Search Results (1,509)

Equitable access to dental care across Germany remains constrained by geographic barriers, yet traditional provider-to-population ratios fail to capture spatial complexity. This study employed a Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method to quantify dental care accessibility at the ZIP-code level across all 8171 German postal code zones, integrating 2022 Census demand data, 38,023 dental facilities, and network-based travel times. Results revealed stark geographic disparities: accessibility exhibited strong positive spatial autocorrelation (Moran’s I = 0.97, p < 0.001), with 22.6% of the population in high-access urban clusters concentrated in Berlin, Hamburg, and the Rhine-Ruhr region, while statistically significant cold spots affected only a small share of the national population but formed geographically persistent clusters in rural and peripheral regions. The Gini coefficient of 0.55 indicated moderate overall inequality, substantially higher than for physician distribution (Gini = 0.19). Regional heterogeneity was prominent: Mecklenburg-Western Pomerania had 74.9% of its population in the lowest accessibility quartile (mean index = 0.7), while Berlin achieved near-universal high access (mean = 15.3). The MH3SFCA framework provides German planning bodies with a dynamic tool for routine accessibility monitoring and scenario analysis, supporting targeted, place-based workforce interventions that move beyond static mapping toward more equitable distribution of dental care. Full article

Rural men in Australia experience poorer health outcomes compared to their urban counterparts, often due to barriers such as limited access to services, cultural norms around masculinity, and geographic isolation. This study explored the motivations underlying rural men’s participation in a student-led health promotion activity conducted by first-year paramedicine students at a large agricultural event. Using semi-structured verbal surveys with 60 participants, qualitative data were collected and analysed through an inductive content analysis approach. Three key themes were created to represent the reasons men engaged in the activity and the impact it had: factors that encouraged engagement and built trust, meeting multiple needs, and giving to student learning for the future. Participants were drawn to the activity by a welcoming environment, peer encouragement, and the consistent presence of health promotion at the event. Health promotion provided an opportunity for men to access basic health assessments, reflect on their wellbeing, and engage in conversations about physical and mental health in a supportive setting. Many participants also expressed a strong motivation to contribute to student learning, particularly through experiential interactions that allowed students to develop technical and interpersonal skills. The findings highlight the value of embedding health promotion activities in familiar, community-based settings and demonstrate the multitude of benefits for rural men. This research contributes to the growing evidence supporting innovative, community-engaged models of work-integrated learning that address health disparities while enhancing student education and rural workforce development. Full article

This study aims to identify the key destination-related factors associated with tourists’ revisit behavior in peri-urban rural areas within Chinese metropolitan regions in the context of short-video marketing, and to reveal the structural relationships and hierarchical characteristics among these factors. First, the LDA topic model was employed to conduct text mining on authentic tourist-generated comments posted on Douyin, through which the core factors related to revisit behavior were identified and conceptually standardized based on tourists’ expressions. Building on this process, 153 experts in relevant fields were invited to evaluate the direction and strength of the relationships among these factors. An integrated DEMATEL–ISM–MICMAC approach was then applied to analyze their causal attributes, hierarchical structure, and systemic roles. The results indicate that the identified factors do not operate independently but instead form a multilayered structure with clear hierarchical characteristics. Among them, rural visual imagery, escape-oriented experience, rural lifestyle experience, and rural industry integration occupy deeper structural levels and exert relatively strong structural influences on factors located at intermediate and surface levels. The findings further suggest that the sustained attractiveness of rural tourism destinations in metropolitan regions cannot rely solely on short-video exposure or isolated “internet-famous” attractions; rather, it requires coordinated alignment among digital communication content, rural industries, lifestyle experiences, and tourism supply. By integrating tourist-generated content, natural language processing, and expert-based structural assessment, this study extends research on short-video tourism marketing from a systems perspective and provides practical insights for peri-urban rural destinations in Chinese metropolitan regions seeking to optimize the structural configuration of tourism resources, products and services, and marketing communication. Full article

