Search Results (2,844)

Regional epidemic risk prediction requires holistic modeling of heterogeneous data sources such as demographic structure, health capacity, geographical features and human mobility. In this study, a unique and multi-modal epidemiological data set integrating demographic, health, geographic and mobility indicators of Türkiye and its neighboring countries was collected. Türkiye’s neighboring countries are Greece, Bulgaria, Georgia, Armenia, Iran, and Iraq. This dataset, created by combining raw data from these neighboring countries, provides a comprehensive regional representation that allows for both quantitative classification and spatial mapping of epidemiological risk. To address the class imbalance problem, Conditional GAN (CGAN), a class-conditional synthetic example generation approach that enhances high-risk category representation was used. In this study, we proposed a multi-view deep learning model named MV-RiskNet, which effectively models the multi-dimensional data structure by processing each view into independent subnetworks and integrating the representations with an attention-based fusion mechanism for regional epidemic risk prediction. Experimental studies were compared using Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder classifier, and Graph Convolutional Network (GCN) models. The proposed MV-RiskNet with CGAN model achieved better results compared to other models, with 97.22% accuracy and 97.40% F1-score. The generated risk maps reveal regional clustering patterns in a spatially consistent manner, while attention analyses show that demographic and geographic features are the dominant determinants, while mobility plays a complementary role, especially in high-risk regions. Full article

As global decarbonization strategies pivot towards the burgeoning sector of recreational mobility, the tension between the attractive force of high-quality amenities and the constraining capacity of transport infrastructure in urban parks has emerged as a critical planning dilemma for high-density metropolises. To disentangle this game mechanism, this study proposes a integrated Dual-Diagnostic Framework that synthesizes a modified gravity model, Grouped OLS regression, and an explainable XGBoost-SHAP algorithm to identify non-linear thresholds under spatial heterogeneity. Leveraging empirical data from Tianjin, a representative high-density metropolis, the analysis reveals a distinct bimodal distribution of carbon emissions from travel to comprehensive parks, confirming a fundamental structural divergence between urban and suburban mobility patterns. Crucially, the non-linear diagnosis uncovers a dominant Facility Configuration Induction mechanism within the suburban interface; here, park scale acts as the primary driver of excess travel, with its induction effect often overriding the mitigation potential of public transit until a specific critical mass is achieved. Consequently, the results identify a rigid threshold for bus station density alongside optimal intervals for park scale, providing quantitative benchmarks and differentiated governance strategies to resolve the paradox between park quality and carbon intensity. Full article

This study investigates the influence of public transport on the quality of urban life through a combined approach that includes both an objective and a subjective assessment. The objective quality of the public transport network in Timișoara was measured using the Public Transport Accessibility Level (PTAL) index, whose values were recalibrated to better fit the context of an Eastern European post-communist city, while citizens’ perceptions were analyzed based on a public opinion survey in Timișoara, conducted over 5 years on 9490 respondents. The research methods used combine cartography and statistics, with tools such as ArcGIS Pro, IBM SPSS Statistics v27, and R v4.5.2. The results highlight a correlation between accessibility levels and user satisfaction, emphasizing spatial disparities between the city center, which enjoys excellent accessibility, and the periphery, where accessibility is much lower. The integration of these two dimensions provides a holistic perspective on urban mobility and makes relevant contributions to sustainable planning strategies and the development of smart city initiatives. Full article

Rural shrinkage is increasingly expressed through changing residential mobility, housing under occupancy, and intermittent dwelling use, rather than a simple linear process of permanent outmigration and abandonment. Yet empirical measurement of occupancy dynamics and the service-mediated mechanisms shaping residence stability remains limited. This study proposes an evidence-driven and explainable assessment framework that links energy-informed occupancy dynamics with settlement building area and mechanism identification, using Fuyuan City as a case study. Daily electricity consumption time series from 2021 to 2024 are used to infer occupancy dynamics and detect behavioral signatures of long term residence, seasonal residence, return visits, and vacancy. Shape-based temporal clustering identifies six occupancy trajectories, revealing pronounced heterogeneity in mobility rhythms within the rural settlement system. Settlement vacancy-related built-environment changes are characterized from 2 m remote sensing imagery, using a trained YOLO-based building detection workflow, producing settlement-level total building area as a physical indicator of the development intensity. Integrating these behavioral measures with multi-source spatial factors, the mechanism model shows that development, governance, and environmental conditions influence residence stability primarily through service provision. Among service domains, education services exhibit the strongest direct association with long-term residence stability, while transport and daily life services show modest positive effects and healthcare presents a smaller positive effect. Development conditions positively promote all service types, whereas governance and environmental context display differentiated and, in some pathways, opposing effects across services. Overall, the framework enables interpretable monitoring of rural shrinkage dynamics by jointly quantifying occupancy trajectories, settlement morphology, and service-mediated pathways shaping residential outcomes. Full article

