100 word bio

A practicing GIScientist, Chaogui Kang teaches in School of Remote Sensing and Information Engineering at Wuhan University. He graduated in GIScience from Nanjing University (B.A., 2009) and Peking University (Ph.D., 2015). He was also visiting student and research affiliate in SENSEable City Lab at Massachusetts Institute of Technology (September 1, 2012 - August 31, 2013), and held a joint position as research associate in Department of Land Surveying and Geo-Informatics at The Hong Kong Polytechnic Unversity (July 21, 2017 - October 17, 2017). 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.

Spotlight

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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

Chaogui was appointed committee member of the Chinese Society for Urban Studies from 2018 to 2023.

Abstract

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Archives

Our paper on An analytical framework for understanding urban functionality from human activities has been accepted by 10th International Conference on Geographic Information Science.

Abstract

The intertwined relationship between urban functionality and human activity has been widely recognized and quantified with the assistance of big geospatial data. In specific, urban land uses as an important facet of urban structure can be identified from spatiotemporal patterns of aggregate human activities. In this article, we propose a space, time and activity cuboid based analytical framework for clustering urban spaces into different categories of urban functionality based on the variation of activity intensity (T-fiber), mixture (A-fiber) and interaction (I- and O-fiber). The ability of the proposed framework is empirically evaluated by three case studies.

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