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.
Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation. First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e., endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.
[read full article] [download pdf]
Weather conditions have a substantial impact on urban residents’ daily travel activities. They usually determine the travel demand within a specific spatial location by land use type, as well as the route selection strategy between a pair of travel origin and destination. This information is crucial for stakeholders including urban dwellers, city planners and transport managers to optimize urban mobility, facility allocation and transportation resilience. In this paper, we apply spatiotemporal statistics, multiple linear regression and clustering analysis on taxi data and weather records of Wuhan City, China to understand the spatiotemporal characteristics of residents’ travel demand and taxi drivers’ route selection under different weather conditions. As a result, the dominant weather condition factors influencing residents’ travel activities are revealed on space and time. First, taxi demand is more vulnerable to weather changes on weekdays than weekends. It is negatively proportional to the increasement of rainfall, temperature and wind speed. Second, at city scale taxi demand decreases along with raining on weekdays while the demand increases on weekends. In particular, the short-distance travels increase while medium- and long-distance travels decrease. Third, taxi demand is more vulnerable to weather changes within the urban area than the suburban area. On rainy days, medium-distance travels within the urban area decrease, whereas short-distance travels within the suburban area increase. Fourth, taxi demand on residential area increases, whereas the demand on commercial area decreases on rainy days. Last, taxi drivers are found to prefer the shortest path on sunny days and the fastest path on rainy days. Those research results can assist urban planners and municipal managers to enhance their understanding of urban residents’ mobility pattern and their spatiotemporal dynamics more deeply.
[read full article] [download pdf]