Population decline, particularly through aging, is reshaping demographic structures and placing increasing pressure on health and care systems. However, the mechanisms linking these changes to late-life health outcomes and the functioning of medical systems remain insufficiently understood. Here, we examine the dynamic interrelationships among population change, Alzheimer's mortality as a representative late-life outcome, and treatment- and caregiving-oriented medical infrastructure. We show that increases in population are followed by sustained declines in Alzheimer's mortality over the medium to long term, whereas depopulating regions exhibit persistently rising mortality despite expansions in long-term care facilities. In contrast, treatment-oriented medical infrastructure is consistently associated with lower mortality. These findings reveal an imbalance in which caregiving capacity expands where treatment capacity contracts. Policies prioritizing treatment-oriented systems, particularly early diagnosis and intervention, are critical for mitigating late-life mortality risks under sustained population decline.
Against the backdrop of global warming, the response of the ecologically sensitive and fragile alpine grasslands on the Tibetan Plateau to future climate change remains uncertain. Aboveground biomass (AGB) is a key indicator of ecosystem processes and supports the livelihoods of local herders. Therefore, this study established a multi-scale relationship of alpine grassland AGB across the "plot-landscape-region" scales, investigated the spatiotemporal distribution patterns of alpine grassland AGB and its driving factors, and identified future climate scenario risks for alpine grassland AGB using threshold analysis. The results indicated that the spatial distribution of alpine grassland AGB exhibited an "east-high, west-low" pattern, with aridity, precipitation, and elevation as the primary drivers (60.43%-62.30%). Alpine grassland AGB showed an increasing trend, covering 53.17% of the total area. Following the "importance-risk" zoning logic, the critical zones for optimising alpine grassland AGB were identified. Under the mid-term and long-term scenarios of SSP245 and SSP585, the AGB in the northeastern part of the Tibetan Plateau might be affected by high temperatures. The critical zone types were mainly "Important - TMP risk" and "Very important - TMP risk". Under the longer, higher carbon emissions scenario, there were more critical zones related to excessive precipitation in the southeastern part of the Tibetan Plateau. The AGB retrieval method could be applied to other regions, and the research findings could assist decision-makers in optimising resource allocation or implementing protection measures.
Commercial fishing vessels that disable their automatic identification system (AIS) transponders, commonly referred to as dark vessels, pose a growing challenge for maritime governance, environmental monitoring, and preventing illegal, unreported, and unregulated (IUU) fishing. AIS disabling is often a calculated behavior to evade detection while fishing illegally or engaging in unregulated exchanges with transshipment vessels. Such activity is especially prevalent in regions with limited enforcement capacity and is thought to occur along or inside exclusive economic zones, or territorial waters of nations. This study examines the spatial distribution and co-location of two vessel classes in West African waters: 1) dark vessels operating under flags of convenience, a recognized risk factor for IUU fishing, and (2) non-convenience-flagged dark vessels. Using open-access AIS data and vessel registry information from Global Fishing Watch, kernel density and Getis-Ord Gi* analyses show that convenience-flagged dark journeys differ from non-convenience-flagged ones, though Lee's L tests indicate spatial similarities, too. Convenience-flagged dark journeys were more likely to start or end outside or near maritime boundary edges, and this pattern was uneven across West African national maritime boundaries. Finally, repeated AIS disabling was highly concentrated: 11 convenience-flagged vessels accounted for 73 percent of dark journeys, and convenience-flagged vessels averaged nearly six dark journeys per vessel compared to just over one for non-convenience-flagged vessels. Interpreted through a crime-science lens, these findings highlight how opportunity structures and enforcement gaps shape illicit maritime activity. The study contributes a scalable, spatial approach to identifying and monitoring IUU fishing risk, offering practical insights for adaptive, time-aware maritime enforcement strategies.