Total factor productivity (TFP) serves as the primary driver of high-quality development and a key determinant for the sustainable growth of Japan’s beef cattle industry. This study analyzes panel data from nine agricultural regions in Japan, covering the period from 2004 to 2022, and applies the Malmquist-Luenberger index model to measure and decompose TFP in the sector. It utilizes various methods, including the Dagum Gini coefficient, kernel density estimation, and Markov chains, to examine regional disparities and dynamic changes. Additionally, the study applies the geographic detector and spatial Durbin model to explore the spatiotemporal evolution and influencing factors. The results show that: (1) From 2004 to 2022, TFP in Japan’s beef cattle industry steadily declined, accompanied by growing regional imbalances. The Tokai region was the only area to experience positive TFP growth, while other regions generally saw declines. (2) The spatial disparity in TFP growth has increased, with an intensified imbalance and a widening gap between regions. TFP distribution is becoming more “multipolar,” with considerable dynamic mobility. (3) TFP exhibits a general positive spatial correlation. Geographic detector analysis reveals that factors such as the number of agricultural research and development personnel, fiscal support, industrial agglomeration, feed production capacity, and labor productivity are the key drivers behind spatial TFP differentiation, reflecting a complex interplay of multidimensional factors. (4) Industrial agglomeration, fiscal support, and the number of agricultural R&D personnel exhibit significant spatial positive spillover effects, indicating that coordinated regional progress is essential for fostering the sustainable and healthy development of the beef cattle industry. This study provides theoretical and empirical support for the sustainable development of Japan’s beef cattle industry and offers policy recommendations to enhance the economic growth quality of the beef cattle industries in both Japan and China. Full article

Real-time semantic segmentation has been widely adopted in resource-constrained applications such as mobile devices, autonomous driving, and drones due to its high efficiency. However, existing lightweight networks often compromise segmentation accuracy to reduce parameter count and improve inference speed. To achieve an optimal balance among accuracy, latency, and model size, we propose the Dilated–Separable Convolution and Attention Fusion Network (DSAFNet), a lightweight real-time semantic segmentation network based on an asymmetric encoder–decoder framework. DSAFNet integrates three core components: (i) the Double-Layer Multi-Branch Depthwise Convolution (DL-MBDC) module that fuses channel splitting and multi-branch depthwise convolutions to efficiently extract multi-scale features with minimal parameters; (ii) the Multi-scale Dilated Fusion Attention (MDFA) module that utilizes factorized dilated convolutions and channel-spatial collaborative attention to expand the receptive field and reinforce key contextual features; (iii) the Multi-scale Attention Lightweight Decoder (MALD) that integrates multi-scale feature maps to generate attention-guided segmentation results. Experiments conducted on an RTX 3090 platform demonstrate that DSAFNet, with only 1.00 M parameters, achieves 74.78% mIoU and a frame rate of 74.74 FPS on the Cityscapes dataset, while 70.5% mIoU and a frame rate of 89.5 FPS on the CamVid dataset. Full article

Urban emergency medical services (EMSs) depend on time-critical accessibility, spatial demand distribution, and resilient transport networks. This study examines how built-environment characteristics shape spatiotemporal population intensity (as a proxy for latent EMS demand) within Shenzhen’s 10 min ambulance-accessible Emergency Medical Circle (EMC), using high-resolution Baidu Huiyan mobile-device data. Human activity intensity was quantified in 200 × 200 m grids and modeled against 20 built-environment indicators using a Gradient Boosting Decision Tree (LightGBM), with SHAP employed for interpretable attribution. By analyzing the distribution density and variance of SHAP dependence patterns, pronounced diurnal shifts in dominant drivers were identified. Medical facility density anchors nocturnal demand, road network permeability dominates pre-dawn mobility, land-use entropy and functional diversity peak during the midday period, while transit hubs and mixed-use amenities consolidate evening activity. The results further reveal critical non-linear thresholds—such as medical facility density (~1.5–2.5 km⁻²) and building density (~45,000–60,000 m² km⁻²)—beyond which marginal contributions diminish or become negative, indicating that proximity alone does not guarantee effective emergency coverage. These findings provide quantitative, time-sensitive guidance for EMC planning, highlighting the need for balanced facility dispersion, network prioritization, and demand-aware spatial design. By integrating high-resolution population dynamics with visually interpretable machine learning, this study advances a human-centered and operationally grounded framework for resilient emergency medical systems. Full article

