples’ lives, it causes big challenges such as traffic congestion, air pollution, energy consumption, etc. As part of the effort for solving these problems, people have been collecting and analyzing data that is being generated in the urban space, e.g., traffic data, mobility data, POI data, etc., for finding insights into the problems and/or serving citizens better decision making. Since the majority of the data involves the spatial and/or temporal dimensions, techniques for spatial and spatio-temporal data analytics are playing critical roles. In this talk, we will overview this data-driven process, introduce some of its interesting applications, and also present some of our recent work on spatial and spatio-temporal data analytics, including dynamic spatial matching, co-location pattern mining, traffic anomaly detection, and interesting region discovery, etc.
LONG Cheng is currently an Assistant Professor at the School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU). From 2016 to 2018, he worked as a lecturer (Asst Professor) at Queen’s University Belfast, UK. He got the PhD degree from the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology (HKUST) in 2015. His research interests are broadly in data management and data mining with his vision to achieve scalable spatial computing, to make sense of urban related data for smarter cities, and to manage and analyze emerging big data such as IoT data for richer knowledge. His research has been recognized with one “Best Research Award” provided by ACM-Hong Kong, one “Fulbright-RGC Research Award” provided by Research Grant Council (Hong Kong), two “PG Paper Contest Awards” provided by IEEE-HK, and one “Overseas Research Award” provided by HKUST. He has served as a Program Committee member/referee for several top data management and data mining conferences/journals (TODS, VLDBJ, TKDE, ICDM, CIKM, etc.). He is member of ACM and IEEE.