Equitable spatial distribution of urban parks does not guarantee equitable realized usage, as place-based accessibility metrics may fail to capture whether residents actively visit and derive benefit from parks in their daily lives. Using Shenzhen, China as a case study, this study investigates the disparity between place-based accessibility and activity-based park usage in urbanizing villages versus market housing communities. Mobile phone signaling data was employed to identify community-level park visitation frequency, while a Gaussian-based 2SFCA method was applied to measure place-based park accessibility. A spatial nearest-neighbor matching strategy was further used to compare park usage between urbanizing villages and adjacent market housing communities under comparable locational conditions. To examine factors associated with observed usage disparities, an interpretable machine learning framework integrating XGBoost and SHapley Additive exPlanations (SHAP) was adopted. Our findings reveal that: (1) Despite comparable place-based park accessibility, urbanizing villages exhibit significantly lower activity-based park usage, a disparity persisting even across spatially proximate neighborhoods. (2) Lower park visitation in urbanizing villages was most strongly associated with differences in community-level socioeconomic composition. (3) Work-related conditions, including workplace stability and home–work proximity, were also associated with variations in community-level park visitation. These findings demonstrate that equitable spatial provision does not necessarily translate into equitable realized use and highlight the importance of incorporating actual usage patterns and community-level socioeconomic conditions into needs-oriented urban park planning.
Assessing ecosystem stability is increasingly important for understanding whether ecosystem service (ES) supply can remain reliable under intensifying climate variability and human disturbances. However, most regional stability assessments still rely primarily on vegetation dynamics, while the temporal stability of ES supply and its multidimensional stability mechanisms remain insufficiently quantified, especially in large-scale ecological restoration landscapes. Here, we quantified the temporal stability of ES supply from five ESs and combined it with vegetation-based resistance and resilience to construct a comprehensive ecosystem stability index (ESI). Using partial least squares structural equation modeling (PLS-SEM), we identified the key multi-pathway drivers of ecosystem stability, including vegetation recovery proxy (VRP), climate conditions, socioeconomic factors, soil and topographic constraints, and landscape pattern. Taking the Loess Plateau (LP) as a representative region during 2000–2020, we found that (1) ES supply was generally higher in the eastern and southern LP, whereas the temporal stability of ES supply showed a fragmented mosaic pattern. (2) Resistance peaked in the northwestern desert-grassland transition and loess tablelands, while resilience was higher in the eastern and southeastern cropland-dominated regions, resulting in a pronounced northwest-southeast contrast in ESI. (3) Climate-detrended enhanced vegetation index residual trends revealed stronger climate-adjusted vegetation recovery signals in the central and southern LP. (4) PLS-SEM showed that ESI was mainly supported by resistance and resilience, with resistance constrained by biophysical background conditions and resilience primarily associated with VRP and geographic context. These findings improve the assessment of ecosystem stability and inform region-specific restoration and disturbance management on the LP.
Urban renewal is increasingly implemented through heterogeneous micro-scale interventions, yet monitoring these changes and assessing their equity remains methodologically elusive. We introduce an expert knowledge-guided vision-language model (VLM) framework using 105,332 paired street view images from Shanghai (2017–2022) to detect, classify, and interpret renewal measures. Through domain knowledge injection and negative constraints, our model achieves high-precision classification across six substantive renewal types, revealing multi-dimensional renewal disparities. a) Allocation: Mantel tests show renewal configuration is more strongly associated with functional services and perceptual characteristics than with housing prices, with most renewal types concentrated in moderately served transitional zones rather than the most disadvantaged communities. b) In terms of measures, semantic analysis reveals qualitative measure inequity—high-value areas receive aesthetic interventions, while low-value areas receive pragmatic maintenance. c) Capitalization: exploratory hierarchical regression models show that renewal composition is associated with housing-price growth in both high- and low-price areas, and that the direction of the association differs by renewal type and price zone: Greenery & Landscaping is positively associated with housing-price growth in low-price areas but negatively in high-price areas, while Road & Traffic Infrastructure and Storefront & Signage are negatively associated with price growth in low-price areas. This study demonstrates VLMs can unveil justice issues beyond spatial allocation.
