Applied Spatial Analysis and Policy最新文献

This study proposes an AI approach for analyzing retail space transformation and quantifying retail gentrification at scale. Moving beyond survey and interview-based approaches that are limited in coverage and granularity, we leverage a large language model (GPT-4o) to perform semantic reasoning on shopping-mall point-of-interest (POI) data. The method innovates an LLM-enabled entity extraction and classification pipeline that operationalizes retail gentrification through interpretable indicators, including brand count, level, internationalization, and localization. It offers a novel framework for enabling systematic, citywide measurement of commercial restructuring. Theoretically, we extend the retail gentrification lens from streetscapes to shopping malls and develop a mall-oriented assessment framework that integrates GPT-4o outputs with POI attributes into an intelligent evaluation model. Using Shanghai as a case, we apply a dual time-slice comparison (2019 and 2024) and triangulate results with three empirical cases to validate the model’s ability to detect gentrification signals. Results reveal strong spatio-temporal polarization: intensified gentrification in central areas alongside peripheral downgrading, with high-value clusters shifting toward the northeast. This approach offers a scalable paradigm for cross-city comparative research and commercial planning.

The growth of urban entrepreneurial ecosystems depends not only on co-location of firms but also on the structure and quality of their networks. This study examines how formal investor connections and implicit informal proximity-based links affect the growth of technological startups in Warsaw, Poland. Administrative microdata for 567 startups founded in 2016 are used to construct (i) a formal network of investor ties, distinguishing between individual and corporate investors, and (ii) an implicit informal network based on spatial proximity within 1 km, operationalised through a spatial weight matrix (SWM). Complex Network Analysis (CNA) is applied to describe network topology, and econometric modelling assesses the relationship between connectedness types and five-year asset growth. Results show that formal connectedness significantly enhances growth, whereas excessive co-location is associated with small but significant declines in performance, suggesting congestion and competition effects. The findings provide a transferable spatial-analytical framework for diagnosing connectivity in developing entrepreneurial ecosystems and offer actionable insights for urban policymakers, development agencies, and accelerators in Central and Eastern Europe (CEE) and beyond.

Rapid population aging poses severe challenges to socioeconomic development and places unprecedented pressure on sustainable rural development. However, the mechanisms through which aging impacts rural sustainability remain underexplored. Utilizing large-scale survey data from China, this study develops a comprehensive assessment framework to measure rural community sustainability (RCS). We analyze the mediating effects of human capital, land use transitions, and agricultural modernization in the process of population aging that undermines RCS. The results reveal that China’s overall RCS score is 56.38, indicating substantial scope for improvement, with significant variations observed across dimensions and provinces. Cropland resources, communal assets, labor resources, agricultural dependency, and economic development levels constitute the primary internal constraints for RCS. This study demonstrates that while population aging has no direct significant impact on overall RCS levels, it significantly undermines RCS through multiple mediating pathways. Specifically, aging indirectly weakens RCS by exacerbating cropland abandonment and reducing labor force supply, while promoting agricultural mechanization and facilitating cropland transfers can effectively mitigate the adverse effects of aging on RCS. Notably, population aging has a significant negative impact exclusively on the dimension of services and infrastructure sustainability. Based on these findings, we propose several policy interventions to optimize rural resource allocation and mitigate aging-related constraints.

Set against the dual imperatives of rural revitalization and cultural heritage preservation, research on heritage-based rural tourism has widely examined spatial clustering and influencing factors, yet comparatively less attention has been paid to cross-jurisdictional fragmentation and the identification of cultural heritage corridors based on reproducible spatial rules. This study examines how heritage-based rural tourism destinations are spatially organized and what factors drive their vitality in the Yangtze River Delta, using 1,247 nationally designated traditional villages. Based on a multidimensional dataset integrating geospatial coordinates, elevation and terrain conditions, transportation infrastructure, point-of-interest (POI) information, and cultural heritage resources, we first characterize regional clustering patterns and local hotspots, then evaluate multi-factor drivers, and finally identify potential cross-regional cultural heritage corridors. Kernel density estimation reveals a pronounced “core-and-corridor” configuration, with high-intensity clusters located in the Hangjiahu Plain, the Taihu Basin, and the transition zone spanning southern Anhui and western Zhejiang. Local Indicators of Spatial Association (LISA) further confirm discrete high-activity hotspots and highlight spatial fragmentation, where administrative boundaries form isolated “cultural enclaves” in which heritage resources are insufficiently activated. Multiple linear regression and random forest analyses jointly indicate that road network density is the dominant positive determinant of tourism vitality, whereas elevation and terrain ruggedness exert significant negative influences; the contribution of socio-economic variables is more limited in the linear specification but becomes more evident in the non-linear model. To operationalize the corridor perspective, we apply the density-based spatial clustering algorithm DBSCAN to heritage-rich villages (1 km buffer) to automatically detect corridor clusters, complemented by rule-based/manual delineation for a second potential route. Two principal cultural corridors are thus identified: an empirically delineated Huizhou corridor characterized by traditional craftsmanship, ancestral architecture, and folk theatre, and a prospective water-based route integrating historic settlements and regional performing arts. By advancing a “Pattern–Endowment–Driver–Gradient–Coupling” model, this study provides an empirically grounded basis for integrated spatial governance and cross-jurisdictional corridor planning for traditional villages.

