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

Our paper on Understanding the interplay between bus, metro and cab ridership dynamics in Shenzhen, China has been accepted by Transactions in GIS.

Abstract

The most common mass transit modes in metropolitan cities include buses, subways and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro and taxi ridership from Shenzhen, China to unveil the spatio-temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio-temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city’s metro network and land-use functionality. In general, different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way for exploring the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymaker and municipal manager in optimizing public transportation facility allocation and city-wide daily commuting distribution.

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Archives

The team attended The 14th Conference on Location Based Services at Zurich, Switzerland.

Abstract

The 14th International Conference on Location Based Services was successfully held by ETH Zurich and the ICA Commission on Location Based Services in Zurich, Switzerland on 15-17 January 2018.

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