Polycentric China

Identifying and Evaluating Urban Centers for the Whole China Using Open Data

The urban center is the core component of urban structure. Its identification and evaluation have long been a concern of the urban planning discipline. However, the central city areas (urban centers) have never been well delineated for the China city system, leading few urban studies on urban centers due to data unavailability. To address this gap and based on reviewing existing identification methods of the urban center, this chapter proposes a novel approach for identifying urban centers using increasingly ubiquitous open data points of interest (POIs) and

evaluating the identified nationwide urban centers using various types of open data from four dimensions, respectively. These dimensions range from scale, morphology, function, to vitality aspects, thus providing opportunities for exploring the overall development characteristics of nationwide urban centers. We hope this chapter may shed light on future urban studies on urban centers of China.

Ma and Long 2017 Springer_UrbanCenters.p
Adobe Acrobat Document 1.6 MB

China Polycentric Cities Based on Baidu Heatmap

This paper redefines urban center based on the activities which are carried out through Internet, and identifies all urban centers of 658 cities utilizing Baidu heatmap. We take the new method of recognizing urban centers as a bottom-up pattern which will assist the traditional top-down method. Among 658 cities, there are 69 polycentric cities; and we focus on them to explore the general law of Chinese polycentric cities. All polycentric cities are classified into three categories according to the number of urban centers, which are primary polycentric city, growing polycentric city, and mature polycentric city. We further analyze areas, average distance and activity intensity of all polycentric cities on the basis of these three categories. According to our analysis, Chinese big cities perform significant polycentric city, while development of small cities (especially county-level city) are extremely lagging. Disparities among all polycentric cities in areas of centers are huge; Generally, they all tend to develop a hierarchical structure. As the polycentric cities keep developing from primary level to mature level, the communication distance will increase gradually, but the improvement of centers to city dynamic is also remarkable. At last, the regression analysis indicates that the number of employment and GDP per capita have significant correlation with the formation and development of urban centers. Accordingly, we provide three suggestions for Chinese cities regarding to the importance of developing center, the efficiency of centers’ network, and the new method of identifying centers. 

李娟等 2016 上海城市规划_中国多中心.pdf
Adobe Acrobat Document 3.9 MB

Live-work-play Centers of Chinese cities: Identification and Temporal Evolution with Emerging New Data

The live-work-play (LWP) center, as a more comprehensive profile of a city center, has attracted increasing attention in recent years. In this paper, we propose a straightforward framework to identify and evaluate LWP centers by using ubiquitously available points of interest (POIs), a proxy of urban function. The framework is then applied for 285 Chinese cities. The results show that 35 Chinese cities in 2014 are associated with polycentric urban structure and 23 in 2009. The temporal evolution of LWP centers of Chinese cities concluded on the basis of multi-year identification could be summarized as three types of evolution in view of LWP center number, morphology and location as follows. First, more polycentric cities emerge in 2014 in comparison with those in 2009. Second, the morphological change type can be further classified as “relative dispersion”, “relative concentration”, and “absolute concentration”. Third, the location change type can be classified as five types-displacement, division, fusion, emerging, and recession. In the last experiment, the regression results show that the larger population and greater road junction density significantly contribute to the LWP center formation.

Li et al 2018 CEUS_LWPcenters.pdf
Adobe Acrobat Document 1.8 MB