The paper is focused on small-scale beer production in Hungary. Through the insight in small firms' strategies in this highly competitive sector, we aim to get a more fine-grained understanding of how regime changes (post/socialism, post/Fordism) were interrelated, how it impacted the evolution of a niche industry, and how peripherality manifested in the production of a common drink which has been shifting form a mass-product category towards the realm of symbolic consumption in the past few decades. The analysis rests on a statistical analysis of microbreweries’ data obtained from online sources, interviews conducted with producers, and the triangulation of empirical results. The paper highlights the temporal delay and its structural characteristics of beer revolution in the CEE periphery, and explains, how spatially segmented consumption entailed geographical fault lines in business prospects and strategies of small firms in a niche sector.
Rapid urbanisation in the Greater Accra Metropolitan Area GAMA has profoundly transformed land use and land cover (LULC) patterns. Yet, existing studies often rely on volatile administrative boundaries that obscure intra-metropolitan dynamics. This paper addresses this gap by employing a fixed zonal framework (Zones A–E) to analyse LULC change from 2000 to 2024, using Landsat satellite imagery and spatial metrics, namely the Average Annual Urban Expansion Rate (AUER), Urban Expansion Intensity Index (UEII), and Urban Expansion Differentiation Index (UEDI). Findings show that built-up areas more than doubled (+149.72%), primarily at the expense of forest (−78.05%) and sparse vegetation (−22.58%). Urban growth shifted from a monocentric coastal expansion (2000–2013) to explosive inland sprawl (2013–2024), with Zones B (Northern) and E (Western) emerging as the dominant growth frontiers. Notably, the historic core (Zone D) exhibited negative expansion, signalling physical saturation. These trends confirm GAMA's transition to a polycentric urban region, but also expose critical misalignments with GAMA's 2040 Plan and Strategic Environmental Assessment. Our standpoints highlight the crucial importance of coordinated spatial governance, synchronized infrastructure in peripheral areas, and urban renewal in the city centre to foster equitable, resilient, and sustainable metropolitan growth in line with the Sustainable Development Goals.
Against the backdrop of global digital transformation and the rapid diffusion of intelligent technologies, artificial intelligence industrial agglomeration (AIIA) has become an important force reshaping regional innovation (RI) patterns. Yet systematic evidence on how emerging industrial agglomeration affects RI remains limited. Using panel data for 287 prefecture-level cities in China from 2011 to 2023, this study combines spatial analysis and econometric models to examine the impact of AIIA on RI and its mechanisms. The results show that RI level increased steadily, with a spatial pattern of coastal concentration and diffusion toward key inland nodes. AIIA also rose markedly, with high-level agglomeration areas concentrated in the eastern coastal region and gradually expanding outward. AIIA significantly promotes RI, and the result remains robust after multiple robustness checks. The effect is stronger in eastern regions, cities outside urban agglomerations, ordinary prefecture-level cities, and non-resource-based cities. Along the industrial chain, agglomeration across the upstream foundational, midstream technological, and downstream application layers promotes RI level, with stronger effects in the upstream and downstream layers. Mechanism tests show that human capital and digital economy development act as positive transmission channels. Government action and urban green space strengthen the innovation effect of AIIA, whereas innovation network embeddedness weakens its marginal effect. Threshold and spatial econometric results further indicate that AIIA has a critical-scale feature and significant positive spatial spillover effects. This study deepens the understanding of AIIA's role in shaping RI level and provides empirical evidence for optimizing artificial intelligence industrial layouts and enhancing regional innovation capacity.