Accurate and fine-scale population distribution data are essential for effective disaster risk assessment and mitigation, particularly in rapidly urbanizing regions where exposure to natural hazards is intensifying. This study presents a framework for population mapping under present and future conditions by integrating conventional approaches with machine learning. The framework combines scientific observations, such as satellite imagery, with policy-driven information, including zoning plans, thereby reflecting local social, political, and regulatory contexts. Specifically, this study focuses on creating high-resolution (40 m) gridded population maps for two densely populated watersheds, Kalu Oya and Mudun Ela, in Sri Lanka. The population grids were generated via a dasymetric approach, which disaggregated census data from administrative boundaries with building footprint datasets for the current condition. Future population distributions for 2030, 2050, and 2070 were projected via random forest (RF) algorithms under shared socioeconomic pathways (SSP1–SSP5). The RF model, developed using 25 features, showed the highest sensitivity to the normalized difference vegetation index (NDVI), land cover associated with urbanization, and distances from roads, healthcare facilities, and rivers. Current population grids were used to train the model, and accuracy was evaluated via statistical indicators, yielding an R² of 0.64, an MAE of 0.80 per/grid, and an RMSE of 1.06. The model was further enhanced by incorporating future development data extracted from official urban development plans for 2030. The outcome of this study provides a comprehensive framework for data-scarce region to generate high-resolution population maps useful for disaster risk management during flood and other natural disaster using publicly available dataset.

This study examines the relationship between digitalization and total fertility rates (TFR) across OECD countries over the period 2000–2021, accounting for spatial interdependence in fertility dynamics. Digitalization is conceptualized as a multidimensional process and is primarily measured using fixed broadband subscriptions per 100 people, complemented by mobile broadband penetration and ICT goods exports as robustness indicators. Employing spatial panel econometric techniques and comparing the results with non-spatial fixed-effects models, the analysis reveals a robust positive association between digitalization and realized fertility at the macro level. The preferred spatial error model indicates significant spatial dependence, suggesting that fertility outcomes are shaped by regionally correlated unobserved factors, such as shared welfare regimes, cultural proximity, and synchronized economic conditions, rather than direct spillovers of fertility levels. The results further identify an inverted U-shaped relationship between income per capita and fertility, as well as a U-shaped association between female labor-force participation and fertility, consistent with evolving work–family arrangements in advanced economies. While digitalization and female labor-force participation each display independent relationships with fertility outcomes, their interaction does not attain statistical significance. Overall, the findings underscore the importance of digitalization and spatial context in understanding contemporary fertility patterns and suggest that coordinated investments in digital infrastructures alongside family-supportive institutions may help mitigate persistently low fertility in OECD countries.

As cities pursue high-quality economic growth amid increasingly volatile domestic and global environments, enhancing economic resilience has emerged as a crucial strategic objective. This paper investigates the impact of new-type urbanization pilot policy (NUPP) on urban economic resilience (UER) using panel data from 292 Chinese cities spanning 2008 to 2019. Employing a difference-in-differences approach, the empirical results show that NUPP significantly enhances UER. Further analysis indicates that the mechanisms through which NUPP enhances UER—including industrial structure upgrading, technological innovation promotion, human capital accumulation, climate resilience strengthening, and digital economy development—are more pronounced in cities with lower levels of economic development. Heterogeneity analyses reveal that the positive effects of NUPP are stronger in cities with more developed industrial bases, less developed financial sectors, lower economic development levels, cities selected in the first batch of pilot programs, as well as western and inland cities. Additionally, spatial analysis identifies positive spillover effects of NUPP on the economic resilience of neighboring cities, with these effects primarily transmitted through industrial structure upgrading, technological innovation, and environmental regulation. These findings underscore the importance of deepening and refining NUPP strategies to establish sustainable, long-term pathways for fostering resilient urban economies.

This study presents an integrated framework to assess the social-ecological resilience of county-level units experiencing population decline, using Heilongjiang Province in northeast China as a case study. Building on the Human–Environment Systems (HES) framework, we construct a multidimensional evaluation model encompassing population vitality, economic capital, infrastructure, cultural assets, natural resources, and environmental protection. The analysis covers 61 county-level units from 2010 to 2020, examining subsystem coordination and population change–resilience linkages. The results reveal significant spatial heterogeneity in resilience outcomes. While ecological resilience generally improved over the past decade, social resilience remained uneven, especially in the Harbin–Daqing–Qiqihar industrial corridor and in former coal-mining areas. Overall, coupling coordination between social and ecological subsystems remains low, though notable gains have occurred in border counties where cultural–ecological resources are jointly leveraged. Population decline exerts varied effects: in parts of eastern Heilongjiang, reduced demographic pressure has coincided with ecological gains, whereas in central and northern areas, out-migration and diminishing managerial capacity have weakened ecological resilience. In contrast, population decline has a relatively limited impact on social resilience or subsystem coordination. The obstacle diagnosis identifies limited household wealth accumulation, gaps in cultural heritage preservation, and environmental quality as the primary barriers to strengthening resilience, with low educational attainment and population aging posing persistent challenges. Policy priorities should therefore focus on investing in human capital, accelerated industrial diversification, stronger cultural–ecological integration, and low-carbon transitions through cleaner production and improved environmental governance. The proposed framework also provides a transferable tool for county-scale assessment in other shrinking regions.

This study examines how territorial inequality and governance capacity shape the geography of intimate femicide in Chile. While most research on gender-based violence emphasizes individual or relational risk factors, less attention has been paid to the structural and institutional conditions that determine women’s vulnerability across places. Using municipal-level data for 2008–2022, we analyze the association between femicide rates, human development, and local state capacity. Results show that municipalities with higher human development exhibit lower femicide rates, but this protective effect depends on the strength of local governance. Where administrative and security capacities are strong, development translates into greater safety for women; where they are weak, socioeconomic progress alone does not prevent lethal violence. These findings highlight how uneven development and institutional asymmetries mediate gendered risks, showing that women’s security is not only a matter of social norms or household conditions but of territorial justice and state capability. Strengthening local institutional capacity is therefore essential to transform formal gender equality into effective protection across territories.