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Open AccessArticle

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

by Kun Qin 1,2,†, Yuanquan Xu 1, Chaogui Kang 1,3,*,†, Stanislav Sobolevsky 3 and Mei-Po Kwan 4,5,6
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
3
Center for Urban Science + Progress, New York University, Brooklyn, NY 11201, USA
4
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China
5
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China
6
Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 570; https://doi.org/10.3390/ijgi8120570
Received: 13 October 2019 / Revised: 15 November 2019 / Accepted: 2 December 2019 / Published: 11 December 2019
Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed. View Full-Text
Keywords: big geospatial data; urban crowd flow; spatio-temporal dynamics; morphological analysis big geospatial data; urban crowd flow; spatio-temporal dynamics; morphological analysis
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Qin, K.; Xu, Y.; Kang, C.; Sobolevsky, S.; Kwan, M.-P. Modeling Spatio-Temporal Evolution of Urban Crowd Flows. ISPRS Int. J. Geo-Inf. 2019, 8, 570.

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