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.

Graduate Students interested in Urban Big Data Analytics are always welcome to join the team !

Please do not hesitate to contact us at cgkang@whu.edu.cn for more information.

Recent news

Our paper on Quantifying tourist behavior patterns by travel motifs and geo-tagged photos from Flickr has been published on ISPRS International Journal of Geo-Information.


With millions of people traveling to unfamiliar cities to spend holidays, travel recommendation becomes necessary to assist tourists in planning their trips more efficiently. Serving as a prerequisite to travel recommender systems, understanding tourist behavior patterns is therefore of great importance. Recently, geo-tagged photos on social media platform like Flickr provide a rich data source that captures location histories of tourists and reflects their preferences. This article utilizes geo-tagged photos from Flickr to extract trajectories of tourists, and then extends the concept of motifs from topological spaces, to temporal spaces, and to semantic spaces, for detecting tourist mobility patterns. By representing trajectories in terms of three distinct types of travel motif and further using them to measure user similarity, typical tourist travel behavior patterns associated with distinct sightseeing tastes/preferences are identified and analyzed for tourism recommendation. Our empirical results confirm that the proposed analytical framework is effective to uncover meaningful tourist behavior patterns.

[read full article] [download pdf]



[conference website] [geospatial week 2017]