Accurate spatial information on individual tree locations is essential for precision forestry, the integration of field and remote sensing data, and tree-level forest analyses. This study compared the positional accuracy and tree identification performance of four tree-mapping approaches: legacy paper maps, a pseudolite-based field positioning system (TerraHärp), drone-based laser scanning, and mobile laser scanning (MLS). The analysis was conducted in five long-term experimental forest sites in Lithuania, comprising pine- and spruce-dominated stands with varying stand densities. Tree locations derived from legacy maps and the TerraHärp system were compared to assess systematic and random positional discrepancies. TerraHärp-derived tree positions were subsequently used as a reference to evaluate the laser scanning-based methods. Positional accuracy was assessed using Hotelling’s T² test, root-mean-square error, and the National Standard for Spatial Data Accuracy (NSSDA), while spatial autocorrelation of deviations was examined using Moran’s I. The results indicated that discrepancies between TerraHärp and legacy maps were dominated by systematic horizontal shifts in the historical maps, whereas random positional variability was relatively small and consistent across stand types. Drone-based laser scanning showed a strong dependence of tree identification accuracy on stand density and mean tree diameter. Overall, CHM-based segmentation yielded more accurate tree identification than 3D point cloud segmentation, with mean F1-scores of 0.78 and 0.72, respectively. Positional accuracy varied by method, with the largest errors from CHM apexes and highest 3D point cloud points (mean NSSDA ≈ 1.8–2.0 m), improved accuracy using the lowest 3D cluster points (1.45–1.72 m), and the highest accuracy achieved using mobile laser scanning (mean NSSDA 0.76–0.90 m; >95% of trees within 1 m). These results demonstrate that pseudolite-based field mapping provides a reliable reference for high-precision tree location and for integrating field and laser scanning data in managed conifer stands. Full article

This study investigates the spatial dimensions of labour market polarization in Hungary by examining the widening gap between developed agglomerations and lagging peripheral regions. It explores how educational inequality, technology-driven risks, and constrained mobility affect the spatial aspects of labour market polarization. It covers all 197 districts of Hungary on the LAU-1 level. Using cluster analysis and OLS regression models, we shall explore relationships between employment rates, educational attainment, automation exposure—as based on occupation-level data—and a composite mobility index. From the data, we detected distinct labour market zones, which are dynamic agglomerations, industrial transition zones, and peripheral lagging. The data confirms that the “triple trap” is clearly experienced by the peripheral regions, with lower educational attainment, high exposure to automation impacting nearly 50%, and mobility constraints keeping the workforce bound to local public works employment. These results provide evidence that labor market polarization is a self-reinforcing spatial process. It implies that successful policy interventions should be comprehensive, addressing the interrelated elements of transport infrastructure, skill development, and regional economic diversification in one stroke to break the vicious circle of immobility. Full article

Few studies examine how slow-onset climate change interacts with local structural conditions to shape internal migration and long-term community sustainability. Using 2021 county-to-county migration data for the contiguous United States, this study analyzes spatial variation in in-migration, out-migration, and net migration rates in relation to temperature anomalies and place-based socioeconomic characteristics. Spatial regression results reveal no uniform relationship between recent temperature anomalies and migration outcomes. Instead, migration patterns are more strongly associated with urban status, housing market conditions, population composition, and long-run average climate. In some counties, higher temperature anomalies are associated with reduced out-migration, suggesting constrained mobility where economic and housing conditions limit relocation options. By contrast, extreme anomalies and greater environmental vulnerability are linked to lower in-migration, indicating diminished destination attractiveness. Overall, the findings suggest that internal migration responses to climate stress are mediated by local structural conditions rather than driven by temperature change alone, underscoring the importance of equitable adaption policies and place-based resilience strategies for sustainable regional development. Full article

In recent years, cycling mobility in urban environments across Spain has grown significantly, driven by sustainability policies and behavioral shifts following the COVID-19 pandemic. However, this growth has been accompanied by an increase in accidents in urban areas, where more than 72.6% of cyclist accidents are concentrated, with large cities being the most affected. This study aims to explore and analyze the factors influencing cycling accidents in Spanish municipalities with populations exceeding 50,000, during the period of 2020–2023. A total of 24 variables were analyzed, encompassing not only innovative cyclist infrastructure network features (line connectivity), but also urban morphology and street infrastructure, weather conditions and mobility (all transportation modes). The methodological approach combines Principal Component Analysis (PCA) with two negative binomial regression models: one addressing all cycling accidents, and another focusing specifically on collisions between cyclists and motor vehicles. PCA shows the complex relations between urban features when comparing cyclist accidents among cities. The main results from the Negative Binomial analysis show that increased bicycle lane length significantly reduces cycling accident risk, while higher intersections with traffic signal density are associated with a greater likelihood of car–bicycle crashes. These findings emphasize the importance of cycling infrastructure provision and intersection design and regulation as key policy levers for improving urban cyclist safety. Future research should seek to corroborate these results through micro-spatial analyses and accident geolocation, assessing their severity and accounting for more detailed data on cycling infrastructure. Finally, the results’ discussion underscores the importance of implementing holistic urban mobility strategies that prioritize cyclist safety. Full article

