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.
Street space quality affects human behavior, public health and urban culture. The article introduces an evaluation method to improve street space quality. With streetscape images, the paper analyzes objective elements and subjective assessment, and evaluates spatial quality of streets in Beijing and Shanghai.
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.