Applied Spatial Analysis and Policy最新文献

Digital economy policies, acting as a sturdy cornerstone for the development of the digital economy, play a pivotal role in stimulating entrepreneurial vitality of the logistics industry. This study leverages panel data from 277 cities in China spanning the period from 2009 to 2022 to conduct an in-depth exploration of the impact of digital economy policies on entrepreneurial vitality of the logistics industry, their underlying operational mechanisms, and their heterogeneous effects across different cities. The empirical findings indicate that digital economy policies DEPs exert a positive and lagged impact on the entrepreneurial vitality of the logistics industry. The results of the mechanism tests reveal that digital economy policies can elevate entrepreneurial vitality of the logistics industry through three crucial channels: promoting the construction of digital infrastructure, fostering digital innovation vitality, and providing talent support. The heterogeneity analysis uncovers that digital economy policies exhibit more pronounced positive effects in advanced cities and e-commerce demonstration cities. This study not only deepens our understanding of the relationship between digital economy policies and entrepreneurial vitality of the logistics industry but also provides a scientific basis for policymakers to implement targeted and precise policies, while simultaneously facilitating the high-quality development of logistics enterprises.

The construction of the Three Gorges Project has triggered profound transformations in the Rural Production Space System (RPSS) of the Three Gorges Reservoir Area (TGRA), characterized by land submergence, population resettlement, and industrial restructuring. While existing studies have explored these changes from sectoral perspectives, the application of life cycle theory to analyze RPSS evolution remains a critical gap. This study develops a novel analytical framework integrating life cycle theory to systematically examine the spatiotemporal dynamics of RPSS transformation in the TGRA from 1990 to 2020. We identify three core subsystems population, land, and industry and reveal their distinct transition trajectories: population and land transformations peaked before 2010, driven by urbanization and resettlement policies, while industrial transformation surged post-2010 under endogenous growth mechanisms. Notably, the RPSS transitioned from a “blood transfusion” model (external resource dependency) to a “blood production” model (self-sustaining development), highlighting the interplay of policy interventions and internal adaptations. Theoretically, this study pioneers the integration of life cycle theory into rural spatial system analysis, offering methodological advancements for complex system transitions. Finally, we propose actionable strategies “green industrial transformation, urban-rural integration, and institutional reforms” to address sustainability challenges in the TGRA, with implications for global regions impacted by large-scale hydraulic projects.

Reducing spatial inequality has been a central objective of Morocco’s Advanced Regionalization Project since 2015, yet regional disparities persist and continue to deepen. The twelve Moroccan regions evolve at different speeds, reflecting the enduring effects of core–periphery structures, path dependence, and spatial lock-ins that channel resources toward coastal growth poles while constraining peripheral territories. This research investigates the mechanisms behind these unequal trajectories, focusing on how demand-side welfare gaps, investment efficiency, and localized institutional capacity shape spatial inequality across regional, provincial, and municipal levels. Using spatial econometric estimation combined with nighttime-light data as a proxy for economic activity, the study reveals that while urban consumption and population concentration drive growth in illuminated cores, investments fail to generate inclusive spillovers. These structural imbalances reinforce geographic polarization and undermine the goals of territorial cohesion and social equity. Results reveal that growth and light intensity are concentrated in illuminated cores where consumption and population density reinforce cumulative advantages, whereas investment shows weak spillover capacity and often amplifies the core–periphery divide. These findings demonstrate that inequality in Morocco is not solely an income outcome but a manifestation of spatially embedded development structures and governance asymmetries. The evidence calls for a reorientation from uniform, top-down policies toward place-based strategies that leverage local potential, strengthen institutional capacity, and promote inclusive investment. Beyond Morocco, the analysis offers insights for developing countries facing similar spatial divides, highlighting the need for territorially sensitive approaches to achieve the SDGs on decent work, reduced inequalities, and sustainable cities.

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.