Applied Spatial Analysis and Policy最新文献

Urban vitality is widely recognized as a key criteria of urban quality, yet achieving a real-time evaluation of urban vitality and linking it with the effectiveness of urban renewal strategies presents ongoing challenges in policy and planning decision-making. To fill in this gap, this study developed an integrated evaluation framework and applied it to assess urban renewal projects in Suzhou through three quantitative dimensions: spatial behavior patterns, social perception dynamics, and policy intervention impacts. Key findings include: (1) In old communities, parking and structural upgrades significantly improve urban vitality (coefficients 0.127 and 0.099). (2) Public-facility renewal shows strong gains from traffic improvements (0.208), while excessive leisure space reduces effectiveness (− 0.299). (3) Cultural identity, lighting, and service facilities generate notable spatial spillover effects, underscoring multi-intervention synergies in mixed-use areas. The effectiveness of urban renewal initiatives is shaped by joint efforts of multiple interventions, especially in mixed-functional zones with residential, commercial, and public facilities. It is expected that this pioneering framework would support decision-making in future urban planning and design from a more scientific and data-driven perspective.

Identifying the key factors influencing healthcare resource distribution is crucial for advancing universal health coverage (UHC) and Sustainable Development Goals (SDGs). However, there remains a lack of empirical evidence based on nationwide, fine-grained spatial panel data in China. This study examines the spatiotemporal patterns and influencing factors of hospital beds and doctors across China’s prefectural cities, using spatial panel data from 2007 to 2022. Results show that, first, despite persistent spatial inequality, the level and equality of healthcare resources in China have steadily improved over the past years. Second, spatial panel data econometrics analyses show that the distribution of healthcare resources per capita is jointly influenced by public fiscal capacity, city support capacity, healthcare delivery system and population demand factors. Finally, findings highlight the roles of fiscal revenue-expenditure responsibilities and the “tiao” systems such as medical schools and military affiliated hospitals, as well as the potential neglect of floating population’s healthcare needs. In conclusion, this research provides robust evidence to support healthcare resource allocation and policymaking in China.

Rapid urbanization in arid megacities like Riyadh makes it difficult for people to access urban green space (UGS), which is essential for making cities livable and keeping people healthy. This study analyses the specific equity of urban green space (UGS) distribution across age and gender demographics. The study used 2020 demographic data and high-resolution greenspace mapping, applied to a 1 km² fishnet grid, using a special autocorrelation methodology. While the WorldPop demographic dataset retains its original 0.01 km² (100 m) resolution before aggregation, a 1 km² fishnet grid was employed in this study for spatial analysis. Per capita green space availability was assessed against WHO standards, followed by bivariate LISA and Moran’s I to quantify spatial mismatch between demographic density and UGS. Results show central and northwestern districts i.e. Diriyah and Al Murabba containing large continuous patches (> 338,190 m²), while peripheral zones like Bawdah and Al Haeer consists of lesser UGS area (< 20,919 m²). Children are the most disadvantageous groups with particularly in central and southern Riyadh (e.g., Al Murabba, Al Aziziyah) falling in the vulnerable 0–9 m² per capita category i.e. below the WHO guideline of 20 m². Global bivariate Moran’s I (-0.0699, p = 0.001) and highly negative z-scores confirm a strong inverse spatial association between population concentration and UGS. Local LISA maps show High-Low clusters (high population, low green space) concentrated in inner, eastern, and southeastern zones, while Low-High clusters dominate peripheral areas, indicating underused resources. This suggests that space is not vacant and green resources are underutilized. To address these disparities, decentralized greening strategies such as pocket parks, green corridors, and neighbourhood-level interventions targeting child-dense and working districts are imperative to align urban planning with SDG 11, SDG 13, and SDG 3.

Existing studies on urban innovation networks predominantly focus on multidimensional proximities. To advance this field, this study highlights the analysis of cities’ latent capacities, specifically potential comparative advantages (PCAs), as its core contribution. We analyze how industry-technology synergies driven by PCAs reshape collaborative innovation networks in China’s intelligent net-connected new energy vehicle (NEV) industry. Integrating product space and knowledge space theories, we construct a dual-layer network framework based on patent applications and industrial enterprise statistics to examine cities’ latent capacities across 53 Chinese cities (2013–2022). Using the Temporal Exponential Random Graph Model (TERGM) to analyze network dynamics, we found that: (1) Innovation collaboration is significantly driven by the complementarity between one city’s latent technological potential and another’s existing industrial capabilities. This indicates that partnerships are formed not just by similarity, but by matching local potential with external expertise. (2) Core industry segments, such as batteries and intelligent systems, rely heavily on these cross-domain synergies. Additionally, supporting services, such as engineering research and charging services, play a foundational role in NEV industry chain development. (3) The innovation network demonstrated emergent small-world characteristics. However, the significant influence of PCA synergies indicates that cities are actively utilizing latent capacities to build cross-regional bridges. This mechanism suggests that the strategic alignment of regional PCAs can help transcend structural lock-ins, thereby disrupting path-dependent trajectories and increasing network adaptability. These findings extend proximity-centric frameworks by revealing how latent synergies work alongside traditional proximities and advance innovation network theory through evolutionary economic geography.

