Search Results (720)

Wikidata provides extensive coverage of tourism-related Points of Interest (POIs), yet its heterogeneous type system and uneven metadata limit its direct use in smart tourism applications. This paper presents an end-to-end pipeline that transforms Wikidata POIs into a compact and interpretable tourism-oriented representation supporting multi-category assignments. We collect POIs from six countries—Greece, Italy, Spain, Norway, Sweden, and Denmark—and construct a dataset that integrates core identifiers with textual descriptions, type information, heritage indicators, geographic coordinates, and Wikipedia sitelinks. We introduce an eight-category tourism taxonomy capturing key themes, including cultural venues, archaeological and historic sites, monuments, fortifications, religious sites, protected areas, natural features, and coastal or water locations. As a reproducible baseline, category likelihoods are estimated using sentence embeddings and similarity to category anchor descriptions, producing a probability vector for each POI. Building on this baseline, we propose a fuzzy inference layer that integrates embedding-based probabilities with structured Wikidata signals to generate interpretable membership degrees across categories and enable principled multi-category classification. This fusion is particularly valuable for smart tourism applications, as it supports robust faceted exploration and personalized recommendations (e.g., “historic + coastal”), while providing evidence-based explanations that enhance user trust and facilitate curator oversight when POI metadata is sparse or ambiguous. The resulting pipeline produces ranked POI catalogs by country and category, country-level tourism profiles, and diagnostic views for examining uncertain cases. The approach is fully reproducible and readily adaptable to other geographic regions or domain taxonomies. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

The rapid adoption of electric vehicles (EVs) creates a planning challenge for the Kampala-Wakiso metropolitan region in Uganda, where the electricity grid already faces local network constraints. This study applies the EVPV-Simulator, an open-source geospatial modelling framework that links mobility demand, charging demand, and EV-PV complementarity, to assess projected charging demand and solar integration potential in the Kampala-Wakiso metropolitan region. By simulating the charging requirements of a projected fleet of 60,000 EVs, the study identifies a pronounced evening charging peak concentrated in residential areas and weakly aligned with daytime solar availability. Under the base-case charging pattern, increasing PV capacity raises the self-sufficiency potential, but has limited influence on the evening peak. In the base-case with 40 MW of installed PV capacity, the self-sufficiency ratio reaches 39.6%, while peak demand falls by only 0.20%. A charging location sensitivity analysis then shows that temporal alignment improves substantially when charging shifts from home towards workplaces and Points of Interest (POI). In a selected daytime oriented scenario with 40% workplace charging and 60% POI charging, the self-sufficiency potential reaches 68.97% and the mean daily maximum net load falls to about 18 MW at 40 MW of installed PV capacity. These results show that the value of solar integration depends strongly on where charging occurs, and that daytime charging access should be treated as a central variable in EV infrastructure planning. The study provides a planning oriented basis for future work incorporating feeder level validation, explicit PV siting constraints, and storage. Full article

This study proposes a multi-criteria decision-making framework for determining optimal locations for shared micromobility stations in Kars, Türkiye. The approach integrates spatial data with structured expert evaluation and applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank candidate locations. Eight representative locations were evaluated based on five criteria: points of interest (POId), public transport distance, activity level, accessibility, and installation suitability. Spatial indicators were obtained through map-based measurements, while qualitative criteria were assessed using expert-based scoring by 11 experts. The results indicate that locations with high activity density, strong accessibility, and a high concentration of POIs achieve the highest suitability scores. The city center (L2) and Kafkas University (L1) were identified as the most suitable locations, with closeness coefficients of 0.862 and 0.783, respectively. In contrast, the train station (L5) showed the lowest suitability, with a closeness coefficient of 0.326. A sensitivity analysis confirmed that the ranking structure remained unchanged under moderate variations in criteria weights, indicating the robustness of the proposed model. The findings suggest that micromobility systems are primarily driven by intra-urban mobility demand rather than by long-distance transportation nodes. From a sustainability perspective, the proposed framework supports evidence-based planning of shared micromobility infrastructure, which can contribute to reducing dependence on private automobiles, improving urban accessibility, and promoting low-carbon transportation. The findings provide practical guidance for municipalities seeking to develop environmentally sustainable, socially accessible, and resource-efficient urban mobility systems in medium-sized cities. The framework can also support broader sustainable urban development strategies and contribute to the achievement of sustainable mobility objectives. Full article

