Applied Spatial Analysis and Policy最新文献

Urban districts bear the important functions of the city and are an indispensable part of the urbanization process in China. However, the existing research rarely takes the urban districts as the research object, and ignores the spatiotemporal heterogeneity of influencing factors. To fill the above gaps, this study puts forward an analytical framework of urban expansion in order to fully reveal the spatiotemporal characteristics of the expanding cities. This study assumes that urban expansion is driven by six factors (including population, economy, industry, traffic, environment, and urban construction), and these factors have different effects on different cities in different time periods. Based on the above framework and assumptions, the main contents and methods of this study are as follows: First, taking urban districts as the basic unit, the expanding cities during 2010–2020 are identified by using the population change rate. Second, the characteristics of urban expansion are analyzed. Then, the multiple linear regression (MLR) model is used to screen out the significant influencing factors. Finally, the geographically and temporally weighted regression (GTWR) model is used to test the spatiotemporal heterogeneity of these factors. The results show that the phenomenon of urban expansion is widespread and presents obvious spatiotemporal characteristics. The four factors in the hypothesis, population density, per capita GDP, tertiary industry and environmental governance, have been proved to be the key drivers of urban expansion. The influence of these factors has significant spatiotemporal heterogeneity. The main contribution of this study is to put forward a logical framework for studying urban expansion from the perspective of urban districts, which includes three aspects: identification, spatiotemporal characteristics, and influencing factors. At the same time, it provides a macro perspective for policy makers to understand the characteristics of urban expansion and provides more scientific support in urban development planning.

This study investigates the differential gains from regionalization by examining how institutional forms and border effects shape regional cooperation’s impact on interurban capital flows in China’s Yangtze River Delta (YRD). Drawing on dataset of cooperation news and investment events, it investigates whether, how, and for whom different modes of regional cooperation facilitates capital redistribution. The findings indicate that interprovincial mechanisms, particularly intercity alliances and official exchanges, significantly promote capital flows from core to peripheral cities. Yet the redistributive effects are highly uneven. Only a subset of strategically positioned border-zone cities that are empowered by delegated authority and embedded within cross-regional industrial alliances secure substantial capital inflows. This spatial selectivity does not indicate governance failure but reflects a state-led strategy of targeted spatial development. In contrast to the hopeful assumption of inclusive trickle-down benefits, regional cooperation in China follows a logic of experimental regionalism, privileging a limited number of institutionally prepared peripheral cities as pilot for cross-provincial integration for core-led capital spillovers. Over time, such selective inclusion may contribute to broader core–periphery convergence, but only through a phased, state-orchestrated process of institutional coordination.

The matter of ensuring equal access to healthcare services is a challenge of global significance, particularly in developing countries. Improvement in these services would lead to a reduction in health deprivation and regional inequities. This research estimates the multidimensional health poverty (MHP) index to (i) identify the spatial clusters of districts vulnerable to health deprivation and explore the spatial connection between neighboring districts concerning MHP scores, and (ii) investigate the local-varying spatial effect of determinants MHP. Pakistan social and living standard measurement (PSLM) data were used to estimate the index by applying Alkire–Foster framework. Spatial dependence was tested through Moran’s I statistic while local-varying coefficients were estimated with multiscale geographically weighted regression (MGWR). Distinct spatial clusters of districts with lower access to healthcare services in terms of MHP were seen in Baluchistan, while higher access to healthcare districts were concentrated in Punjab. A discernible upward trajectory in spatial relationship was noted during the period spanning 2008-09 to 2019-20. The MGWR analysis revealed that income, education, hospital access, television, and public transportation access were significantly and negatively associated to MHP incidence. It is observed that local coefficients of MGWR exhibit variation across districts in Pakistan. Findings of this study showed a rise in spatial inequalities concerning MHP. It is imperative for the government and public health workers to give precedence to highly deprived or lower access to health care services districts by directing their attention towards the primary factors that significantly enhance access to healthcare services.

