Search Results (983)

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

Tourism is a key spatial process linking human mobility, resource consumption, and environmental change. Despite growing awareness of climate risks, sustainable travel behavior often remains inconsistent with pro-environmental attitudes, reflecting the persistent attitude–behavior gap. This study examines how psychological factors—sustainability motives, ecological identity, and climate attitudes—interact with artificial intelligence (AI) transparency to shape travel decisions with spatial and environmental consequences. Using survey data from 1795 leisure travelers and a discrete-choice experiment simulating hotel booking scenarios, the study shows that ecological identity and climate attitudes reinforce sustainability motives and intentions, while transparent AI recommendations enhance perceived clarity, data visibility, and reliability. These transparency effects amplify the influence of eco-scores on revealed spatial preferences, with trust mediating the relationship between transparency and sustainable choices. Conceptually, the study integrates psychological and technological perspectives within a geographical framework of human–environment interaction and extends this lens to rural destinations, where travel decisions directly affect cultural landscapes and climate-sensitive ecosystems. Practically, the findings demonstrate that transparent AI systems can guide spatial redistribution of tourist flows, mitigate destination-level climate pressures, and support equitable resource management in sustainable tourism planning. These mechanisms are particularly relevant for rural areas and traditional cultural landscapes facing heightened vulnerability to climate stress, depopulation, and uneven visitation patterns. Transparent and trustworthy AI can thus convert environmental awareness into spatially sustainable behavior, contributing to more resilient and balanced tourism geographies. Full article

Large-scale Low Earth Orbit (LEO) satellite constellations have become critical infrastructure for global communications, yet routing optimization remains challenging. Due to high-speed satellite mobility and limited local perception capabilities, traditional shortest-path algorithms struggle to adapt to dynamic topology changes and effectively handle random fluctuations in traffic loads and inter-satellite link states. Meanwhile, as constellation scales expand, centralized routing mechanisms face deployment difficulties due to high communication latency and computational complexity. To address these issues, this paper proposes a hierarchical load-balanced routing optimization algorithm based on geographic partitioning. The algorithm divides the constellation into multiple regions by latitude and longitude, establishing a hierarchical cooperative decision mechanism: the upper layer handles inter-region routing decisions while the lower layer manages intra-region routing optimization. Within regions, a load-aware K-shortest paths algorithm enables path diversification, achieving global coordination through cross-region information sharing and dynamic path selection, thereby reducing end-to-end routing latency while enhancing adaptability to dynamic environments and balancing routing performance with system scalability. In simulation scenarios with a Starlink-like architecture of 1512 satellites, experimental results demonstrate that compared to shortest-path routing, the algorithm reduces end-to-end latency by 14.1% and average satellite load by 15.9%. Under dynamic load scenarios with incrementally increasing user traffic, the algorithm maintains stable performance, validating its robustness under traffic fluctuations and link state variations. Full article

Pedestrian accessibility is a critical dimension of sustainable and inclusive transportation systems, yet many cities lack reliable data on infrastructure features that support visually impaired users. Among these, podotactile paving plays a vital role in guiding movement and ensuring safety at intersections and transit nodes. However, tactile paving networks remain largely absent from digital transport inventories and automated mapping pipelines, limiting the ability of cities to systematically assess accessibility conditions. This paper presents a scalable approach for identifying and mapping podotactile areas from mobile and handheld laser scanning data, broadening the scope of data-driven urban modelling to include infrastructure elements critical for visually impaired pedestrians. The framework is evaluated across multiple sensing modalities and geographic contexts, demonstrating robust generalization to diverse transport environments. Across four dataset configurations from Madrid and Mantova, the proposed DeepLabV3+ model achieved podotactile F1-scores ranging from 0.83 to 0.91, with corresponding IoUs between 0.71 and 0.83. The combined Madrid–Mantova dataset reached an F1-score of 0.86 and an IoU of 0.75, highlighting strong cross-city generalization. By addressing a long-standing gap in transportation accessibility research, this study demonstrates that podotactile paving can be systematically extracted and integrated into transport datasets. The proposed approach supports scalable accessibility auditing, enhances digital transport models, and provides planners with actionable data to advance inclusive and equitable mobility. Full article