Electromobility is increasingly recognized as a cornerstone of sustainable transport, yet its adoption remains uneven across regions. This study develops an integrated framework that combines geospatial analysis, multi-criteria decision-making (MCDM), and power system evaluation to identify and prioritize fast-charging sites at the national scale. Applied to Costa Rica’s national road network (NRN), encompassing both urban centers and peripheral regions, the framework integrates spatial suitability, socioeconomic priorities, and grid readiness across projected electric vehicle (EV) penetration scenarios. Critically, power system simulations reveal voltage instability at distribution nodes (as low as 89.88% p.u.) under 3% EV penetration despite 99% renewable generation, demonstrating that grid capacity, not planning methodology, constitutes the primary barrier to electric mobility adoption. This finding, derived from the first national-scale analysis that integrates equity-driven spatial prioritization with comprehensive grid validation using real fleet projections, challenges conventional assumptions in transport-focused infrastructure planning. The framework provides a transferable tool for countries seeking to align EV infrastructure planning with sustainability and decarbonization objectives, while highlighting that grid reinforcement must precede, not follow, the deployment of fast-charging infrastructure. Full article

The rapid growth of electric vehicles (EVs) is intensifying charging demand in space-constrained parking facilities, where fixed charging piles (FCPs) are often underutilized due to parking–charging coupling and stall blocking. This study develops a coordinated planning framework for a hybrid charging system that integrates FCPs and mobile charging robots (MCRs). Two optimization models—operator profit maximization and social welfare maximization—are formulated to jointly determine the capacity configuration (numbers of FCPs and MCRs) and the spatial layout of FCPs and MCR base stations, subject to a queueing-theory-based waiting-time constraint. A nested heuristic solution method combining particle swarm optimization (PSO) and K-means++ is designed for tractable computation. Numerical experiments on a representative parking facility demonstrate a clear complementarity between fixed and mobile chargers: FCPs serve baseload demand economically, while MCRs provide flexible capacity that reduces average waiting time and mitigates congestion. The results further quantify the divergence between private and social objectives; when robot costs are reduced, the social-welfare model deploys approximately 35% more robots than the profit-maximizing solution to reduce user time losses. By improving charger utilization, the proposed hybrid planning approach enhances resource efficiency and supports sustainable EV charging infrastructure in dense urban parking facilities. Full article

Amid the growing convergence of leisure mobility, tourism, and rural development, second homes have emerged as a significant spatial phenomenon reshaping community structures in tourism-oriented rural areas. This study examines how second-home leisure practices contribute to place-making and community identity formation through land-use transformation and everyday spatial experience. Using the Mayangxi Ecotourism Area in Fujian Province, China, as a case study, this study develops a “space–sense of home–place identity” analytical framework grounded in Lefebvre’s theory of the production of space. A mixed-methods design integrating fieldwork, interviews, questionnaire surveys, and structural equation modeling is adopted. The results indicate that perceptions of physical, social, and cultural space significantly enhance second-home users’ sense of home. Physical and social spaces exert strong direct effects on place identity, with social interaction emerging as the most influential factor. Although sense of home positively mediates the relationship between spatial perception and place identity, this mediation is conditional rather than automatic. These findings suggest that second homes should be understood not merely as outcomes of land development, but as negotiated everyday spaces through which land-use transformation, social interaction, and emotional attachment collectively shape community reconstruction in tourism-oriented rural areas. Full article

Zhenwu (Perfected Warrior), one of the most influential Daoist martial deities, was historically shaped by the northern celestial emblem Xuanwu and later personified and integrated into the Daoist pantheon. While scholarship on Zhenwu has largely relied on textual sources, cliff sculptures provide a material setting in which doctrine, ritual space, and iconography can be examined together. Taking the Zhenwu niche (No. 1) at Nanshan, Dazu (Chongqing) as a case study, this article first situates the niche within the spatial program of the Nanshan Daoist carvings and describes its architectural design, composition, and inscriptional evidence of worship. It then revisits key motifs associated with Zhenwu—such as the sword, bare feet, and the turtle–snake pair—through Daoist and inner-alchemical (neidan) textual traditions. Rather than positing a direct or exclusive link between the Nanshan sculpture and inner-alchemical practice, the article argues that the niche mobilizes an established iconographic repertoire that could have resonated with late imperial discourses of self-cultivation, and that its northern placement within the Nanshan ensemble reinforces these cosmological associations. By combining site-based analysis with a cautious reading of Daozang and neidan texts, the study contributes to scholarship on Daoist visual culture and offers a framework for comparing Zhenwu images across regions and media. Full article