The electrification of urban freight is a central pathway for advancing China’s dual-carbon agenda, yet the spatial and temporal mismatch between charging supply and logistics demand remains a major bottleneck. Using Shanghai as a case study, this paper develops an integrated framework of hotspot identification, mechanism interpretation, and planning response for electric logistics vehicle (ELV) charging demand. Based on the operating records of more than 1200 pure electric logistics vehicles in Shanghai from 1 March to 30 November 2023, 85,367 valid charging events were extracted. ST-DBSCAN is used to detect charging demand hotspots, and a negative binomial model is employed to examine their determinants. The results show that charging demand is highly differentiated in space and time, following a pattern of daytime concentration in core logistics areas and nighttime dispersion toward peripheral parking and recharging spaces. Initial state of charge, daily mileage, logistics point of interest (POI) density, and road network density are all significantly associated with hotspot intensity, while the effects of time vary across daytime and nighttime charging contexts. The predominance of slow charging, together with a pronounced midday charging peak (12:00–17:00), points to a potential fast-charging pressure of fast-charging capacity in major logistics nodes. Based on these findings, the paper proposes targeted recommendations for hub-oriented fast-charging deployment, fleet–charging coordination, and data-driven governance. The study provides empirical evidence for improving the spatial planning and refined governance of urban freight energy infrastructure. Full article

Modern web and tourist maps use pictorial symbols to help users quickly and easily identify Points of Interest (POIs). Pictorial symbols are sometimes misinterpreted due to poor design choices. As a result, it is important to evaluate pictorial symbols with map users. This paper uses an online questionnaire to examine how different graphic parameters—such as frame outline, frame background, frame shape, color hue, and pictogram category (semantic, visual, or arbitrary)—are perceived by map users. The evaluation of pictograms includes three aspects: understanding, to capture the map reader’s opinion; preference, to investigate the map maker’s choice; and appropriateness, to document the evaluation of an existing map. Seven popular Points of Interest (POIs) were selected for the evaluation of pictorial symbols: Hotel, Restaurant, Parking, Museum, Airport, Hospital, and Church. Based on the questionnaire results and the statistical analysis of 520 responses, several conclusions were drawn. Users prefer symbols with a frame outline and a frame background. They also prefer symbols with a white background, which increases contrast and improves legibility. In contrast, users do not have a strong preference for a specific frame shape. In general, users can recognize symbol groups based on frame shape, but the effect is stronger when the color hue appears in the frame background or outline. The statistical analysis demonstrates that perceived appropriateness constitutes an objective measure related to comprehension. Furthermore, appropriateness is independent of the pictogram classification as semantic, visual, or arbitrary. Instead, it is determined by the graphic ability of the pictogram to represent a specific POI. This conclusion reaffirms the importance of designing successful semantic and visual pictograms or adopting those already familiar to map users, as familiarity has also been identified as an important factor by this research. Overall, this paper, based on user evaluations, provides practical insights to improve pictorial symbols on a tourist map. Full article

Understanding the complex relationship between the built environment and urban vitality is essential for evidence-based urban renewal. However, most existing studies rely on linear regression models that fail to capture the non-linear threshold effects inherent in urban systems and depend on costly proprietary datasets that limit reproducibility. This study proposes a scalable, open-data-driven framework to decode the non-linear mechanisms governing population-based urban vitality in Zhengzhou, a rapidly regenerating metropolis in Central China. Using Areas of Interest (AOIs) as functional spatial units to mitigate the Modifiable Areal Unit Problem (MAUP), we construct a multidimensional built environment indicator system (5D+S: Density, Diversity, Design, Distance to Transit, Destination Accessibility, and Surroundings) from multi-source open data, including 100 m WorldPop population grids, OpenStreetMap building vectors, Points of Interest (POIs), and transit station data. An explainable machine learning approach combining XGBoost with SHapley Additive exPlanations (SHAP) is employed to identify the relative importance of built environment factors and quantify their non-linear threshold effects on population-based urban vitality (operationally defined as residential population density derived from WorldPop 100 m grids). Across 3920 AOIs, XGBoost (R² = 0.846, RMSE = 0.104) substantially outperforms Ordinary Least Squares regression (R² = 0.634), confirming pervasive non-linear relationships, with stable 5-fold cross-validated R² = 0.713 ± 0.115. SHAP analysis reveals four dominant drivers: Distance to Commercial Core (DistCBD), Bus Station Density within 500 m (BusDen500), Green Coverage Ratio (GreenRatio), and Building Density (BD). Critical thresholds are identified: vitality contributions decay sharply beyond approximately 4.3 km from the CBD; at least 4 bus stations within 500 m are required for meaningful transit benefit; building density delivers positive returns within a 2–30% range; and excessive green coverage above 8.5% within 500 m is associated with declining population-based vitality, a finding that reflects spatial competition between ecological land use and residential density rather than a negative effect of greenery per se. These findings provide quantitative design guidelines for precision urban renewal, moving beyond “the more, the better” planning assumptions to identify optimal intervention ranges. Full article

