Applied Spatial Analysis and Policy最新文献

This study presents an innovative approach that integrates Geographic Information Systems (GIS) and Machine Learning (ML) techniques to optimize school site selection in the Konak and Karabağlar districts of İzmir. The study was conducted using 12 different criteria and 39 alternative sites by integrating the Analytic Hierarchy Process (AHP), Equal Weighting, and Extreme Gradient Boosting (XGBoost) methods with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The alternative sites were analyzed based on the average pixel values of the criteria, and AS4 was identified as the most suitable site. The ML-based method, ML_Rank, significantly contributed to the ranking of alternatives, showing high compatibility with the Equal Weighting and XGBoost methods (ρ = 0.9745 and ρ = 0.8813, respectively). In contrast, AHP exhibited the lowest correlation among the methods, highlighting the superior objectivity and consistency of ML-based approaches in ranking alternatives. The scalable and automated structure of ML_Rank enabled the rapid and reliable analysis of large datasets and complex criteria. Spearman correlation analysis demonstrated that ML-based methods improve decision-making processes by producing objective and consistent results. Thus, the findings reveal that GIS and ML techniques can be effectively utilized in critical planning processes such as school site selection. These methods contribute to sustainable urban planning by supporting the equitable distribution of educational services, enhancing student accessibility, and promoting efficient resource utilization.

Urban planning and policy development increasingly rely on open spatial data and analytical tools to address complex challenges such as rapid urbanization, climate change, and disaster risk. This study systematically reviews and classifies optimization methods applied to open spatial data, aiming to enhance its utility in urban planning and policy contexts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 77 peer-reviewed articles published between 2015 and 2025 were selected from an initial pool of 1,050 studies sourced from the Scopus database. The findings highlight that institutional datasets—both fully and partially open-access—and crowdsourced platforms, particularly OpenStreetMap (OSM), dominate as primary data sources. QGIS and Python emerge as the most frequently used analytical tools across a diverse range of urban applications. Building on the synthesis of the reviewed literature, this study introduces a five-dimensional optimization framework comprising functional, computational, data connectivity, participatory, and reproducibility dimensions, which collectively enable more adaptive, transparent, and collaborative approaches to urban spatial modeling. The framework offers practical guidance for leveraging open data in evidence-based urban planning and policymaking, ultimately contributing to more sustainable and resilient cities.

The effective utilization of limited urban land is achievable through the implementation of comprehensive zoning plans. Planning decisions must prioritize the allocation of land for its most appropriate uses, grounded in scientific criteria and urban planning principles. This study aims to develop a framework and decision-support platform specifically for the siting of filling stations, which represent a distinct category of urban land use. To inform planning efforts, site selection analyses were conducted using the Analytic Hierarchy Process (AHP), a widely recognized method within the Spatial Multi-Criteria Decision-Making (S-MCDM) framework, integrated with Geographic Information Systems (GIS). The proposed model facilitates the identification of optimal locations for filling stations in accordance with zoning regulations. As a result of the analysis, a total of 3,445 suitable areas were identified within the city of Trabzon, ensuring adequate spatial distribution across all districts. Notably, the findings indicate that 63% of currently operational filling stations are located in areas deemed unsuitable based on the selected criteria and their respective weights within the AHP framework. These results highlight the necessity for evidence-based planning approaches to guide urban land use decisions effectively.

Understanding how multiple factors affect land subsidence helps to take a scientific approach to preventing and controlling land subsidence. Previous studies have mainly focused on the monitoring and prediction of land subsidence, with less research on the causes of land subsidence. This study proposes an analytical framework to analyze the correlation between land subsidence anomalies and different influencing factors. First, the spatial local outlier measure (SLOM) algorithm is used to calculate the land subsidence anomalies, then the relationship between the land subsidence anomalies and the influencing factors is modeled using the Random Forest algorithm, and finally the contribution of multiple factors to land subsidence anomalies is analysed using the SHapley Additive exPlanation (SHAP) method. The research dataset includes land subsidence monitoring and related socio-economic factors from 2017 to 2019 in Cangzhou City, Hebei Province, and the effects of remote sensing of nighttime lighting, precipitation, DEM, slope, and aspect on land subsidence are determined. The results show that the anomalies of land subsidence in Cangzhou City account for about 10% of all detected points, which are mainly distributed in the west and south of Cangzhou. The analysis identifies human activities and precipitation as the primary drivers, with multi-year average SHAP value contributions of 22.82% and 23.69%, respectively.

COVID-19 has significantly impacted the global community, with tourism exacerbating its spread. However, research on tourism’s contribution to epidemic diffusion is scarce. Here, we integrate interdisciplinary and multidisciplinary knowledge to develop a spatiotemporal epidemic diffusion model for urban tourist areas based on tourist flow and virus contact transmission rules between individuals. We simulate the spatiotemporal virus spread process in tourists at the city scale. The research reveals that heavily-visited small tourist areas are the primary areas for virus transmission. Even small scenic areas with low visitation rates experienced virus transmission after more than one day of latency. For COVID-19, tourist input viruses are likely to cause transmission in sightseeing areas. However, whether the virus spreads uncontrollably among tourists depends on the city’s reception quantity. Furthermore, spatiotemporal attribute datasets can assist authorities in evaluating infection risks to adopt targeted strategies based on local natural, economic, and social characteristics.