Urban green spaces (UGS) support health and urban resilience, yet realized access depends on both proximity and travel options, especially in transit-oriented cities. Older adults often face higher impedance from slower walking, transfer sensitivity, and first- and last-mile burdens, so walking-only or age-neutral measures may understate inequalities when trips require multimodal travel. This study assesses UGS accessibility in Hong Kong using an age-stratified Gaussian two-step floating catchment area framework (AS-G2SFCA) in the time domain. We model walking, Mass Transit Railway (MTR), and bus travel for younger adults aged 15–64 and older adults aged 65+, distinguish urban parks from country parks, and summarize distributional inequality using a disadvantage-weighted Gini while identifying low-access clusters via local indicators of spatial association. Results show that urban-park accessibility is consistently higher than country-park accessibility, with larger cohort differences for destination-oriented country parks than for neighbourhood-oriented urban parks. Within the modelling framework, incorporating public transport is associated with higher accessibility for both cohorts and lower disadvantage-weighted inequality, with weighted Gini coefficients decreasing from 0.42 (younger) and 0.46 (older) under walking-only conditions to 0.16 and 0.14, respectively, under multimodal access. Bus-based accessibility surfaces are more spatially even than MTR-based surfaces, while accessibility exhibits strong spatial clustering, with global Moran’s I values of 0.96 and 0.95 for younger and older adults, respectively. Neighbourhood disadvantage is weakly associated with accessibility at the street-block scale, highlighting mode-specific transit coverage and first- and last-mile conditions as key levers to narrow age-related inequities in green access.
Rapid inter-day temperature fluctuations pose increasing risks to coupled human–environment systems, yet the spatiotemporal dynamics of public sensitivity to such variability remain poorly quantified. This study develops a geospatial–computational framework integrating deep learning-based social sensing with explainable machine learning. Analyzing approximately 1.9 million geotagged temperature-related posts from 367 Chinese cities across three representative years (2017, 2020, and 2022), we constructed a city-scale sensitivity index to measure residents' responsiveness to thermal shocks. Results reveal a fundamental asymmetry, with perception sensitivity to cooling (0.114%/°C) being 2.3 times higher than to warming (0.049%/°C) on average, though this gap narrows under extreme temperatures (peaking at 0.286%/°C and 0.296%/°C, respectively). Sensitivity shows marked spatial heterogeneity and regional clustering, with high-sensitivity hotspots concentrated in southwestern China and low-sensitivity coldspots in northwestern and northeastern regions. Furthermore, factor analysis reveals that climatic baselines (e.g., inter-day temperature variability, precipitation), environmental characteristics (e.g., elevation), and socioeconomic factors (e.g., population density) significantly modulate perception sensitivity. These findings highlight the urban amplification effect and validate social media as a real-time sensor for human thermal stress. Our approach supports spatially targeted adaptation planning and the integration of subjective perception into urban early warning systems.
With the rapid expansion of online food delivery services (OFDS), their role as digital access channels to healthy food has become increasingly important. However, limited evidence exists on whether the mechanisms shaping healthy OFDS use differ across intra-urban spatial contexts. Taking Nanjing as a case study, this study uses survey data and binary logit models to compare residents in the main urban area and peripheral areas. The analytical framework incorporates individual socioeconomic attributes, healthy eating attitudes, perceptions of healthy OFDS, travel attitudes, and built environment conditions. The results reveal significant spatial heterogeneity. In the main urban area, high-frequency healthy OFDS use is mainly associated with individual-level efficiency-oriented behavior. Travel minimization has the largest positive effect (AME = +10.1%), whereas perceived travel quality and travel satisfaction are negatively associated with usage frequency (AMEs = −4.8% and −5.4%). Perceived affordability of healthy OFDS and online healthy food facilities density also show positive effects (AMEs = +6.1% and +7.2%). In peripheral areas, healthy OFDS use is more strongly related to individual digital level and built environment conditions. Average daily Internet use (AME = +7.1%) and offline healthy food facilities density (AME = +6.4%) show positive effects, while online healthy food facilities density shows a negative effect (AME = −4.7%). Household shopping responsibility and perceived accessibility of healthy OFDS are consistently positive across both areas. This study extends the understanding of residents’ healthy OFDS usage behavior and provides empirical evidence for designing spatially differentiated healthy food provision policies in the context of digitalization.
