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Urban agglomerations are fundamental regional units of development and attract large-scale migrant population. Previous studies have only focused on migrant population distribution in major urban agglomerations. Therefore, this study analysed the spatiotemporal characteristics of migrant population distribution in China during 2000–2010 at city level from the perspective of urban agglomerations. The results indicate that urban agglomerations were accumulation areas of migrant population. Numerous people have migrated into 19 urban agglomerations, which has enlarged regional differences in migrant population distribution. The interprovincial migrant population dominated within urban agglomerations, whereas the intraprovincial migrant population dominated outside urban agglomerations. In the future, intraprovincial migration will become the dominant migration mode. The evolution of migrant population distribution pattern in urban agglomerations agrees with classic theories of unbalanced regional development. The determinants of migration in different regions were compared. Results revealed that economic and government driving forces jointly influenced migration; however, economic forces exceeded government forces. Economic forces were more influential within urban agglomerations, whereas government forces played more important roles outside urban agglomerations. Increased income and job opportunities were the core attractions of urban agglomerations. Moreover, with an increase in the urban agglomeration development level, the influence of economic forces increased, whereas that of government forces decreased. The findings provide a deeper understanding of migrant population distribution in China, which will benefit population management across various regions. [ABSTRACT FROM AUTHOR]

The middle class in metropolitan Chinese cities has become an important social group. With the rapid development of urbanization and constant advancement of suburbanization, the middle class has increasingly come to influence city traffic. Research into middle-class commuting activities thus has practical significance for improving traffic congestion and reducing the commuting burden in metropolitan cities. Based on a dataset formed by 816 completed surveys, this paper analyzes the commuting mode, time and distance of middle-class residents in Guangzhou City using the descriptive statistical method. The results indicate that private cars are the main commuting mode, followed by public transport. Meanwhile, middle-class residents mainly undertake medium-short time and medium- short distance commuting. The study subsequently uses multilevel logistic regression and multiple linear regression models to analyze the factors that influence commuting mode choice, time and distance. The gender, age, number of family cars, housing source and jobs-housing balance are the most important factors influencing commuting mode choice; housing, population density, jobs-housing balance and commuting mode significantly affect commuting time; and transport accessibility, jobs-housing balance and commuting mode are the notable factors affecting commuting distance. Finally, this paper analyzes what is affecting the commuting activities of middle-class residents and determines the differences in commuting activity characteristics and influence factors between middle-class and ordinary residents. Policy suggestions to improve urban planning and urban management are also proposed. [ABSTRACT FROM AUTHOR]

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran's I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies. [ABSTRACT FROM AUTHOR]

Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction. [ABSTRACT FROM AUTHOR]

In addition to socioeconomic factors, urban planning and transportation organization are beginning to play an increasingly important role in the reduction of CO 2 emissions. However, little attention has been paid to the ways in which this emerging role can be framed. Therefore, this study aims to examine the combined impacts of socioeconomic and spatial planning factors on CO 2 emissions in cities that have experienced rapid urbanization, using an econometric model and a comprehensive panel dataset incorporating socioeconomic, urban form, and transportation factors for four Chinese megacities—Beijing, Tianjin, Shanghai and Guangzhou, —in the period 1990–2010. Making use of remote sensing land-use data, the digitization of transportation maps, and a set of socioeconomic data, we developed an extended STRIPAT model in order to empirically estimate the impacts of the selected variables on CO 2 emission levels in these cities. The results indicate that the socioeconomic factors of economic growth, urbanization, and industrialization will lead to increased CO 2 emissions, while the service level and technology level can contribute to the reduction of CO 2 emissions. The results also suggest that the expansion of urban land use and increases in urban population density should be controlled through urban planning measures in order to reduce CO 2 emissions. In addition, pursuing compact urban development patterns would also help to reduce CO 2 emissions. Transportation factors including urban road density and the traffic coupling factor were both found to have exerted significant negative effects on CO 2 emission levels, indicating that increases in the coupling degree between urban spatial structure and traffic organization can also contribute to reducing such emissions. Our results cast a new light on the importance of practices of urban planning and spatial optimization measures in achieving CO 2 emission reductions. The findings obtained in this study are seen as providing important decision support in building low-carbon cities in China. [ABSTRACT FROM AUTHOR]

Planning policies are important drivers of spatial evolution in Chinese metropolitan areas; however, research on the impact of planning policy implementation on the spatial evolution of metropolitan areas is lacking. This study identifies the spatial evolution process of the Guangzhou metropolitan area, evaluates the results of planning policy implementation, and analyzes the possible spatial interactions between them. The results show that the spatial evolution of the Guangzhou metropolitan area is consistent with the point-axis development theory, and has formed a polycentric network-type spatial structure evolution. Moreover, there is an obvious spatial correlation between the implementation planning policies and the spatial evolution of a metropolitan area; that is, the planning policies have driven the spatial structure of the metropolitan area to develop gradually in different periods. This research enriches the theoretical study of the spatial evolution of metropolitan areas, and provides a point of reference for the development and planning of other metropolitan areas. • We propose a new approach to evaluate the implementation results of planning policy • Planning policy plays an important role in promoting the early formation and development of Guangzhou metropolitan area • Our study enriches the theory of spatial evolution of Chinese metropolitan areas based on the study of Guangzhou metropolitan area [ABSTRACT FROM AUTHOR]