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Accurate measurement of urban sprawl is vital for urban planning and management. Urban planning-induced internal structure complexity affects the extent of urban sprawl. In addition, urban sprawl is closely linked to economic development. The study attempts to explore the impact of urban sprawl from an economic-dominated perspective. Thus a City-Ring road-County (CRC) scale framework based on top-down administrative divisions for urban sprawl measurement is proposed: 1) the single-index measurement based on economic activity is applied to calculate urban sprawl; 2) the spatiotemporal pattern of urban sprawl is investigated through a case study in 31 economy-dominated provincial capital cities across China from 2005 to 2015; 3) the impact of economy and land on urban sprawl is explored using correlation analysis. The results indicate that the degree of urban sprawl at the city scale shows an "inverted U-shaped" curve from 2005 to 2015, which represents that the phenomenon of urban sprawl was most severe in 2010. It finds that urban sprawl was more severe in the east and central regions relative to the provincial capitals in the western region, with the situation being most severe in the northeast region. Regions that have been transformed from suburban to urban built-up areas need to be given priority attention by the local government, including population movement, land layout, and fiscal policy, to meet the criteria of the urbanization process. Through correlation analysis, we also found that urban sprawl was influenced by the industry structure and the form of built-up area. The outcome of the study suggests that the data scale is sufficiently small in granularity to provide geographic boundaries for systematic analysis of urban sprawl in multiple administrative regions. Thus, the study helps provide a reference for differential planning policy formulation by governments at diverse economic levels. [ABSTRACT FROM AUTHOR]

Urban informal settlements (UISs) are densely populated and poorly developed residential areas in urban areas. The mapping of UISs using remote sensing is crucial for urban planning and management. However, the large-scale extraction of UISs is impeded by the labor-intensive task of collecting numerous training samples and the lack of automatic and effective city partition. To overcome these challenges, we proposed a large-scale extraction framework for UISs based on semantic segmentation of highresolution remote sensing images. Utilizing Deeplab V3 Plus as the foundational extraction model, the proposed framework introduces fast sample collection based on GLCM features. Besides, an automatic city partition approach combined with clustering and fine-tuning was proposed to enhance the performance on extracting a specific category of UISs. The results of the case study conducted in 36 major Chinese cities show that the proposed framework achieved good performance, with an overall F1 score of 85.76%. Furthermore, comparative assessments were performed to demonstrate the effectiveness of automatic city partition. The proposed framework offers a practical approach for the largescale extraction of UISs, which holds great significance for sustainable development, poverty estimation, infrastructure construction, and urban planning. [ABSTRACT FROM AUTHOR]

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban hierarchical spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. [ABSTRACT FROM AUTHOR]

Against the backdrop of urban sustainable development around the world, how to coordinate both economic growth and ecological benefits in urban space becomes an important problem. Therefore, this study simulated and predicted the spatiotemporal changes in urban economy and ecosystem service value (E.S.V.) equivalent ratio under the current policies by 2030, and analysed how adjusting planning policies influences economy and ecology. This process was based on the future land use simulation (F.L.U.S.) model of coupled neural network, and on methods assessing the spatial changes in ecosystem services and land economy. This study aims to analyse urban land economy and E.S.V., and assess how China's land spatial planning guides and promotes high-quality urban economic development. Results show that artificial intelligence (A.I.) simulation can forecast the results of spatial planning policies of national lands, to make policy-making more forward-looking. The guidance of planning policies on urban expansion accelerates the increase in economic value of urban residential and commercial lands, thereby promoting the economic growth. However, adjusted planning policies may lead to ecological destruction. So, this study provides model verifications and path guidance to realise coordinated sustainable development between economy and ecology, serving as an important reference to formulating proper policies for urban development. [ABSTRACT FROM AUTHOR]

Urban population size dynamic adjustment has been widely studied for a long time; however, the dynamic adjustment at the urban hierarchy level remains unknown. This study utilises China's urban population data to investigate the dynamic adjustment. Specifically, we utilise methods of the Gini coefficient, linear regression, and coefficients of Spearman, and Moran's I to explore the dynamic adjustment tendency, adjustment rate and correlation of urban population size distribution of different urban hierarchies and groups of different urban hierarchies. We find that the urban population size spatial distribution is gradually even within the same urban hierarchy. The adjustment rate to be even of Megacity II is the highest, and Megacity I is the bottom. We also find the population size spatial distribution of the whole urban system is gradually uneven. We explore the dynamic process by sorting urban hierarchies into six groups. Furthermore, we find there is a complex correlation between different urban hierarchies and global spatial correlation among China cities. These results are valuable as references for Chinese policy-makers and practitioners and will enable them to adopt sustainable land use and urban population management measures. The study outcomes will also benefit future studies on spatial population size distribution in international communities. [ABSTRACT FROM AUTHOR]

Studying the structure of polycentric cities can promote a better understanding of urban development and contribute to urban planning. In this study, we identified polycentric cities in China and evaluated the urban centre development level of polycentric cities from new data and method. We used Luojia-1A night-time light (NTL) data, combined with the concept of natural cities (NCs), to identify urban centres and thus identify polycentric cities in China. In addition, we used the urban centre development index (UCDI) to quantify the urban centre development level (UCDL) that represents the overall urban centre development level within a polycentric city. The polycentric cities in China are characterized by the spatial distribution pattern of a larger number in the east and fewer in the west. There are a large number of polycentric cities in eastern China, and the closer to the coastal areas, the more polycentric cities there are. The distribution of UCDL in China's polycentric cities is characterized by significant spatial heterogeneity. UCDLs are generally smaller in polycentric cities in western China. In addition, polycentric cities in northeastern China have smaller UCDL. Polycentric cities with high UCDL are concentrated in the central and coastal regions of China. [ABSTRACT FROM AUTHOR]

Cities play an essential role in promoting the development of the whole national economy. Many scholars have proposed different methods to study this problem, but all have ignored the critical influence of network attention on the economy. Therefore, this study uses big Network data to profoundly analyse the relationship between urban spatial development and economic growth in eastern, central, and western China, further study this issue, and provide theoretical support for urban economic development. This study first divides China into three regions: eastern, central, and western, then selects the indicators of the national economy and urban development from 2014 to 2019, and finally constructs a panel regression model to explore the relationship between urban space elements and national economic development. The results show that China's national economic development is closely related to spatial factors such as urban transportation, environment, science and technology, energy consumption, and housing price. These findings will help to make full use of the natural advantages of each region and guide the rational planning of the urban economy. This study provides references for urban development planning and industrial transfer in various regions of China and offers feasible suggestions for China's overall national economic growth. [ABSTRACT FROM AUTHOR]

The dynamic population distributions by activity type (e.g. working, shopping or in-home) are vital for resource allocation, urban planning and epidemic containment. Although studies have incorporated individual-level human mobility data to map population distribution by activity type, access to such data is hindered due to privacy issues and they rely on auxiliary data to provide priori activity knowledge. This paper presents a method for generating the population dynamics by activity type. We first introduce more readily available sequential snapshot data to construct the population mixture model, then decompose the population mixture, and finally estimate the dynamic population size for each activity. We test the method in the central districts of Guangzhou city, China, based on real-time Tencent user density data. Correlation analysis and accuracy assessment prove that our method can accurately estimate hourly distributions for populations engaging in working, stay-at-home, and socializing activities. The temporal distribution of the working population reproduces the regular work scenarios and socializing population displays complex spatial patterns. We also find that there is an underlying relationship between a region's function and its dynamic population structure. The presented method has great potential for application and could provide new insight for studying urban dynamic functions. [ABSTRACT FROM AUTHOR]