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.
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 email@example.com for more information.
Urbanization’s rapid progress presents an urgent challenge for developing a predictive, quantitative theory to support the comprehensive understanding of the urban built environment. Among, the intertwined relationship between urban functionality and activity diversity has been widely recognized and quantified in terms of the richness of points of interest (POIs). In this research, we provide a novel methodology for revealing the co-occurrences of POIs within the city and the mechanisms of the interplay between them. We build and study the network of relatedness between POIs, finding that more common POIs are located in a densely connected core whereas more rare and unique POIs occupy a less-connected periphery. We simulate the diffusion process of the network in different subdistrict, finding that common POIs act more on the speed of diffusion, unique POIs act more on the scope of diffusion. This research may help inform stakeholders the proper way of promoting the vitality of the local urban built environment.
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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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