100 word bio

A practicing GIScientist, Chaogui Kang teaches in School of Remote Sensing and Information Engineering at Wuhan University and holds a joint position as research associate in Department of Land Surveying and Geo-Informatics at The Hong Kong Polytechnic Unversity. He graduated in GIScience from Nanjing University (B.A., 2009) and from Peking University (Ph.D., 2015). During September 1, 2012 and August 31, 2013, he was visiting student and research affiliate in SENSEable City Lab at Massachusetts Institute of Technology. Trained in Geography, Data Science and Complex Network for 10+ years, Chaogui has authored over 20 publications in leading international journals and conferences. His primary research interest lies in Urban Informatics, Spatial-Social Networks and Human Mobility with the assistance of pervasive urban sensing techniques.



I am launching the research initiative Urban CoLab, where “co” stands for “co”llective intelligence, “co”mputing, “co”mplexity and “co”llaborative. In the lab, we leverage a couple of “urban sensing” data sets to explore human mobility patterns and urban spatio-temporal structures. Our mission is to develop “data-driven, human-centric” methodologies for tackling urban problems from a geospatial perspective.

Recent news


[conference website] [geospatial week 2017]


Our paper on An Analysis of Entropy of Human Mobility from Mobile Phone Data has been published on Geomatics and Information Science of Wuhan University.


Quantifying human mobility patterns is intensively investigated by scientists from computational social science, statistical physics and complex science. In last decade, mobile phone data provide an unprecedented tool for capturing individuals’ travel activities in space and time. However, its nature of sparsity in time and imprecision in space imposes significant bias upon the derived mobility patterns. This research proposes two efficient techniques to cope with this issue. First, we implement an activity-location and travel-OD identification method to reconstruct reliable trajectories from call detailed records for mobile users. Second, we introduce the approximate entropy, which is superior to conditional entropy, for quantifying the regularity of individuals’ consecutively visited locations. With a case study in Harbin, the proposed approaches enable us to uncover meaningful patterns of urban mobility in terms of frequently and consecutively visited locations.

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