Search Results (152)

As tourism shifts from sightseeing to experience-oriented consumption, understanding how tourist perception differs across heterogeneous destination types and spatial scales remains challenging. Using 23,439 Volunteered Geographic Information (VGI) reviews archived for six destinations in Shangrao, China, this study compares mountain-type and rural comprehensive destination products. These are operational dominant-function categories rather than mutually exclusive geomorphological classes. The archive supports fine-grained sentiment, topic, and semantic-network analyses; annual temporal comparisons use the full 23,439-review corpus covering 2019–2025, whereas a separate subset of reviews posted from 1 August 2022 with official IP labels, aggregated into 2022–2024 province–year observations, supports Pooled Ordinary Least Squares (Pooled OLS) estimation. A hybrid lexicon–XLM-RoBERTa workflow, BERTopic, semantic co-occurrence analysis, and Pooled OLS are integrated in a cross-scale framework. Static results reveal shared strengths and weaknesses—high scenery and overall-experience evaluations but low price evaluations—alongside type-specific structures: mountain reviews concentrate on natural scenery, climbing effort, and accessibility, whereas rural reviews span village landscapes, cultural activities, accommodation, and nighttime experiences. Temporally, mountain demand retains a stable scenic core while accessibility concerns become more salient; rural demand shifts from traditional agricultural landscapes toward nighttime performances and other experience-oriented products. Cross-scale regressions identify destination- and dimension-specific correlates rather than causal drivers: urbanization is positively associated with several rural evaluations, while ecological contrast, climatic difference, and competing scenic resources are associated with more critical assessments in selected dimensions. The findings show that perception differences arise from the interaction of destination product structures and origin-region contexts, supporting differentiated accessibility management for mountain destinations and balanced product innovation, service improvement, and commercialization control for rural destinations. Full article

The accelerated pace of urbanization across the Global South calls for precise, real-time building footprint data to underpin effective urban governance and enhance disaster resilience. Conventional mapping approaches, however, suffer from inefficiency in data acquisition and updating. Although Volunteered Geographic Information (VGI) provides a crowdsourced solution for geospatial data collection, it is commonly hindered by significant heterogeneity—manifested in inconsistent data completeness, positional inaccuracies and poor topological consistency across different datasets. To address these critical limitations, this study proposes an intelligent geospatial agent framework designed to autonomously fuse building data from multiple heterogeneous sources, including VGI, Very High-Resolution (VHR) satellite imagery, and Light Detection and Ranging (LiDAR) data. This study’s core innovative points are embodied in three key modules: a supervised VGI quality verification module that leverages the Random Forest model to evaluate the reliability of individual building feature elements; a hybrid building extraction engine which integrates LiDAR data with the Segment Anything Model (SAM) to realize zero-shot building extraction; and a cognitive rule engine that adopts Multi-Criteria Decision Analysis (MCDA) for the intelligent resolution of spatial conflicts. Comprehensive validation experiments were conducted in two African cities experiencing rapid urbanization—Kigali and Dar es Salaam. The results show that the proposed framework boosts data completeness by more than 29% and attains a fused dataset F1-Score of 0.919, effectively converting incomplete VGI data into a geospatial resource with near-official authoritative quality. Full article

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

Volunteered Geographic Information (VGI) is geographic data obtained from voluntary contributions of individual contributors on social media and non-social media platforms, where contributors exhibit diverse interests and behavior patterns. While studies have found that the COVID-19 pandemic has influenced VGI contributor behavior on social media platforms (Facebook, X, and Instagram, etc.), less is known about contribution behaviors on non-social media VGI platforms such as OpenStreetMap (OSM). This study investigates how individual OSM contributors’ data contribution behaviors changed after the COVID-19 outbreak, using New York City as a case study. Metrics quantifying temporal, spatial, thematic, participation, and social interaction aspects of contribution behavior were developed to characterize individual-level contribution behaviors in both the pre- and post-COVID periods (2016–2019 and 2020–2023, respectively). Contributors were clustered into three groups based on pre-COVID behavioral patterns (as reflected by the metrics) using the K-Means algorithm. The resulting model was then applied to identify changes in contributors’ cluster memberships in the post-COVID period. Results reveal differences in contribution behaviors between the two time periods. Compared to pre-COVID contributors, post-COVID contributors, on average, showed stronger contribution engagement, including longer lifespans, larger spatial extent of edits, higher contribution volumes, a greater emphasis on modification over creation, and stronger co-editing network interactions. Healthcare amenity-related edits remained a small fraction of total contributions across both periods and all clusters. Contributors participating in data contribution in both time periods generally increased data contribution engagement after the COVID outbreak, characterized by longer lifespans, broader spatial coverage, more balanced creation and modification, and stronger network centrality. These findings highlight changes in individual contribution behavior under COVID-19 and exhibits the value of examining VGI contribution at the individual level. Full article

This study investigates flooding and erosion impacts and human responses in Aoraki Mount Cook and Westland Tai Poutini national parks in Aotearoa New Zealand. These fast-eroding landscapes provide important test cases and insights for considering the public access dimensions of climate change. Our objectives were to explore and characterise the often-overlooked role of public access as a ubiquitous concern for protected areas and other area-based conservation approaches that facilitate connections between people and nature alongside their protective functions. We employed a mixed-methods approach including volunteered geographic information (VGI) from a park user survey (n = 273) and detailed case studies of change on two iconic mountaineering routes based on geospatial analyses of digital elevation models spanning 1986–2022. VGI data identified 36 adversely affected locations while 21% of respondents also identified beneficial aspects of recent landscape changes. Geophysical changes could be perceived differently by different stakeholders, illustrating the potential for competing demands on management responses. Impacts of rainfall-triggered erosion events were explored in case studies of damaged access infrastructure (e.g., roads, tracks, bridges). Adaptive responses resulted from formal or informal (park user-led) actions including re-routing, rebuilding, or abandonment of pre-existing infrastructure. Three widely transferable dimensions of public access management are identified: providing access that supports the core functions of protected areas; evaluating the impacts of both physical changes and human responses to them; and managing tensions between stakeholder preferences. Improved attention to the role of access is essential for effective climate change adaptation in parks and reserves. Full article