The separation between workplaces and residences has led to prolonged commutes, exacerbating traffic congestion and carbon emissions in Chinese cities. While existing studies have applied the 5D framework—Density, Diversity, Design, Destination Accessibility, and Distance to Transit—to examine the built environment’s role in commuting behavior, few have incorporated daily weather conditions or explored interaction effects with socioeconomic factors. This study addresses these gaps by investigating how built environments around workplaces and residences influence commuting modes within the Chinese context, with particular attention to weather and individual attributes. Using travel data collected via the Daynamica app, we applied factor analysis to condense 5D indicators into two composite measures: Density-Diversity (DenDiv) and Facility Accessibility (FA). A nested logit model was then employed to analyze six commuting modes, including interactions among the built environment, weather conditions, and individual characteristics. Results indicate that higher DenDiv and better FA at both locations significantly reduce bus usage. Increased DenDiv at both locations promotes bicycle commuting and discourages e-bike use. Improved FA near residences encourages subway commuting, whereas FA near workplaces suppresses it. Weather conditions—especially rainfall—also significantly alter mode preferences, reinforcing public transport use while deterring cycling. Interaction analyses further reveal that built environment effects vary across income, age, and health groups. These findings underscore the need for spatially differentiated planning strategies that integrate meteorological factors to promote sustainable commuting.

Road traffic accidents remain a major cause of fatalities worldwide, accompanied by considerable economic and societal costs. This study aims to find the relationship between key contributing factors to accidents and road traffic violations in rural and urban areas separately, an aspect overlooked in the previous research. More specifically, the study examined the impact of traffic violations across the eight urban and rural states in the United States, analyzing variables such as driver fault, vehicle ownership, license type, function type, and speed. Frequent violations, including negligent driving, manslaughter, vehicle registration infractions, hit-and-run incidents, and reckless driving, were assessed using the Random Forest machine learning model. Performance metrics, including accuracy, F1-measure, precision, and Kappa statistics, validated the model’s effectiveness. Partial dependence plots explored the relationships between violations and contributing factors. The results revealed distinct patterns between urban and rural areas. In rural settings, violations were driven mainly by speeding, negligent driving, overcorrecting, and non-owner drivers. In urban areas, reckless driving, drug use, improper lane usage, tailgating, and failure to yield were the predominant factors. These findings underscore the need for tailored interventions to address area-specific violations, helping policymakers implement strategies to reduce violations and improve road safety.

Understanding the dynamic relationships among multidimensional human wellbeing (MHW) is crucial for advancing the 2030 Sustainable Development Goals and fostering regional sustainability. In this study, we quantified and mapped economic wellbeing (ECOW), social wellbeing (SOCW), and environmental wellbeing (ENVW) in the Yangtze River Delta Urban Agglomeration (YRDUA) from 2001 to 2021 using multi-source panel data. We then investigated the spatio-temporal synergies/trade-offs and driving forces of MHW through multi-scale geospatial analysis and machine learning methods. The results represent: (1) High-value areas of ECOW and SOCW were similarly concentrated in developed eastern cities, while northern Zhejiang and southern Anhui’s forest regions exhibited the highest ENVW. (2) The temporal correlation and most cities’ spatial correlation among MHW were positive, indicating a macro trend of synergistic growth. The correlation intensity of ECOW-SOCW was significantly higher than that of ECOW-ENVW and SOCW-ENVW. (3) From a micro perspective, nearly all cities had encountered varying degrees of MHW trade-offs at different time points. The alternating patterns of synergies and trade-offs embodied the wave-like progression toward regional sustainability. Developed and underdeveloped cities had relatively higher synergy indices in ECOW-SOCW and SOCW-ENVW, respectively, while medium-developed cities demonstrated the strongest trade-offs in ECOW-ENVW. (4) The dominant factors for ECOW, SOCW, and ENVW showed some differences, with industrial structure, economic development, government intervention, employment situation, population size, and terrain features serving as core forces. The positive influence of most factors drove the emergence of synergistic trend, while frequent trade-offs observed at finer scales mainly stemmed from fluctuations in non-linear effects.

