Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data

Beibei Yu, Zhonghui Wang, Haowei Mu, Li Sun and Fengning Hu
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Beibei Yu: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
Zhonghui Wang: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
Haowei Mu: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
Li Sun: School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
Fengning Hu: Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

Sustainability, 2019, vol. 11, issue 23, 1-18

Abstract: Along with the rapid development of China’s economy as well as the continuing urbanization, the internal spatial and functional structures of cities within this country are also gradually changing and restructuring. The study of functional region identification of a city is of great significance to the city’s functional cognition, spatial planning, economic development, human livability, and so forth. Backed by the emerging urban Big Data, and taking the traffic community as the smallest research unit, a method is proposed to identify urban functional regions by combining floating car track data with point of interest (POI) data recorded on an electronic map. It provides a new perspective for the study of urban functional region identification. Firstly, the main functional regions of the city studied are identified through clustering analysis according to the passenger’s spatial-temporal travel characteristics derived from the floating car data. Secondly, the fine-grained identification of the functional region attributes of the traffic communities is achieved using the label information from POI data. Finally, the AND-OR operation is performed on the recognition results derived by the clustering algorithm and the Delphi method, to obtain the identification of urban functional regions. This approach is verified by applying it to the main urban zone within Chengdu’s Third Ring Road. The results show that: (1) There are fewer single functional regions and more mixed functional regions in the main urban zone of Chengdu, and the distribution of the functional regions are roughly concentric centering in the city center. (2) Using the traffic community as a research unit, combined with dynamic human activity trajectory data and static urban interest point data, complex urban functional regions can be effectively identified.

Keywords: functional regions; traffic community; floating car data; Delphi method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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