Accurately identifying inefficient urban land is essential for urban regeneration and mining underutilized assets. Previous studies have primarily focused on examining the overall efficiency of land use without adequately considering the heterogeneity of urban land use types and comprehensive characteristics of urban quality. As a result, the spatial accuracy and precision of research findings have been relatively low. To address this gap, we developed a comprehensive method to identify inefficient urban lands for residential, commercial, and industrial use. The method integrated multi-source geographic data to quantitatively characterize the efficiency of different land use types considering six key dimensions, including building attribute, urban service, transportation condition, environmental quality, business performance, and production efficiency, utilized principal component analysis to reduce the multicollinearity and the dimensionality of the data, and identified land clusters with similar features that were inefficiently used by means of hierarchical clustering. By applying the method to Changsha, China, we validated its effectiveness. The results demonstrate that the method can accurately identify inefficient residential, commercial, and industrial land, with kappa coefficients of 0.71, 0.77, and 0.68, respectively. The identification results reveal the spatial distribution patterns of different types of inefficient land. Inefficient residential land is concentrated towards the city center, particularly in central areas. Inefficient commercial land is relatively evenly distributed, mainly outside the core commercial regions. Inefficient industrial land clusters towards the periphery, forming several agglomeration areas centered around industrial parks. By precisely identifying inefficient urban land and focusing on the key influencing factors, the proposed method enables the site selection of urban regeneration, site redevelopment evaluation, and optimization of urban resources.

Exploring the heterogeneous characteristics of the urban expansion process is essential for understanding the dynamics of the urban spatial structure. Many studies focused on depicting the spatio-temporal characteristics based on urban expansion patches. However, measuring heterogeneous characteristics of urban expansion from agglomeration areas comprising the expanded urban construction land patches have not been adequately explored. This study presents a novel approach and two improved indices for characterizing the heterogeneity of urban spatial agglomeration areas during urban expansion. Firstly, we proposed a Gaussian mixture model considering multiple constrains and density-based spatial clustering of applications with noise (DBSCAN) integration method to identify and extract the urban agglomeration areas automatically. Secondly, the gradient analysis and the compact index using the inverse “S” function are introduced to explore the spatio-temporal characteristics from a macrocosmic perspective. Finally, the compactness index (NCI) and normalized dispersion index (NDIS) are improved based on agglomeration area data. The microcosmic heterogeneous characteristics are measured by these two improved indices and the positional offset characteristics indices (POCIS). The method was implemented in the urban area of Changsha, Hunan Province, China in 2005, 2010, and 2015. The results show that (1) compared to that in the Changsha City Master Plan (2003–2020), the recognition rate was higher in the agglomeration areas than others. (2) The overall expansion trend in Changsha transitioned toward decentralization, making Changsha a polycentric city. (3) The agglomeration of urban expansion in the east-west direction became compact; that in the north-south direction became looser; most clusters expanded to the west and a new sub-center would appear. The proposed method can effectively characterize their heterogeneity, which can provide valuable references for urban planning and policymaking.

Understanding the integration process of urban agglomeration is essential for sustainable regional development and urban planning. However, few studies have analyzed the spatial integration patterns of metropolitan regions according to the impacts of landscape ecology along rail transit corridors. This study performed a comprehensive inter-city gradient analysis using landscape metrics and radar charts in order to determine the integration characteristics of an urban agglomeration. Specifically, we analyzed the evolution of spatial heterogeneity and functional landscapes along gradient transects in the Changsha–Zhuzhou–Xiangtan (CZT) metropolitan region during the period of 1995–2015. Four landscape functional zones (urban center, urban area, urban–rural fringe, and green core) were identified based on a cluster analysis of landscape composition, connectivity, and fragmentation. The landscape metric NP/LPI (number of patches/largest patch index) was proposed to identify the urban–rural fringe, which revealed that the CZT region exhibited a more aggregated form, characterized by a single-core, continuous development, and the compression of green space. The integration of cities has resulted in continued compression and fragmentation of ecological space. Therefore, strategies for controlling urban expansion should be adopted for sustainable urban development. The proposed method can be used to quantify the integration characteristics of urban agglomerations, providing scientific support for urban landscape planning.

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

The effective extraction of impervious surfaces is critical to monitor their expansion and ensure the sustainable development of cities. Open geographic data can provide a large number of training samples for machine learning methods based on remote-sensed images to extract impervious surfaces due to their advantages of low acquisition cost and large coverage. However, training samples generated from open geographic data suffer from severe sample imbalance. Although one-class methods can effectively extract an impervious surface based on imbalanced samples, most of the current one-class methods ignore the fact that an impervious surface comprises varied geographic objects, such as roads and buildings. Therefore, this paper proposes an object-oriented deep multi-sphere support vector data description (OODMSVDD) method, which takes into account the diversity of impervious surfaces and incorporates a variety of open geographic data involving OpenStreetMap (OSM), Points of Interest (POIs), and trajectory GPS points to automatically generate massive samples for model learning, thereby improving the extraction of impervious surfaces with varied types. The feasibility of the proposed method is experimentally verified with an overall accuracy of 87.43%, and its superior impervious surface classification performance is shown via comparative experiments. This provides a new, accurate, and more suitable extraction method for complex impervious surfaces.

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