Applied Spatial Analysis and Policy最新文献

Understanding intercity population movement is vital for the socioeconomic development of urban areas. Based on Amap big data spanning 2019 to 2023, this study utilizes a combination of social network analysis and explainable machine learning techniques to explore the spatiotemporal patterns and determinants of intercity population movement across 368 cities in China. The findings highlight significant heterogeneity in the spatial distribution of population flow, characterized by a “southeast dense, northwest sparse” pattern, though spatial disparities have narrowed over the past five years. Population flow varied significantly across cities, with high-tier cities exhibiting contrasting net population inflow compared to low-tier cities. Hierarchical clustering patterns were evident, with the Beijing-Tianjin-Hebei region, Yangtze River Delta, and the Pearl River Delta emerging as primary hubs of population distribution. The population flow network demonstrated distinct community characteristics, with divisions closely aligned with geographical proximity and provincial-level administrative divisions. Basic regression analysis identified city population size, economic development, public service quality, and air quality as significant factors in population mobility. Further analysis using explainable machine learning techniques revealed that distance, high-speed rail connectivity, and population size were the most impactful determinants, displaying complex nonlinear relationships. Additionally, this study identified the evolution of nonlinear effects associated with key determinants over time. These findings advance the theoretical understanding of mobility mechanisms beyond linear assumptions and offer valuable insights for optimizing urban agglomeration structures and guiding mobility management policies.

The Green Transition, the shift to a climate‑neutral economy, has been a top priority on the European policy agenda in recent years. While its environmental and economic objectives are ambitious, its socio‑economic effects might be uneven across regions, shaped by structural weaknesses, institutional capacity, and geographic characteristics. This study aims to assess the socio-economic vulnerability of Greek regions to Green Transition policies by examining both their exposure to the transition’s impacts and their capacity to cope with emerging challenges and opportunities. Using a multidimensional and integrated approach, we develop three composite indices to capture exposure, adaptability, and overall socio-economic vulnerability. The findings reveal a clearly uneven spatial footprint of the Green Transition in Greece: while all regions are expected to be significantly affected, some face disproportionately higher vulnerability. This highlights the need for targeted interventions to ensure a fair and inclusive transition across all regions.

A comprehensive understanding of urban spatial form and functionality is essential for developing effective strategies and anticipating the impacts of planning decisions. Despite the growing academic interest in functional approaches, urban centers in planning practice are still predominantly defined by morphological attributes. This conceptual limitation is particularly evident in developing countries, where the implementation of polycentric development strategies requires a more integrated, multidimensional framework that incorporates both functional and morphological centrality. This study introduces a comprehensive framework for empirically assessing urban spatial structures by integrating morphological and functional dimensions through GIS-based spatial and temporal analysis. The primary objective is to examine polycentric urban patterns from the perspectives of static urban form and dynamic urban flows and to develop a composite Integrated Centrality Index that unifies these two dimensions. Using Ankara as a case study, the findings reveal significant spatial mismatches between morphological and functional centrality. While population density and the provision of basic services shape morphological centrality, they are insufficient predictors of functional performance. In contrast, areas offering specialized services and land use diversity demonstrate higher levels of human mobility and functional centrality. Furthermore, the study highlights that topo-geometric street features correlate strongly with vehicular traffic, whereas metric attributes, particularly betweenness index, are associated with public transport travel. By linking spatial form with functional flows, this research offers a comprehensive approach for understanding urban centrality. The results provide critical insights for planners and policymakers, underscoring the need for mobility-oriented, functionally diverse strategies that move beyond morphology-driven urban planning paradigms.