This dataset presents field observations collected in the municipality of Goiatuba, Goiás State, Brazil, a consolidated and representative agricultural frontier of the Brazilian Cerrado biome. The region presents diverse land use dynamics, including annual cropping systems, irrigated fields with up to three harvests per year, and pasturelands. We conducted a field campaign from 3 to 7 November 2025, corresponding to the beginning of the 2025/2026 Brazilian crop season, when crops were at distinct early phenological stages. To ensure representativeness, we delineated 117 reference fields prior to the field campaign, and an additional 463 plots were surveyed during work. Geographic coordinates, crop types, and photographic records were obtained using the GPX Viewer application, a handheld GPS receiver, and the QField 3.7.9 mobile GIS application running on a tablet uploaded with Sentinel-2 true-color imagery and the municipal road network. Plot boundaries were subsequently digitized in QGIS Desktop 3.34.1 software, following a conservative mapping strategy to minimize edge effects and internal heterogeneity associated with trees and water catchment basins. In total, more than 26,000 hectares of agricultural fields were mapped, along with additional land use and land cover polygons representing water bodies, urban areas, and natural vegetation fragments. All reference fields were labeled based on in situ observations and linked to Sentinel-2 mosaics downloaded via the Google Earth Engine platform. This dataset is well-suited for training, testing, and validation of remote sensing classifiers, benchmarking studies, and agricultural mapping initiatives focused on the beginning of the agricultural season in the Brazilian Cerrado. Full article

Open public data is a vital institutional arrangement for overcoming data constraints in corporate low-carbon technological innovation. Using a panel dataset of China’s Shanghai and Shenzhen A-share listed firms over the 2007–2023 period, this study employs a difference-in-differences (DID) approach to examine the impact of open public data on corporate low-carbon technological innovation. The results show that open public data has a significant positive effect on corporate low-carbon technological innovation, and the results remain robust across multiple validation tests. Mechanism tests point out that government transparency negatively moderates the promotional effect of public data openness on corporate low-carbon technological innovation, while barriers to factor mobility positively moderate this effect. The heterogeneity analysis indicates that the positive impact of open public data is more pronounced among firms characterized by higher R&D investment, lower financial constraints, and greater digitalization. Further analysis indicates that open public data also exhibits significant geographic and industry spillover effects, with the geographic spillover following an inverted U-shaped pattern of decay and the industry spillover driven by peer imitation. This study provides evidence on leveraging open public data to stimulate low-carbon innovation and facilitate green economic transformation, offering valuable insights for advancing data-driven sustainable development globally. Full article

Against the backdrop of China’s slowing urbanization and increasing regional disparities, existing research on the spatiotemporal evolution and multidimensional drivers of urban–rural transformation (URT) requires further elaboration, particularly regarding county-level differentiation and the dynamic interactions among these drivers. This study integrates spatiotemporal hot spot analysis with a multi-factor geographical detector model to systematically examine China’s URT from 1990 to 2023. The findings reveal the following: (1) The area of urban–rural construction land increased by 149.54% overall from 1990 to 2023, but the annual average growth rate dropped sharply to 4.32% during 2000–2023, indicating overall deceleration in spatial expansion. (2) Significant structural adjustments occurred at the county level: the proportion of counties with high spatial expansion degree decreased by 20%, while counties experiencing spatial contraction increased by 6%, suggesting that growth dynamics have become increasingly concentrated in limited counties. (3) Spatially, a clear “northern contraction and southern expansion” divergence emerged, which was primarily driven by the synergistic effects of policy reorientation, market-driven factor mobility, and differential natural endowments. (4) Expanding counties benefited from urban agglomeration plans, population influx, industrial upgrading, and favorable terrain, whereas contracting counties were constrained by rigid ecological and farmland conservation policies, population outmigration, undiversified industries, and topographical limitations. These findings provide an important premise for formulating feasible policies on differentiated spatial governance and urban–rural sustainable development. Full article