Global commodity chains reshape rural territories through interconnected environmental and socioeconomic processes; these dynamics are often examined through the lens of land-use change. Export-oriented agricultural frontiers illustrate how global market integration can generate landscape and societal transformations. In western Mexico, the expansion of the avocado industry exemplifies commodity-driven territorial change, linking land-use dynamics with transformations in rural economies and livelihoods. This study analyzes how export integration into the international avocado commodity chain was associated with transformations in land systems and socioeconomic dynamics across four municipalities in Michoacán, Mexico, between 2003 and 2023. Using high-resolution land-use maps alongside demographic and socioeconomic indicators, we apply a qualitative, comparative and inductive synthesis of spatial and census data to examine how commodity integration interacted with local conditions to shape landscape and territorial organization. Avocado expansion coincided with territorial transformation, including the conversion of pine-oak forest and traditional agricultural lands, as well as changes in labor arrangements, demographic dynamics, and rural livelihood systems. Land-use analysis shows continued orchard expansion and accelerated land conversion after 2015. Socioeconomic indicators reveal population growth, increased female employment, and persistent income inequality, highlighting the uneven incorporation of rural territories into global commodity chains. We identified four territorial pathways: agricultural specialization, agro-industrial urbanization, transitional diversification, and rural commodity integration. These findings contribute to debates on uneven geographical development and commodity frontier expansion by showing how global commodity integration intersects with ecological, economic, and social processes to generate distinct pathways. Full article

Accommodation establishments constitute a core component of tourism infrastructure, and their location choices directly affect water resource utilization, land pressure, and the spatial equilibrium of tourism development—issues that are particularly acute in vast arid regions. Yet the spatial organization of accommodation supply across extensive drylands characterized by fragmented oasis distribution, and the reasons why standard and non-standard accommodation follow divergent location logics, remain poorly understood. This study addresses three questions: (1) How are nine accommodation categories, differentiated by type and quality, distributed across Xinjiang? (2) Do directional spatial associations exist among categories that are consistent with hierarchical, path-dependent development? (3) Which factors drive these patterns, and do their effects exhibit the nonlinearity and threshold behavior predicted by location theory? Drawing on 12,073 accommodation establishments from the Ctrip platform, we construct a staged analytical framework in which each technique answers a specific question: the nearest-neighbor index and standard deviational ellipse characterize global patterns; kernel density estimation and OPTICS clustering identify local agglomerations; directional local co-location quotients measure asymmetric spatial associations; and XGBoost–SHAP isolates nonlinear drivers and threshold effects. Results reveal a highly concentrated “single-core, multi-center” structure anchored by Urumqi, Yining, and Kashgar, with rapid expansion toward the Ili Valley, Kashgar, and Altay since 2019. Standard accommodation tracks urban centrality and transport nodes, while non-standard accommodation tracks tourism resource endowments, consistent with location-theoretic expectations. Directional co-location analysis reveals hierarchical spatial associations among categories, and driving factors exhibit pronounced nonlinear threshold effects. From a sustainability perspective, the identified thresholds—elevation (1360 m), water-body proximity, and distance to rural tourism demonstration sites (3 km)—constitute quantifiable, spatially explicit sustainability indicators that can be incorporated into planning tools to monitor and steer accommodation development away from ecologically sensitive zones. Global Moran’s I diagnostics of model residuals (reduction of 83–99.7%) suggest that these findings are unlikely to be artifacts of spatial autocorrelation; this diagnostic, however, complements rather than replaces spatially blocked validation. The study contributes category-differentiated, spatially directed evidence for policies balancing tourism expansion against water security and ecosystem integrity, serving sustainable tourism development in arid-region destinations. Full article