The widespread use of mobile Internet- and location-based services has generated large-scale check-in data in location-based social networks, creating opportunities for intelligent urban-mobility analysis and personalized mobility services. Making the next point-of-interest (POI) recommendation is an important task in this setting because it supports context-aware destination suggestion, travel assistance, and smart mobility services. However, existing methods still face challenges in jointly modeling higher-order mobility patterns, uneven time intervals, geographic reachability, and fine-grained intra-day temporal regularities. To address these issues, this paper proposes ASTHN, an Adaptive Spatio-Temporal Hypergraph Network for next POI recommendation. ASTHN constructs three fine-grained spatio-temporal context hypergraphs from minimum time interval, spatial proximity, and hourly preference, and uses hypergraph neural networks to learn view-specific POI representations. A context-adaptive fusion module then aligns and integrates multi-source spatio-temporal signals, while an ST-GRU with spatio-temporal gates captures dynamic trajectory evolution. Temperature scaling is further applied at the output layer to alleviate overly concentrated score distributions. Experiments on Foursquare-NYC and Foursquare-TKY show that ASTHN consistently outperforms representative baselines. With results reported as mean ± std over three random seeds, ASTHN improves over the strongest baseline by 3.79%, 14.62%, 2.28%, and 1.24% on NYC in Recall@5, Recall@10, NDCG@5, and NDCG@10, respectively. On TKY, the corresponding improvements are 5.83%, 37.20%, 13.86%, and 20.49%. Ablation, parameter, complexity, and application-oriented case analyses further demonstrate the effectiveness, stability, and practical usability of ASTHN for next POI recommendation in urban-mobility scenarios. Full article

Temples are tourist attractions that represent the history and culture of Thailand, especially in Chiang Mai province—a city with a rich history that has become a prominent destination attracting visitors from around the world. Many temples remain undiscovered yet are ready for tourists to visit; however, due to unfamiliarity, tourists tend to visit only the well-known temples, as other visitors do, missing great opportunities to engage with new cultural heritage tourism experiences. To address this issue, we propose a Hybrid Three-Stage Route Planning Recommendation (HTS-RPR), a novel method for tourist route planning that delivers recommended routes based on tourists’ preferred constraints. This model contains three-stage route recommendations providing an optimal single-day route with mandatory and recommended points of interest (POIs) through a metaheuristic integrating Mixed Integer Programming (MIP), heuristic-based POI recommendation filtering, and Genetic Algorithm route optimization with Bayesian reward and peak-time awareness, ensuring that users can effectively travel cultural routes with high popularity and satisfaction while avoiding attractions during periods of high traffic. To validate the efficacy of the proposed model, experiments with three baseline methods were conducted. The results demonstrate that HTS-RPR achieves the best fitness score in 55 out of 60 scenarios and the best reward in 54 out of 60 scenarios, with a median fitness score 28.34% and 103.67% higher than the Genetic Algorithm and Multi-Start Simulated Annealing baselines, respectively, and a median total reward exceeding all three baselines by up to 40.74%. Although HTS-RPR’s median execution time is approximately 2.6 times that of the Genetic Algorithm, it remains 84.5% faster than the Multi-Start Simulated Annealing baseline, offering a favorable trade-off between solution quality and computational cost. Moreover, the framework’s pluggable reward function enables destination managers to configure recommendation priorities, including the promotion of undiscovered tourist attractions, while the peak-time-aware optimization mitigates congestion at specific POIs. Full article

This study presents a reproducible high-resolution framework for assessing urban chikungunya environmental suitability and outbreak-related spatial heterogeneity during the 2025 outbreak in the western Guangdong–Hong Kong–Macao Greater Bay Area. Using Sentinel-2–derived environmental indicators together with a random forest–based residual correction of Landsat surface temperature, we developed a 10 m weighted additive Mosquito Habitat Suitability Index (MHSI). Index weights were empirically derived by comparing reported case locations at the street and town level with randomly sampled background points. The optimized weighting scheme indicated that humidity- and water-related conditions contributed more strongly to habitat suitability than vegetation and temperature. Reported case locations generally corresponded to higher MHSI values than background locations, suggesting that the index captures broad spatial patterns of environmental suitability. Comparison with a coarser, model-derived global chikungunya risk map was used as an external comparative consistency assessment rather than predictive validation, showing moderate agreement at the macro-spatial scale (Pearson r = 0.3421) after correction for spatial autocorrelation. Residual-difference analysis, combined with multiple points-of-interest (POI) categories, ordinary least squares (OLS), and geographically weighted regression (GWR), further suggested that human activity, transport connectivity, and healthcare accessibility may account for part of the remaining spatial mismatch not explained by environmental suitability alone. Sensitivity analyses indicated that the broad LST downscaling pattern and the exploratory GWR interpretation were reasonably stable under alternative sampling, smoothing, grid-size, and bandwidth settings. Taken together, this framework provides preliminary spatial evidence for high-resolution environmental suitability assessment and exploratory interpretation of outbreak-related spatial heterogeneity, while underscoring the need for finer-scale epidemiological data and more explicit representation of human-driven processes. Full article