China has entered the late stage of urbanization, where cities of various sizes have been initiating the restoration and construction of urban ecosystems, playing a crucial role in enhancing urban ecological functions. As the earliest city in China to complete urbanization, Shenzhen has implemented a series of urban ecological restoration and construction projects over the past two decades, significantly improving urban ecological quality. In this study, we focused on the Bao’an District in Shenzhen, utilizing high spatial resolution remote sensing images obtained from 2008 to 2023, employing six machine learning algorithms and the landscape security pattern analysis model, to systematically analyze the changes in urban greenspaces at the patch and landscape scales over the 15-year period and their impacts on the regional ecological security patterns. The analytical results indicated that, among the six machine learning models, the U-Net + model demonstrated the highest performance in extracting urban greenspaces. Secondly, the study period witnessed an increase in both the area and connectivity of urban greenspaces in the study area. Finally, the regional ecological security patterns across the study area have been significantly restored, with increases in both the low-security level and the high-security level area coverage of urban greenspaces. The improvements in the ecological qualities of the study area is primarily attributed to the designation of urban ecological redlines, the implementation of urban ecological restoration and construction projects, and the transformation of urban economic development. This study holds significant value for systematically assessing the changes in urban greenspaces of Chinese cities during the late stage of population urbanization. In addition, the above findings also provide practical guidance for optimizing urban greenspace patterns and enhancing urban ecological security.

As urban populations continued to rise, cities faced increasing challenges in land use management, infrastructure provision, environmental protection, and social equity. Addressing these complexities required approaches that were both data-driven and responsive to decision-making needs. This review examined the integration of Geographic Information Systems (GIS), Artificial Intelligence and Machine Learning (AI/ML), and Multi-Criteria Decision Analysis (MCDA) as a comprehensive framework for sustainable urban settlement planning. Drawing from 100 peer-reviewed studies published between 2017 and 2025, the paper evaluated the theoretical foundations, individual capabilities, and combined potential of these tools. GIS provided the spatial foundation through mapping and data layering, AI/ML contributed predictive and adaptive modelling, and MCDA incorporated structured prioritization for complex decision-making. These components interacted in a sequential but iterative manner, where GIS outputs informed AI/ML models, and both collectively served as inputs for MCDA. The integration was flexible and could be adapted depending on the planning context. Together, these technologies enhanced the capacity for land suitability analysis, urban growth forecasting, informal settlement detection, and resilience planning. Case studies from diverse regions demonstrated the effectiveness of this integrated approach in enhancing planning accuracy and stakeholder engagement. The review also identified operational, technical, and governance-related challenges, including data fragmentation, interoperability limitations, and ethical concerns. Finally, it highlighted future directions that emphasized participatory planning, open data standards, and adaptive modelling. The integrated GIS–AI/ML–MCDA framework was found to provide a scalable and inclusive pathway to guide cities toward sustainability and resilience in the context of rapid urbanization. The review’s distinct contribution lay in demonstrating how the integration of GIS, AI/ML, and MCDA offered a unified, adaptive framework that strengthened the links between sustainability and resilience in urban settlement planning.

The sustainable development of resource-based cities is facing more severe challenges than other cities. The achievement of sustainable development in resource-based cities is a multidimensional issue, with the Sustainable Development Goals (SDGs) providing a framework that guides the evaluation dimensions for their sustainable development. Despite the significance of SDGs, few studies have integrated them with the core sustainable development needs of resource-based cities to assess their development capacity. Based on the prefecture-level data from 2006 to 2020 in China, we construct an SDGs-oriented sustainable development evaluation framework. This study verifies the applicability of the framework by analyzing the spatiotemporal dynamics of sustainable development of resource-based cities, and discusses the link between sustainability and carbon emissions. The important findings of this paper include: (1) The sustainable development of resource-based cities shows a balanced tendency in space, and regenerative cities achieved the highest level. (2) there is a significant positive spatial correlation in resource-based cities, and show a narrowing geographical disparity. Considerable disparities exist in different domains in sustainable development level. (3) Many resource-based cities confront the issue of high carbon emissions, indicating persistent efforts towards industrial transformation remain crucial for carbon reduction.

In recent years, China’s shadow education sector has undergone dramatic transformation amid rapid expansion and increasingly stringent regulatory policies. Drawing on nationwide registration and deregistration data from 2000 to 2024, this study systematically examines the spatiotemporal dynamics of institutional entry, exit, and survival. By integrating multi-source data—including POIs, transport networks, policy documents, and city rankings—and applying an Accelerated Failure Time (AFT) survival model, the study reveals three key findings: (1) Temporally, institutional entry and exit closely align with policy shifts, with a sharp rise in exits following the “Double Reduction” policy, particularly among Academic Tutoring Institutions; (2) Spatially, the entry and exit of shadow education institutions exhibit pronounced clustering patterns across different developmental stages, with hotspots primarily concentrated in the economically developed eastern regions and gradually diffusing toward central and western China; (3) Institutional survival is shaped by individual characteristics, macro-environmental conditions, and micro-location attributes. Unlike general industries, shadow education is highly sensitive to policy environments, and the effects of influencing factors vary significantly across city tiers. The research in this paper helps to deepen the understanding of the survival risk mechanism of shadow education institutions, provides empirical support for evaluating the implementation effect of the “double reduction” policy, and also provides valuable references for policymakers and industry practitioners.