Rapid urbanization has led to habitat fragmentation in peri-urban areas, continuously eroding the ecological foundation of sustainable food systems in urban–rural transition zones and posing a real threat to regional food security. Against the backdrop of urbanization disturbances, traditional nature-based solutions have limitations in addressing socioecological nonlinear responses, whereas digital tools offer new governance pathways for enhancing food system resilience. To elucidate the intrinsic mechanisms through which digital technology empowers the resilience of peri-urban food systems, this study, which is grounded in ecological wisdom theory, constructs a system dynamics model that integrates “digital technology-ecological perception-ecological wisdom capital” in a three-dimensional linkage. This model simulates the dynamic process through which sustainable food systems in urban–rural transition zones resist the risks of habitat fragmentation and achieve synergistic steady-state evolution. According to the simulation results, a synthesized steady-state transition in sustainable food systems can be regarded as a self-organizing phase transition process. During resource metabolism, system elements show strong nonlinear symbiotic and mutually beneficial features. Further, there is a significant time-lag effect on improving food system resilience through digital technology empowerment and policy coordination. Also, the effects of governance are not immediately visible. Further, as an important instrumental empowerment carrier, urban–rural spatial and information barriers can be broken through means like digital ecological monitoring. Moderate investment in this regard can promote the acceleration of the system’s self-organizing phase transition. Also, this can enhance resilience against disturbance from habitat fragmentation while ensuring food production and supply. Finally, the ecological carrying capacity of core food production spaces does not increase monotonically. This means that the system possesses an adaptive cyclical fluctuation mechanism, with a periodic oscillatory evolution of carrying capacity. This study breaks through static analytical paradigms, fills the quantitative research gap on the resilience evolution of peri-urban food systems driven by the integration of digital technology and ecological wisdom, and can provide scientific evidence and decision-making support for food–ecological collaborative governance in China’s urban–rural transition zones. Full article

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article

The deep integration of digital technologies with agricultural production is reshaping how rural land is used and offers new opportunities for narrowing the urban–rural gap in land use efficiency. Building on theoretical analysis, this study uses panel data for 700 Chinese counties from 2010 to 2024, treats the Digital Agriculture Innovation and Application Base pilot program as a quasi-natural experiment, and applies a double machine learning model to examine the effect of smart agriculture construction on the urban–rural gap in land use efficiency. The results show the following: (1) Smart agriculture construction significantly narrows the urban–rural gap in land use efficiency, with channel evidence pointing to agricultural technological progress, improved capital allocation, and agricultural industrial upgrading. (2) The policy effect is statistically significant only in formerly poverty-stricken counties, counties with higher land-transfer rates, and counties with stronger digital foundations, while Fisher permutation tests do not establish statistically significant cross-group differences. (3) Decomposition results show that the program raises rural land use efficiency and the rural marginal returns to capital and labor while leaving the corresponding urban indicators statistically unchanged, so the observed convergence is driven primarily by rural catch-up rather than urban decline. From a factor allocation perspective, this study clarifies how smart agriculture construction can rebalance urban–rural land resource use while identifying the statistical limits of the heterogeneity and channel evidence. Full article

The global shift toward an aging population presents significant challenges for healthcare systems. This is compounded by rising disability rates, fragmented care models that struggle to meet complex needs, and a digital divide caused by the emergence of smart technologies. This work aims to evaluate how the digital divide affects the adoption, usability, and perceived benefits of smart home technologies designed to support activities of daily living and health monitoring. The influences of socioeconomic status and geographic location (urban versus rural) on the digital divide are considered. Four databases (Web of Science™, Scopus^®, PubMed^®, and IEEE Xplore^®) were searched between 2014 and 2026, resulting in 71 studies that examined older adults, smart home technologies for daily living or health monitoring, and factors related to the digital divide. Findings were synthesized using the Technology Acceptance Model, Van Dijk’s Digital Divide Framework, and Health Behavior Models. Three key barriers were identified—economic, technical, and social—which disproportionately affected vulnerable groups. Smart Home Health Technologies (SH2Techs) present adoption challenges distinct from standalone devices because they require integrated infrastructure and sustained engagement. Limited research addressing the usability of non-clinical SH2Techs has identified the need for co-design, simplified interfaces, targeted training, and policy reforms to support equitable aging in place. Full article