This paper presents a comprehensive methodology to forecast the daily energy demand associated with recharging private electric vehicles in urban areas. The approach is based on plausible scenarios regarding the penetration of battery-powered vehicles and the availability of charging infrastructure. Accurate space and time forecasting of charging activities and power requirements is a critical issue in supporting the transition from conventional to battery-powered vehicles for urban mobility. This technological shift represents a key milestone toward achieving the zero-emissions target set by the European Green Deal for 2050. The methodology leverages Floating Car Data (FCD) samples. The widespread use of On-Board Units (OBUs) in private vehicles for insurance purposes ensures the methodology’s applicability across diverse geographical contexts. In addition to FCD samples, the estimation of charging demand for private electric vehicles is informed by a large-scale, detailed survey conducted by ENEA in Italy in 2023. Funded by the Ministry of Environment and Energy Security as part of the National Research on the Electric System, the survey explored individual charging behaviors during daily urban trips and was designed to calibrate a discrete choice model. To date, the methodology has been applied to the Metropolitan Area of Rome, demonstrating robustness and reliability in its results on two different scenarios of analysis. Each demand/supply scenario has been evaluated in terms of the hourly distribution of peak charging power demand, at the level of individual urban zones or across broader areas. Results highlight the role of the different components of power demand (at home or at other destinations) in both scenarios. Charging at intermediate destinations exhibits a dual peak pattern—one in the early morning hours and another in the afternoon—whereas home-based charging shows a pronounced peak during evening return hours and a secondary peak in the early afternoon, corresponding to a decline in charging activity at other destinations. Power distributions, as expected, sensibly differ from one scenario to the other, conditional to different assumptions of private and public recharge availability and characteristics. Full article

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article

In the rapidly evolving landscape of logistics and last-mile delivery, optimizing efficiency and minimizing costs are paramount. This paper introduces a novel heuristic approach designed to enhance the efficiency of a truck-and-drone delivery system. Our method addresses the complex challenge of coordinating the movements of a truck, which serves as a mobile depot, and an unmanned aerial vehicle (UAV or drone), which performs rapid, short-distance deliveries. Our system proposes a two-step heuristic. For truck routes, we utilized the Concorde Solver to determine the optimal path, based on real-world road distances between locations in Bacău County, Romania. This data was meticulously collected and processed as a Traveling Salesman Problem (TSP) instance with precise geographical information. Concurrently, a drone is deployed for specific deliveries, with routes calculated using the Haversine formula to determine accurate distances based on geographical coordinates. A crucial aspect of our model is the integration of the drone’s limited autonomy, ensuring that each mission adheres to its operational capacity. Computational experiments conducted on a real-world dataset including 93 localities from Bacău County, Romania, demonstrate the effectiveness of the proposed two-stage heuristic. Compared to the optimal truck-only route, the hybrid truck-and-drone system achieved up to 15.59% cost reduction and 38.69% delivery time savings, depending on the drone’s speed and autonomy parameters. These results confirm that the proposed approach can substantially enhance delivery efficiency in realistic distribution scenarios. Full article

Amid global digital transformation, digital service trade has become a transformative force reshaping international economies. We employ an innovative combination of Social Network Analysis (SNA) and Quadratic Assignment Procedure (QAP) to simultaneously dissect the macroscopic structure and microscopic determinants of the global digital service trade network. Key findings reveal: (1) The global digital service trade network exhibits distinct scale-free and small-world characteristics, reflecting deepening globalization. (2) The global hierarchy demonstrates structural rigidity, wherein core nations persistently reinforce their dominance despite selective upward mobility achieved by certain emerging economies. (3) Clear community differentiation emerges, featuring stable European subgroups, dynamic Asia-Pacific reorganization, and marginalized yet internally diverging Africa-Latin America clusters. (4) QAP regression identifies technological gaps and economic disparities as primary enablers, whereas geographical distance, internet development asymmetries and digital infrastructure divides constitute significant barriers, with linguistic commonality exerting positive effects. Based on empirical findings, we propose policy suggestion from four aspects: multilateral coordination for digital trade rules, digital infrastructure development, regional digital integration, and cross-civilizational digital communities. The study enriches analytical approaches to digital trade networks and provides theoretical foundations and policy insights for constructing an inclusive global digital economy framework. Full article

Understanding the spatial heterogeneity of traffic congestion drivers is crucial for data-informed urban planning in tourist cities. This study investigates the spatiotemporal relationship between built environment characteristics and traffic congestion in the central urban area of a major northern Chinese tourist city. We apply a Multiscale Geographically Weighted Regression (MGWR) model to geospatial data across four typical peak periods and benchmark the results against Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The MGWR model demonstrates superior capability in capturing spatial non-stationarity and multiscale effects. The results reveal strong spatiotemporal heterogeneity in the effects of built environment factors on congestion. Intersection density demonstrates a stronger mitigating effect during weekday evening peaks. Catering facilities significantly exacerbate congestion in tourist hotspots. Tourism-related facilities such as hotels and attractions intensify congestion during weekend peaks. Parking availability shows dual impacts, with peripheral parking reducing pressure and central clustering worsening congestion. Our geospatially disaggregated results provide empirical evidence for location-sensitive and temporally adaptive traffic management and urban design strategies. This study highlights the value of MGWR-based spatial modeling in supporting geoinformation-driven urban mobility planning. Full article