Under the background of negative population growth in China, the construction of infrastructure can effectively promote population mobility and reallocation. As the core of the national large-scale clean energy infrastructure, the West-East Gas Pipeline (WEP) facilitates the efficient cross-regional allocation of energy resources and drives urban green development. However, does it also affect population mobility in shrinking cities? By using urban data from 2006 to 2023, this study analyzes the impact of WEP II on population mobility in shrinking cities along its route. The results indicate that the WEP II significantly reduces population outflow from shrinking cities along its route. The internal mechanism is explained by the improvement of wage, urbanization and urban green development performance brought by WEP II. Heterogeneity analysis shows that WEP II had a significant inhibitory effect on population outflow from shrinking cities of Eastern China, Central China, and non-resource-based, while the influence is not significant in Western and Northeastern China. Shrinking cities exhibit a “serious shrinkage in north, slight shrinkage in the south” pattern, spatial analysis confirms a “north-south” gradient in urban shrinkage intensity. Spatial econometric analysis further reveals that the WEP II has significantly inhibited population outflow from shrinking cities along the pipeline, while promoting population outflow from geographically adjacent shrinking cities. This study provides empirical evidence on the impact of energy infrastructure on regional population mobility and offers an important theoretical basis and practical reference for advancing the energy revolution, improving regional coordinated development strategies, and constructing sustainable urban development models.

This study proposes an innovative urban fire risk evaluation framework based on street-view images to address escalating fire hazards in rapidly urbanizing megacities. Using Jiangning District, Nanjing, China, as a case study, this study analyzes historical fire incident data from 2022 to 2023. The primary innovation lies in developing a novel image-based scoring metric (SIE2.5KM), which extracts features that enable the assessment of vertical fire risk distributed in the three-dimensional built environment. This metric captures fine-grained, dynamic risk factors often overlooked in simple geospatial analysis. The study integrates SIE2.5KM with geospatial data, including population density, building characteristics, and infrastructure accessibility, to model fire risk across 1,775 500 m×500 m grid cells. Through rigorous threefold cross-validation, we compared the performance of four spatial econometric models and three machine learning models. The empirical results show that XGBoost is the optimal model, achieving superior predictive accuracy (R² always > 0.8, with lower RMSE/MAE). Key findings identify SIE2.5KM, distance to fire stations, and mixed land use as critical urban fire risk drivers, while population density exhibits a complex, nonlinear relationship, indicating that the impact of population on fires can be alleviated by diverse fire management measures. Residual analysis explores the optimization potential of this study’s framework in areas such as urban forest interfaces, such as by incorporating combustible vegetation information through more in-depth analysis.

Despite extensive research on rural land reforms, existing studies often rely on linear, variable-oriented approaches that overlook the non-linear, interdependent nature of farmers’ homestead land transfer decisions, leading to oversimplified explanations of heterogeneous responses amid rapid urbanization. This configurational gap is especially evident in China, where farmers’ choices defy single-factor dominance, reflecting complex interactions shaped by multiple conjunctural causation. Advancing a configurational perspective, our analysis reveals three key insights: First, overall, farmers’ transfer willingness emerges through diverse, equifinal pathways characterized by asymmetric causation, demonstrating that decisions follow non-linear combinations rather than independent factors, thus transcending traditional regression models. Second, occupational differentiation generates a “dual paradox,” simultaneously promoting commercial transfers by reducing land dependency and facilitating asset monetization, while inhibiting public-interest transfers through weakened community ties and collective engagement. Third, resource endowments and environmental perceptions exhibit complex, context-dependent interactions, with no universally dominant factor—such as high farmland ownership enabling transfers in resource-rich contexts but constraining them amid economic insecurity. These findings enrich theories of rural transformation by emphasizing non-linear, asymmetric causation and offer policy insights for optimizing homestead reforms, addressing land idleness, urban-rural disparities, and poverty alleviation in developing contexts.