Objectives: This study aimed to investigate the impact of the food environment surrounding schools on nutritional status and body mass index (BMI) of children and adolescents, offering insights for developing evidence-based policies to promote healthier school surroundings. Methods: Based on 357,782 physical examination records from 140,578 children and adolescents aged 6 to 19 in the Shenzhen Student Health Surveillance System for the 2018–2025 academic years, this study employed latent class mixed models to analyze BMI Z-score trajectory changes among children and adolescents. Furthermore, multinomial logistic regression and logistic regression models were utilized to examine the association between the number of catering points of interest (POIs) near schools, including total number, fast-food restaurants, pastry shops, and beverage stores, and the nutritional status and BMI trajectories of children and adolescents, respectively. Data from Huairou District, Beijing, was used to verify the applicability of the findings in Northern China. Results: 20.71% of children and adolescents in Shenzhen were overweight or obese, and 44.70% were consistently overweight from 2018 to 2025. The increase in catering POIs around schools was significantly associated with nutritional status and overweight trajectory, with pastry shops having a particularly pronounced effect. Each interquartile range (IQR) change in pastry shop was associated with 4.25% (95% CI: 2.96%, 5.56%) increase in the odds of overweight compared with the normal nutritional group, and with 5.03% (95% CI: 3.62%, 6.45%) increase in the odds of the overweight trajectory compared with the normal weight trajectory. Moreover, schools in above-median GDP regions required more attention. A similar association between the number of catering POIs near schools and long-term overweight among children and adolescents was observed in Huairou District, Beijing. Conclusions: The food environment surrounding schools might play a contributory role in shaping the BMI trajectories of children and adolescents. The study emphasized the importance of focusing on the food environment near schools, providing insights for weight management interventions among children and adolescents as well as healthy urban planning. Full article

Point-of-interest (POI) data are widely used to spatialize and predict socioeconomic variables, yet their consistency across regions and over time, as well as their cross-regional generalizability, remain insufficiently understood. This study examines these issues using county-level units in China for 2010 and 2020 from three perspectives: relationship structure, cross-regional generalization, and model improvement. First, a power-law model is applied to characterize the nonlinear relationship between POI density and population density and to assess its spatiotemporal heterogeneity. Second, generalizability is evaluated by comparing model parameters and predictive performance under random and spatially stratified sampling. Third, multi-source geospatial data, including nighttime lights, road networks, and land use, are integrated to compare linear, spatial, machine learning, and ensemble models. Results reveal a consistent sublinear relationship with strong spatial heterogeneity. Under spatially independent validation, predictive accuracy declines and becomes more variable, indicating limited cross-regional generalization. Integrating multi-source data with ensemble learning improves stability and reduces uncertainty. POI remains the dominant predictor, though its relative importance becomes more concentrated in 2020. Overall, the study highlights the limitations of POI-based population estimation and proposes strategies to enhance robustness and generalizability. Full article

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

Urban fire risk in high-density cities is characterized by complex spatial heterogeneity and nonlinear relationships with the built environment, population distribution, and climatic conditions. However, most existing studies rely on linear assumptions and offer limited interpretability. To address this gap, we developed an interpretable analytical framework that integrates the CatBoost model with SHAP (SHapley Additive exPlanations), using Futian District in Shenzhen as a case study. We constructed a fire risk surface from historical fire incident data using kernel density estimation (KDE) and incorporated multiple urban environmental factors—including points of interest (POIs), road networks, and meteorological variables—as explanatory variables. The CatBoost model captured nonlinear relationships, while SHAP quantified feature importance and revealed interaction effects. The results show that urban fire risk is strongly associated with the spatial agglomeration of population-related facilities, especially high-density commercial and residential areas, as well as thermal conditions. Several variables exhibit clear nonlinear threshold effects, with their influence on fire risk varying markedly across different intensity ranges. Interaction analysis further indicates that combinations of built-environment characteristics and climatic factors jointly shape the spatial pattern of fire risk. These findings provide empirical insights into the spatial mechanisms underlying urban fire risk and highlight the value of interpretable machine learning in urban safety research. The proposed framework offers a practical tool for developing more targeted, evidence-based fire risk management strategies in high-density urban areas. Full article