Macroplastic pollution in rivers is an increasing environmental concern because rivers function simultaneously as active transport pathways and temporary storage compartments for land-based plastic waste. This study investigated the spatial distribution, composition, and size characteristics of riverbank and floating macroplastic debris in the Can Tho River, a tidal tributary of the Hau River in the Mekong Delta, Vietnam, to improve understanding of macroplastic transport, selective retention, and environmental partitioning between active transport and temporary storage compartments. Riverbank debris was surveyed at twelve sites spanning urban, peri-urban, and rural sections, while floating debris was quantified using a net-based sampling system. Riverbank accumulations exhibited pronounced local spatial heterogeneity, although litter density and mass density did not differ significantly among river sections. Plastics dominated both environmental compartments, accounting for 53–60% of accumulated debris and more than 95% of floating debris by abundance. Riverbank accumulations were dominated by plastic bags, food packaging, and beverage containers, whereas floating debris was dominated by expanded polystyrene foam products. Significant differences were also observed in material composition, plastic-product composition, and size distribution. Riverbank accumulations contained proportionally larger macroplastics (100–500 mm), whereas floating debris was dominated by smaller macroplastics (50–200 mm), supporting the role of size-dependent transport and selective retention in environmental partitioning. These findings show that floating debris and riverbank accumulations represent complementary components of the riverine plastic continuum, linking active transport and temporary storage through selective environmental partitioning. Integrating floating and riverbank monitoring provides a more comprehensive framework for understanding macroplastic transport and environmental fate while informing management strategies to reduce downstream plastic transport to the Hau River and ultimately estuarine and coastal ecosystems. Full article

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation. Full article

Urban and peri-urban regions increasingly face climate-related pressures, competing land-use demands, and the need to expand renewable-energy infrastructure while maintaining agricultural land and landscape functions. Agrivoltaics, which combines photovoltaic energy generation with agricultural production, represents a potentially multifunctional approach to land use; however, regional GIS assessments often stop at environmental suitability surfaces without translating those results into planning-relevant cadastral inventories. This study develops and applies a planning-oriented Geographic Information System (GIS) framework for preliminary statewide agrivoltaic screening in Connecticut. Annual global solar radiation and terrain slope were integrated through a weighted suitability model, while incompatible land-cover classes were treated as hard exclusions through a binary land-cover mask. The workflow subsequently excluded protected and open-space lands, associated suitable areas with cadastral parcels, normalized and dissolved parcel identifiers using ParcelKey, and a recalculated suitable area from the resulting unique parcel geometries and then applied a minimum requirement of 1 ha of cumulative suitable area per retained parcel. The final baseline inventory contained 3497 normalized unique cadastral parcels encompassing 16,366.49 ha of GIS-identified suitable area, with suitable land representing an average of 42.46% of total parcel area. Peri-urban contexts accounted for the largest share of the final suitable area, containing 2497 parcels and 73.16% of the total, compared with 476 urban and 524 rural parcels. Sensitivity analysis indicated strong stability under alternative weighting schemes, with spatial overlap exceeding 99% relative to the baseline. Reducing the suitability-score threshold from 3.0 to 2.5 produced only minor changes, whereas increasing it to 3.5 reduced the inventory to 3095 parcels and 13,712.89 ha. From a sustainability perspective, the framework provides a spatial decision-support approach for coordinating renewable-energy planning with agricultural land stewardship, conservation constraints, and more efficient use of already fragmented land resources. By making the effects of exclusions, parcel thresholds, and analytical assumptions explicit, the approach supports more transparent and reproducible evaluation of land-use trade-offs relevant to sustainable development. The resulting inventory is intended as a first-stage planning resource rather than a determination of project feasibility or site-level sustainability performance. Full article