The relationship between catchment areas of origin and destination stations (CODS) and urban rail transit (URT) ridership is essential for improving the integration of land use and sustainable transportation. The CODS based on the spatial interaction theory can be viewed as a unified pair whose differences create the supply-demand relationship (e.g., residential and commercial areas) to influence URT ridership. However, current studies have paid limited attention to how differences between CODS influence URT ridership. To address this gap, we first propose three innovative indicators—absolute, directional, and basic differences—for measuring key variations (e.g., land use, transport, and population density) between CODS. We then take Guangzhou (China) as a case study. With multi-source data (e.g., smart card transactions, points of interest, and official documents), we employ a Multilevel Negative Binomial Regression (MNBR) model to examine how differences between CODS and their interactions influence URT ridership. Our results show that the proposed indicators meaningfully capture differences between CODS. Differences in residential, commercial, and public service land areas are positively associated with URT ridership. The positive interaction between accommodation and office POI-density differences further suggests a joint effect of specific functional differences. These findings support the effectiveness of our innovative indicators and underscore the important role of differences between CODS on URT ridership. Our study contributes to improving the understanding of the built environment around stations in relation to URT ridership and to developing more effective land use-transportation integration strategies.
Longer farming distances often trigger cropland abandonment, reducing utilization stability. Although China's cropland requisition-compensation balance (CRCB) policy aims to stabilize cropland use, its impact on farming distances remains unclear. This study evaluates the balance status of cropland quantity and slope before and after the expansion of the CRCB scope. Utilizing DEM data to measure three-dimensional farming distances, it then investigates the differences in farming distance changes between balanced and unbalanced zones. Ultimately, it identifies six evolutionary pathways of farming distance. The results show that China has consistently maintained the quantity balance under small-scale requisition-compensation scope, whereas the quantity balance under large-scale scope was only achieved during 2010-2015. The slope of cropland occupied for construction was significantly lower than that of the compensated cropland, with a more pronounced slope gap in mountainous regions. Significant differences were observed between balanced and unbalanced zones. Under the small-scale requisition-compensation scope, compared to unbalanced zones, quantity-balanced zones exhibited a significant decrease in farming distance, whereas this decrease was inhibited in slope-balanced zones. At the large-scale scope, quantity-balanced zones similarly demonstrated a significant decrease in farming distance outside the 2010-2015 period, while slope-balanced zones also shifted toward a significant decrease. Given the evident regional heterogeneity in these impacts, this study identifies six evolutionary pathways of farming distance and proposes corresponding strategies to enhance the stability of cropland utilization.
Estimating physical disorder and associated streetscape perceptions in the urban built environment is critical for understanding and preventing urban crime. While prior research has addressed this topic, few studies have systematically quantified microscale disorder features and streetscape perceptions simultaneously to examine their relative contributions to crime density. This study adopts a highly detailed approach to urban streets, constructing a comprehensive framework of physical disorder characteristics using semantic segmentation models and street view images in New York City. We utilize spatially augmented extreme gradient boosting models to examine the complex relationships among physical disorder, streetscape perception, and both property and violent crime density. Results reveal that physical disorder and subjective streetscape perceptions are significantly associated with crime density and exhibit modest incremental predictive complementarity. Visible physical disorder cues remain the most prominent predictors for both property and violent crime density, with severe signals like structural failure and graffiti consistently ranking near the top. Furthermore, streetscape perceptions provide additional valuable information specific to different crime types. Perceptual dimensions contribute more visibly to property crime explanations, while their relative importance is smaller for violent crime. Nonlinear analyses further indicate that visible disorder serves as an early warning signal of neglect, showing a rapid initial increase in predicted crime density before plateauing. These empirical findings deepen environmental criminology theories by demonstrating that different disorder types and subjective perceptions correspond to distinct crime patterns, thereby providing actionable insights for targeted urban governance, spatial planning, and policy considerations.
