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Article

Detecting Urban Polycentric Structure from POI Data

by 1, 1,2,*, 3 and 2
1
School of Resource and Environmental Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100830, China
3
Beijing Institute of Applied Science and Technology, Beijing 100091, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 283; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060283
Received: 30 April 2019 / Revised: 3 June 2019 / Accepted: 15 June 2019 / Published: 17 June 2019
(This article belongs to the Special Issue Algorithms and Techniques in Urban Monitoring)
It is meaningful to analyze urban spatial structure by identifying urban subcenters, and many methods of doing so have been proposed in the published literature. Although these methods are widely applied, they exhibit obvious shortcomings that limit their further application. Therefore, it is of great value to propose a new urban subcenter identification method that can overcome these shortcomings. In this paper, we propose the density contour tree (DCT) method for detecting urban polycentric structures and their spatial distributions. Conceptually, this method is based on an analogy between urban spatial structure and terrain. The point-of-interest (POI) density is visualized as a continuous mathematical surface representing the urban terrain. Peaks represent the regions of the most frequent human activity, valleys represent regions with small population densities in the city, and slopes represent spatial changes in urban land-use intensity. Using this method, we have detected the urban “polycentric” structure of Beijing and determined the corresponding spatial relationships. In addition, several important properties of the urban centers have been identified. For example, Beijing has a typical urban polycentric structure with an urban center area accounting for 5.9% of the total urban area, and most of the urban centers in Beijing serve comprehensive functions. In general, the method and the results can serve as references for the later research on analyzing urban structure. View Full-Text
Keywords: POI data; urban polycentric structure; density contour tree POI data; urban polycentric structure; density contour tree
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MDPI and ACS Style

Deng, Y.; Liu, J.; Liu, Y.; Luo, A. Detecting Urban Polycentric Structure from POI Data. ISPRS Int. J. Geo-Inf. 2019, 8, 283. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060283

AMA Style

Deng Y, Liu J, Liu Y, Luo A. Detecting Urban Polycentric Structure from POI Data. ISPRS International Journal of Geo-Information. 2019; 8(6):283. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060283

Chicago/Turabian Style

Deng, Yue, Jiping Liu, Yang Liu, and An Luo. 2019. "Detecting Urban Polycentric Structure from POI Data" ISPRS International Journal of Geo-Information 8, no. 6: 283. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060283

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