Disamenity and amenity facilities pull property values in opposite directions, yet prior studies have treated disamenities as a homogeneous category and have been constrained by data scattered across agencies and by mismatched spatial scales. This study integrates multi-source spatial data, comprising 3426 sub-districts (eup-myeon-dong) nationwide, a 500 m population grid of 376,668 cells, and the actual point locations of individual facilities, into a single spatial framework and applies a multi-method design that combines a spatial Durbin model (SDM), explainable machine learning (XGBoost–SHAP), an environmental-justice analysis, and a micro-siting analysis. Four findings emerge. First, not all disamenities are associated with lower prices: only pollution-emitting facilities with a clear emission signature, namely, sewage treatment plants and incinerators, capitalize robustly as cross-sectional associations, into both housing prices and officially assessed land values, with the effect extending into neighboring sub-districts rather than staying purely local. The other disamenities show no robust total effect on either price; their estimates are small or unstable across specifications. Second, capitalization of pollution-emitting facilities is pronounced in the urban sample and disappears in the rural one, and the coefficient structure differs overall between urban and rural areas (global Chow test, p < 10⁻²¹). Third, micro-siting analysis shows that where a facility is placed (siting) and what it leaves behind in prices (capitalization) are distinct layers, and that capitalization tracks the population a facility keeps nearby rather than its nuisance type: pollution-emitting facilities retain enough nearby housing for their burden to register, whereas funerary facilities sited far from people do not. Fourth, the environmental inequity of pollution exposure runs along income rather than age and concentrates in cities rather than in rural areas: the exposure rate among residents of low-income urban sub-districts (49%) is 2.5 times that of the urban population as a whole (19%), while the greater Seoul metropolitan area remains comparatively insulated. These layered findings were possible only once the dispersed spatial data had been integrated and standardized, and the study demonstrates that fusing and efficiently managing spatial data is a precondition for evidence-based land-use and environmental policy. Full article

Background: Limited access to rapid molecular diagnostics remains a major barrier to timely tuberculosis (TB) detection and drug-resistance surveillance in resource-constrained settings. In the Republic of Congo, GeneXpert MTB/RIF platforms remain concentrated in a few laboratories, while peripheral facilities rely predominantly on smear microscopy, highlighting the need for a structured sample referral system. Methods: We conducted a three-year mixed-methods operational study comprising a six-month pilot phase (January–June 2023), initially enrolling 29 TB diagnostic and treatment centers in urban and rural settings followed by a 30-month national roll-out phase (July 2023–December 2025), evaluated retrospectively using routine programmatic surveillance data. Results: During the pilot phase, 962 sputum samples were transported. 294 (30.6%) were TB cases, including 12 (4.1%) RR-TB. Median total turnaround time was 53 h (19–168) in urban and 62 h (26–204) in rural, with 86% overall site participation (92% urban vs. 60% rural). During scale-up, samples increased from 2154 (2023) to 5160 (2024), achieving full national coverage (12/12 departments). Over three years, 2587 TB and 84 RR-TB cases were detected. The system’s contribution to national TB detection grew from 9.2% to 16.0%, and RR-TB detection from 10.4% to 18.7%. Conclusions: Implementation of the structured sample referral system was associated with improved equitable access to molecular TB diagnosis, turnaround times within national targets, and strengthened the RR-TB surveillance. Scalable transport networks integrated into national guidelines may enhance diagnostic equity and drug-resistance detection in centralized laboratory systems. Full article

This paper investigates the use of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to enhance fifth generation (5G) vehicular communications on highways, where traffic congestion and fluctuating user demand can challenge the capacity of terrestrial infrastructure. While UAV-assisted vehicular networking has attracted significant attention, many existing studies rely on simplified mobility, propagation, or communication models that limit the assessment of practical deployment performance. To address these limitations, we develop a realistic UAV-assisted vehicular networking framework that integrates microscopic traffic simulation through Simulation of Urban MObility (SUMO), network control via Traffic Control Interface (TraCI), and standard-compliant 5G communication modeling using MATLAB R2025b 5G Toolbox. The framework incorporates a 3rd Generation Partnership Project (3GPP) rural macro cell (RMa) highway scenario, detailed clustered delay line (CDL)-based channel characterization, and cross-layer communication procedures. Within this framework, we propose a low-complexity trajectory optimization strategy that adapts the UAV position in real time to maximize the average received signal to noise ratio (SNR) while respecting practical motion constraints. Simulation results demonstrate that adaptive UAV positioning enhances communication performance, achieving mean SNR gains of up to 2.04 dB, throughput improvement of up to 11.2%, and block error rate (BLER) reductions of up to 27.3%. These findings highlight the potential of UAV-assisted communications to enhance user-perceived quality of service (QoS) for bandwidth-demanding vehicular applications under realistic 5G highway operating conditions. Full article