With the growing role of social media in crisis communication, public opinion on urban public safety emergencies spreads rapidly, creating multipath configurational effects. Using the Technology-Organization-Environment framework, this study examines the configurational mechanisms of online opinion diffusion from the perspectives of event characteristics, public behavior, and government response. Based on 64 typical urban cases in China (2020–2025), fuzzy-set Qualitative Comparative Analysis and Necessary Condition Analysis are applied to identify multiple pathways leading to high diffusion. The results show that event severity, emotional contagion, government responsiveness, and media involvement interact in diverse ways to drive information amplification. No single factor proves necessary, underscoring the complexity of opinion formation. This study provides theoretical insights into multidimensional interactions and practical implications for strengthening risk governance and opinion management in digital urban environments.

In the post-2010 period in England, largely attributable to austerity measures, two unexpected demographic trends emerged. Infant mortality rates rose and life expectancy at best stalled, and at worse fell. Various analyses of life expectancy trends have unpicked the age-specific mortality rates but without a focus on deaths to infants aged less than one year of age. Since this age-group is the first age in life expectancy calculations, it is possible that changes in infant mortality rates form part of changes in life expectancies. This brief report investigates time trends in infant mortality and life expectancy (LE) at birth between the years 2000 and 2019 by Index of Multiple Deprivation 2015 (income) quintile. Using 2014 as the ‘jump off’ year as this was when infant mortality began to rise, alternative scenarios for the first year of life in LE calculations are used to reveal whether any differences in LE at birth occur, including if the downward trends in infant mortality observed prior to 2014 had continued to 2019. Even though the trend-based model estimates an excess of nearly 1,400 infant deaths occurred, the results show no significant impact on the life expectancy at birth calculations between the observed and scenario-based infant mortalities. This is important for researchers to know since anyone seeking to understand pre-pandemic falling / stalling life expectancies can concentrate on other aspects to determine key reasons. Further work on infant mortality is required since, post-pandemic, rates have risen again.

Understanding the supply-demand relationships (SDRs) of multiple cultivated land functions (CLFs) is crucial for achieving the sustainable utilization of cultivated land. This study aimed to construct a research framework that integrates quantitative evaluation, spatiotemporal evolution, regional differences, and type zoning for analyzing the multiscale SDRs of multiple CLFs. Taking the Yangtze River Delta region as an example, we quantified the supply and demand of three CLFs—agricultural production, ecological service, and landscape culture—along with their changes at grid, county, and prefecture-level city scales between 2000 and 2020. We then analyzed the spatiotemporal characteristics and differences in SDRs along urban-rural gradients and across cities of varying sizes. The results revealed both similarities and differences in the changes and SDRs of multiple CLFs at different spatial scales. The demand for all three CLFs consistently exceeded supply across multiple scales in economically developed areas. Supply deficit regions for all three CLFs were primarily concentrated 10–20 km from city centers. Megacities and supercities exhibited more significant and intensified supply deficits for these CLFs. Finally, we identified eight distinct SDR zones of multiple CLFs exhibiting different characteristics at different spatial scales that aimed to support decision-makers in promoting the sustainable utilization and management of cultivated land.

In the “post-poverty alleviation era,” rural tourism serves as a key strategy for preventing large-scale return to poverty and promoting rural revitalization in western minority regions. This study constructs a structural equation model to analyze the comprehensive effect of preventing return to poverty of rural tourism from three dimensions: economic income, social culture and living environment in Naxi Ethnic Township, Markam County, Tibet. On this basis, it analyses the “core-periphery” spatial differentiation characteristics and the influence mechanism of effect of preventing return to poverty of rural tourism. The results show that: ①Rural tourism has an obvious effect of preventing return to poverty. Which not only consolidates the channels for off-poverty population to obtain high income sources, but also innovates the self-development ability of off-poverty population, and it leads the fashion of off-poverty population to pursue high-quality life. ②The effect of preventing return to poverty in the core and the periphery does not follow a single spatial distance attenuation law, but presents a multidimensional and multivariate spatial imbalance distribution: economic income “core high-periphery low”, social culture “core high-periphery low” and human settlements “core low-periphery high”. ③Through economic “demonstration”, social “diffusion” and environmental “demonstration”, the core area realizes the “core-periphery” integrated effect of preventing return to poverty.

The Chinese government’s primary policy objective in promoting new-type urbanization in recent years has been to enhance people-oriented social welfare. China, as a representative nation of the Global South, positions its counties as the optimum spatial unit for enhancing the integrated development of urban and rural areas, policy implementation must ensure that the capacities and quality of county-level public service facilities are maintained. This paper introduces a set of evaluation and optimization methods for county public service facilities. This method incorporates individual spatiotemporal positioning big data and public service facility AOI data. Using the spatial grid as the analysis unit, the method divides the statistics of the county public service facilities and employs the land area of public service facilities as the medium to calculate the demand value and supply value. Subsequently, specific recommendations for facility optimization and configuration are provided. This paper examines the supply-demand matching of the county’s public service facilities from the macro scale of towns to the micro-scale of spatial analysis units, using Jurong county-level city in Jiangsu province as an experimental case. It demonstrates that schools, entertainment venues, and administrative facilities can attain full coverage. The majority of towns possess adequate hospitals and cultural facilities; nonetheless, facilities are deficient for the aged. The research delineates three types of spatial characteristics regarding the allocation of public service facilities at the meso-scale: flaky distribution, cluster distribution and cyclic distribution, and provides specific guidelines and recommendations for optimization.