This study reconceptualizes guest satisfaction in peer-to-peer accommodation as a spatially embedded experiential outcome. Combining transformer-based NLP (Sentence-BERT, BERTopic) with multilevel and spatial econometric models, we analyze 109,940 Airbnb reviews from 1,092 Hong Kong listings (2011–2023) using strict temporal holdout validation. Experiential quality emerges as the dominant determinant of guest satisfaction, exhibiting a large unique explanatory contribution (partial R² ≈ 0.78), while amenities play a complementary role (partial R² ≈ 0.57). In contrast, economic factors such as price contribute only marginally once experiential and amenity dimensions are accounted for (partial R² ≈ 0.01). A Shapley R² decomposition corroborates this pattern, attributing approximately 70% of the total explained variance to experiential factors. Spatial Durbin Models further show significant spatial clustering and spillovers in satisfaction, with price effects becoming weak or non-significant after accounting for spatial dependence. Overall, the findings suggest that guest satisfaction in peer-to-peer accommodation markets is driven primarily by experiential quality rather than price competition, highlighting the importance of experience-oriented and place-sensitive strategies for platform governance and urban tourism policy.

Understanding spatial patterns among tourist attractions is critical for tourism planning and management. Social media review data offer valuable insights into spatial connections by capturing information on tourists’ perceived relationships and cognitive patterns. However, both the vast volume and unstructured nature of social media review data pose significant challenges for computational analysis and pattern identification. This study leverages the advanced natural language processing capabilities of large language models (LLMs) to systematically interpret social media reviews; combined with complex network theory and geographic information system (GIS) methodologies, it develops a comprehensive framework for analyzing spatial patterns among tourist attractions. By utilizing standardized prompts and structured output formats, LLMs can significantly improve the accuracy and scalability of social media review data analysis. The results of this study indicate that traditional urban historical landmarks remain central nodes in the tourism network, whereas newly developed urban areas are increasingly prominent as intermediate attractions. Analysis of the core-periphery hierarchical structure demonstrates that historical and cultural sites in the old city act as central nodes in the network, while newly developed urban areas are gradually seeing the emergence of intermediate attractions. These structural dynamics contribute to the strategic and efficient allocation of urban tourism resources. Community detection reveals variations in connections between different types of tourist attractions, providing essential support for differentiated approaches to cultural tourism development. The novelty of this study resides in the integration of LLMs with spatial analysis methods, which thereby offers a new approach to social perception analysis in tourism.

Substantial exhaust emissions from on-road vehicles threaten air quality in Hong Kong. This is particularly severe for pedestrians and tourists who are directly exposed to roadside pollution that can hardly be dispersed within deep street canyons. To mitigate this risk, a series of emission control strategies have been implemented in recent years, but their effectiveness in terms of tourist exposure reduction has yet to be assessed. In this study, we established a comprehensive framework that adapts both statistical and numerical modeling approaches to investigate the nitrogen oxides (NO_x) and fine particulate matter (PM_2.5) concentration reduction achieved by four emission control measures in the road transport sector in Hong Kong. The benefits of tourist exposure were then estimated through the tourist exposure assessment with geotagged social media data from TripAdvisor. Results demonstrate that the four measures together reduced annual territory-wide PM_2.5. and NOx concentrations by approximately 6% and 30%, respectively, at the ambient level. The improvements were more pronounced at roadside locations. Phasing out unqualified diesel commercial vehicles (DCV retirement program) was the most effective measure for air pollution mitigation, whereas the catalytic converter (CC) replacement scheme for liquefied petroleum gas (LPG) vehicles was most effective at minimizing tourist exposure to NO_x pollution. All measures show greater benefits in mitigating tourist exposure than resident exposure. These findings provide quantitative evidence that road traffic emission control measures can significantly reduce the health risks to millions of tourists visiting Hong Kong each year. The integrated framework bridges air quality science, machine learning, and tourism geography, offering a transferable methodology for evaluating emission reduction policies in other high-density Asian tourism destinations and supporting evidence-based urban planning for sustainable tourism development.

