Detecting Urban Polycentric Structure from POI Data
AbstractIt 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
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Deng, Y.; Liu, J.; Liu, Y.; Luo, A. Detecting Urban Polycentric Structure from POI Data. ISPRS Int. J. Geo-Inf. 2019, 8, 283.
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.Chicago/Turabian Style
Deng, Yue; Liu, Jiping; Liu, Yang; Luo, An. 2019. "Detecting Urban Polycentric Structure from POI Data." ISPRS Int. J. Geo-Inf. 8, no. 6: 283.
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