Municipal solid waste disposal is a global problem that is associated with continuous population growth and urban expansion especially in developing countries. City planners find it challenging due to lack of accessible, open source and automated tools for evidence-based landfill site selection. Bulawayo, Zimbabwe’s second-largest city, faces acute solid waste management challenges from the Ngozi Mine landfill which is now overcapacity and has exceeded its lifespan often resulting in frequent fires. This study seeks to identify suitable landfill sites in Bulawayo using an open-source and script-based hybrid GIS and Fuzzy AHP approach within R to support sustainable waste management. Eleven environmental, economic, and social criteria from Shuttle Radar Topography Mission, Landsat 8, WorldPop, Diva GIS, Open Street Map, Food and Agricultural Organisation, and Bulawayo City Council were processed and analysed in QGIS (30 m resolution), and weighted via Fuzzy-AHP in R. Weighted overlay produced suitability maps and candidate landfill sites that were validated and ranked using CR, ROC-AUC and OAT sensitivity analysis in R. The results indicate the following area distributions: not suitable (2,940.75 ha, 5.36%), less suitable (24,633.68 ha, 44.92%), suitable (27,011.96 ha, 49.25%), and more suitable (257.95 ha, 0.47%). Six candidate sites, each exceeding 15 hectares, were identified as viable alternatives for addressing Bulawayo’s waste management challenges, with Site C emerging as the top-ranked option (defuzzified score: 0.224) based on its superior performance across 11 weighted criteria. Model robustness was confirmed by a consistency ratio of 0.037 (< 0.1 threshold), an AUC of 0.854 from ROC validation, and a high Spearman rank correlation (mean ρ = 0.922) in sensitivity analysis, ensuring reliable rankings above ± 30% weight perturbations. Bulawayo City Council should immediately prioritize Site C development through phased implementation integrating residential/industrial buffers and urban growth modelling, and pilot geotechnical investigations across all sites to establish slope risk thresholds specific to Bulawayo soils. The findings from the study provide researchers, urban planners and local authorities with a scientifically grounded basis for sustainable waste management policy and site allocation through enhancing reproducibility, automation and cost reduction relative to ArcGIS-based methods, thereby advancing both industry practice and methodological knowledge.

India’s renewable energy transition is evolving through differentiated state-level pathways, raising the question of whether renewable capacity expansion is translating into meaningful fossil displacement and decarbonization. This study assesses transition performance in four major renewable-producing states - Gujarat, Rajasthan, Karnataka and Maharashtra - using an entropy-weighted composite index constructed from environmental, social and economic indicators for 2016–2024 and applies grey forecasting to estimate near-term feasibility through 2030. The composite index scores reveal persistent spatial heterogeneity, ranging from 0.59 − 0.33 across the four states, along with Grey Model (1,1) validation, yielding Mean Absolute Percentage Error (MAPE) = 4.84% and R² = 0.98. Rajasthan ranks highest in 2024, Gujarat shows the strongest recent improvement, Karnataka remains consistently strong, and Maharashtra lags comparatively. Forecasts indicate that all four states continue improving through 2030; however, projected trajectories remain insufficient to close the feasibility gap toward India’s 2030 non-fossil target, suggesting that acceleration rather than continuation is required to align subnational pathways with national ambitions. The results also show that displacement-relevant dimensions such as CO₂ emissions, carbon intensity and renewable generation do not advance uniformly with capacity expansion, underscoring that transition outcomes cannot be inferred from installed capacity alone. Together, the composite and forecasting analyses offer complementary insight into how transition progress, fossil displacement and feasibility vary across states. These findings highlight the importance of monitoring subnational transition dynamics, identifying bottlenecks, and supporting differentiated policy strategies in India’s shift toward a non-fossil energy system. More broadly, the study provides an evidence-based approach for evaluating renewable transition trajectories under data constraints and linking transition performance to national clean energy commitments.

As global urbanization advances, redeveloping low-efficiency land resources is crucial for alleviating spatial supply-demand conflicts and promoting synergy between the built environment and human activities. A primary challenge is determining the most suitable land uses to meet residents’ needs. Traditionally, decisions have been guided by governmental goals and planners’ expertise, often lacking new data and technologies to measure complex interactions between underutilized land and its surrounding environments, driven by residents’ daily activities, which also reflect their spatial functional needs. Understanding human mobility patterns of high-performing land can inform decisions about the redevelopment of those low-efficiency lands. In this regard, we propose a novel approach for determining land use functions in urban renewal areas by accounting for human mobility patterns and their relationships with land use performance. The land use performance is measured using multi-source datasets across three dimensions: vitality, spatial quality, and functional service and output. We then use the knowledge learned from the human mobility patterns of high-performing areas to help determine the dominant functions and the proportion of various land uses of underutilized areas in future development. The case study of Nanjing, China, suggests that the configuration derived from our method, with relatively low loss and high accuracy, aligns well with regulatory planning and stakeholders’ requirements. Additionally, we propose human-centered land use policies that emphasize adjusting key indicators, establishing coordination mechanisms, and implementing supporting measures for redevelopment. These recommendations aim to inform the practice of redeveloping urban low-efficiency land and foster the adaptation of urban form to human dynamics.