Amid accelerating urbanization and the rise of the “15-minute city” concept, spatial equity of neighborhood facilities is critical for sustainable governance. Existing studies often focus on single facility types and inter-group disparities, overlooking multi-facility inequalities, intra-group heterogeneity, and the social construction of accessibility. Taking Zhengzhou, a megacity in Central China, as a case study, this research integrates point of interest and resident survey data within the “15-minute city” framework. Urban network analysis, together with Lorenz curves and Gini coefficients, is applied to evaluate multi-dimensional inequalities across spatial regions, facility categories, social groups, and between objective accessibility and subjective satisfaction. Meanwhile, a social–spatial analytical framework is constructed, and the Geodetector method is employed to identify key determinants and their nonlinear interactions. Neighborhood facility accessibility follows a core–periphery pattern, with higher accessibility in the core than in the periphery. Objective inequalities generally exceed residents’ satisfaction, reflecting expectation gaps and adaptive behaviors. Intra-group disparities are notable: males, migrants, renters, and high-income groups show greater overall inequality; married residents face more unequal education access, while singles experience greater healthcare disparities, highlighting the limits of average indicators. Accessibility is primarily driven by the built environment and socio-cultural factors, with facility-specific variations: education by student numbers, recreation and commercial facilities by economic conditions, and transport facility by population distribution. Nonlinear interactions with socio-cultural factors further amplify these effects. The social–spatial interaction framework advances understanding of mechanisms shaping neighborhood facility equity and offers insights for more inclusive urban governance.

Equitable pedestrian access to everyday services is a key test of spatial justice, yet audits tied to administrative units routinely conceal localised shortfalls. This study maps regions of everyday activity (REA) in Czechia by assigning every dwelling with its nearest facility in basic amenities: education, primary healthcare, neighbourhood retail, public space and public transport, assigned into three analytic groups. Contiguous catchments emerge whose overlaps reveal opportunity-dense cores, while single-layer zones expose potential amenity deserts. The resulting national mosaic discloses a duality: multi-functional REA in metropolitan centres contrast with expansive single-group regions across borderlands and internal peripheries. Because this method relies solely on pedestrian street network distance, omitting capacities, opening hours and user preferences, it offers an indicative rather than exhaustive framework. Even so, REA delineation provides planners with a reproducible baseline for pinpointing mixed-use infill, service consolidation or multimodal links that could effectively shrink everyday geographies of disadvantage and guide subsequent qualitative enquiries.

China’s massive internal migration has profound implications for food security, particularly among migrant communities. Yet, it remains unclear whether and in what ways migrants’ place of origin, rural or urban, affects their household food security. This study analyzes how migration origin impacts household food security through path analysis based on survey data collected in Nanjing, China in 2022 and 2023. The study shows that the overall level of food security of migrant households was significantly lower than that of local households. The level of food security of rural-urban migrant households was lower than urban-urban migrant households and local households. We identified key determinants of household food security including income, formal or informal employment, length of residence, and outward remittances. Policy responses must explicitly recognize the structural disadvantages faced by rural-origin migrants and the associated disparities in food security linked to migrants’ origins, rather than treating migrants as a homogeneous group.

Since Airbnb’s launch in 2008, the for‑profit housing‑sharing economy has been linked to overtourism and worsening housing affordability worldwide. Against this backdrop, the paper at hand examines spatial links between tourism accommodation and housing affordability in Greece’s two largest cities, Athens and Thessaloniki, using detailed neighborhood‑level data on Airbnb listings, conventional hotels, and average residential rents and sale prices. Specifically, the paper first maps the short‑term rental market and the residential rental market separately, then examines their interlinkages through spatial analysis, and finally employs spatial and non‑spatial regressions to disentangle these relationships. Its findings highlight that the spatial patterns of these two markets differ profoundly between the two cities under study, yet consistently show a positive association between high densities of short-term rentals and higher residential rents. Going beyond the level of granularity of most prior studies, the findings document how this relationship varies in nature and intensity across parts of each city, while also emphasizing the role of nightly Airbnb prices. Finally, the analysis confirms a strong link between residential rents and sale prices which—alongside expected building and neighbourhood effects and established price‑to‑rent ratios—brings attention to the growing influence of buy‑to‑rent investment and housing policies. Overall, the paper nuances common conclusions of the relevant literature around short‑term rentals and residential uses, contributes to the sparse literature on these dynamics in Greece, and enriches debates on speculative real estate investment.