This paper investigates the integration of proximity theory (PT) into the management of public transport service disruptions within sustainable Mobility-as-a-Service (MaaS) systems, an area that is largely underexplored. PT provides a multidimensional framework for analyzing relationships and interactions within complex systems, encompassing five dimensions: geographical, cognitive, institutional, organizational, and social, each influencing coordination, learning, and adaptability. Building on this framework, the study introduces temporal proximity as an original sub-dimension of geographical proximity, forming a spatial–temporal proximity theory (PTST), which highlights the critical role of timing, synchronization, and coordinated responses in transport disruption management. To operationalize these principles, a mixed-integer programming (MIP) model was developed to optimize traveler assignments across 50 routes for 10 travelers, minimizing delays, transfers, walking distance, crowding, and CO₂ emissions. Two scenarios were analyzed: one without environmental considerations and another with CO₂ penalties. Results show that emissions were reduced by up to 50% for certain routes, while maintaining feasible travel times and route choices. The case study demonstrates that PTST can be operationalized as a practical tool, bridging mobility resilience and environmental responsibility, and providing actionable insights for sustainable and intelligent MaaS platforms. Full article

This study investigates the role of modern small-scale cities in addressing public health challenges through the lens of spatial justice, using the city of Tartu, Estonia, as a case study. Tartu has been recognized for its progressive public health initiatives, including the Tartu Health Care College, Mental Health Centre, Smoke-Free Tartu campaign, Health Trail network, Healthy School Program, and an expanding smart bike-sharing system. By employing Geographic Information Systems (GIS), we map and analyze the spatial distribution and accessibility of health-promoting infrastructure, such as healthcare facilities, green and blue spaces, health trails, and mobility services, across the urban landscape. A population-weighted accessibility assessment indicates that, although Tartu’s central districts (e.g., Kesklinn (HRI: 0.972)) are well-served, peripheral and densely populated districts such as Annelinn (HRI: 0.351) and Ropka (HRI: 0.377) exhibit notable deficits in health-related infrastructure. However, access to green infrastructure and mobility services is more evenly distributed citywide, reflecting a relatively equitable provision of non-clinical health assets. These findings highlight both the strengths and spatial gaps in Tartu’s health-oriented urban design, emphasizing the need for targeted investment in underserved areas. The study contributes to emerging studies on health-justice planning in small-scale urban contexts and demonstrates how spatial analytics can be guided to advance distributional justice in the provision of public health infrastructure. Ultimately, this research indicates the essential role of spatial analysis in guiding inclusive and data-informed health planning in urban environments. Full article

Against the backdrop of the rapid development of digital technologies, such as mobile internet, big data, and cloud computing, the geographical agglomeration of industries is gradually shifting toward virtual agglomeration. In this paper, we examine the effect of both geographical and virtual agglomeration of the agricultural industry on farmers’ agricultural income, and we focus on the transmission mechanism of technological innovation in this process. In the empirical section, using the citrus industry as an example, we employed a moderated mediation effect model for verification and derived the following conclusions: (1) Both geographical and virtual agglomeration of the agricultural industry promote an increase in farmers’ agricultural income by enhancing technological innovation, respectively. (2) Virtual agglomeration of the agricultural industry has a negative moderating effect on the relationship between geographical agglomeration and farmers’ agricultural income, that is, virtual agglomeration alleviates the “crowding effect” and to some extent substitutes for geographical agglomeration. (3) In the mechanism where geographical agglomeration in the agricultural industry increases farmers’ agricultural income through technological innovation, virtual agglomeration has a positive moderating effect. This paper is important for enabling farmers to share the benefits of the digital economy and achieve continuous growth in agricultural income. It is also important for the sustainable development goals adopted by the United Nations, such as eliminating poverty (SDG1), eliminating hunger (SDG2), promoting sustainable economic growth and full employment (SDG8), and promoting innovation (SDG9). Full article