Using nighttime light data to identify the structure of polycentric cities and evaluate urban centers.

Studying the structure of polycentric cities promotes a better understanding of the process of urban development and contributes to urban planning and management. However, existing studies cannot compare the level differences between urban centers and quantify the overall urban center development level within polycentric cities. Therefore, we combined nighttime light (NTL) data and the natural city (NC) to conduct this study from multiple perspectives. First, NCs were determined from Luojia-1A NTL data with high spatial resolution. Second, urban centers were selected from NCs. Third, urban center level (UCL) was proposed to compare the level differences of urban centers. Fourth, urban center development index (UCDI) was proposed to quantify the overall urban center development level within a polycentric city. A quantitative verification method was used to confirm that the detection accuracy of urban center identification is high. Furthermore, the applicability of the proposed method in different NTL datasets was verified by the identification of urban centers and the calculation of UCDIs. Unlike traditional methods, the shape and scope of the urban center identified using the proposed method are not restricted by administrative boundaries. Moreover, the level differences of urban centers and the overall urban center development level within a polycentric city, can be expressed by quantitative indicators, which helps in comparing the differences between urban centers.

Delimitating the Natural City with Points of Interests Based on Service Area and Maximum Entropy Method.

The natural city, which is essential to understand urban physical scale and identify urban sprawling in urban studies, represents the urban functional boundaries of the city defined by human activities rather than the administrative boundaries. Most studies tend to utilize physical environment data such as street networks and remote sensing data to delimitate the natural city, however, such data may not match the real distribution of human activity density in the new cities or even ghost cities in China. This paper suggests aggregating the natural city boundary from the service area polygons of points of interest based on Reilly's Law of Retail Gravitation and the maximum entropy method, since most points of interests provide services for surrounding communities, reflecting the vitality in a bottom-up way. The results indicate that the natural city defined by points of interests shows a high resolution and accuracy, providing a method to define the natural city with POIs.