Urban revitalization processes are increasingly requiring inclusive and data-driven approaches that address spatial inequalities and support the achievement of the Sustainable Development Goals (SDGs). The article presents a methodology for utilizing social geoparticipation tools in the revitalization process of the Warsaw University of Technology campus. The study demonstrates how campus-scale geoparticipation can incorporate SDGs and spatial justice principles in micro-urban contexts, with a methodology that is transferable to city-scale projects and provides practical guidance for inclusive and sustainable urban governance. This enables the transformation of volunteered geographic information (VGI) data and spatial databases into practical spatial knowledge that supports sustainable urban development. Empirical analysis of 710 responses and nearly 1000 mapped locations revealed that 83% of respondents identified insufficient greenery as the primary spatial problem. At the same time, accessibility (β = 0.618) and green infrastructure quality (β = 0.553) were the strongest predictors of the need for change. The collected feedback from the academic community was processed using exploratory data analysis and spatial statistics into a spatial knowledge base. ESRI’s ArcGIS Experience Builder (Developer Edition version 1.16) was employed in the app’s development. A custom function was developed to meet the requirements of the geo-questionnaire fully. The application was ultimately deployed within the CENAGIS domain of the IT infrastructure at Warsaw University of Technology. Authors employed the structural equation modeling (SEM) method and provided statistical analysis of community expectations. The findings provide actionable evidence for urban planners, campus managers, and decision-makers seeking to implement data-driven, participatory revitalization strategies, demonstrating how social geoparticipation can directly inform sustainable design and policy-making at both campus and city levels. Full article

Geospatial Artificial Intelligence (GeoAI) offers a scalable solution for automating the generation and updating of volunteered geographic information (VGI) maps—addressing the limitations of manual contributions to crowd-source mapping platforms such as OpenStreetMap (OSM). This study evaluates the accuracy of GeoAI-generated buildings specifically, using two Generative Adversarial Network (GAN) models. These are OSM-GAN—trained on OSM vector data and Google Earth imagery—and OSi-GAN—trained on authoritative “ground truth” Ordnance Survey Ireland (OSi) vector data and aerial orthophotos. Altogether, we assess map feature completeness, shape accuracy, and positional accuracy and conduct qualitative visual evaluations using live OSM database features and OSi map data as a benchmark. The results show that OSi-GAN achieves higher completeness (88.2%), while OSM-GAN provides more consistent shape fidelity (mean HD: 3.29 m; σ = 2.46 m) and positional accuracy (mean centroid distance: 1.02 m) compared to both OSi-GAN and the current OSM map. The OSM dataset exhibits moderate average deviation (mean HD 5.33 m) but high variability, revealing inconsistencies in crowd-source mapping. These empirical results demonstrate the potential of GeoAI to augment manual VGI mapping workflows to support timely downstream applications in urban planning, disaster response, and many other location-based services (LBSs). The findings also emphasize the need for robust Quality Assurance (QA) frameworks to address “AI slop” and ensure the reliability and consistency of GeoAI-generated data. Full article

The increasing use of Global Positioning System (GPS) tools reshapes nature-based recreational practices. While previous research has examined the role of GPS technologies in outdoor recreation, limited attention has been given to the specific factors driving GPS use in nature-based settings such as natural parks. This case study examines the sociodemographic, behavioural, motivational and experiential factors influencing GPS use among visitors to the Capçaleres del Ter i del Freser Natural Park (Catalonia, Spain). A structured visitor survey (n = 999) was conducted over a one-year period and a hierarchical binary logistic regression model was applied to evaluate the explanatory contribution of four sequential variable blocks. The results showed that the behavioural factors (i.e., physical activity intensity) emerged as the strongest predictor of GPS use. Additionally, the final model demonstrated that visitors who were younger, engaged in higher-intensity physical activities, motivated by health-related goals, undertook longer routes, and reported more positive experiences were significantly more likely to use GPS tools during their visit. These findings highlight the need to adapt communication strategies to diverse visitor profiles and leverage volunteered geographic information (VGI) for improved visitor monitoring, flow management, and adaptive conservation planning. Full article

The availability of user-generated spatial data (user spatial content, USC) has transformed social science research, enabling the real-time, large-scale exploration of socio-spatial dynamics. This article traces the evolution from volunteered geographic information (VGI) to USC, highlighting their multidimensional nature and epistemological significance. Brief examples underscore USC’s potential for capturing the interplay between territorial factors, digital activity, and social phenomena, ranging from mapping urban vitality to tracking large-scale crises. However, the recent tightening of data access in the post-API era demands a rethinking of research approaches. Alternatives such as data donation, dedicated applications, and geoparsing can maintain the viability of USC-driven analyses. Overall, this article underlines the need for diversified, ethical, and methodologically sound strategies to harness USC’s value in understanding the digitally intertwined realities of contemporary society. Full article

Visualization and interpretation of user-generated spatial content such as Volunteered Geographic Information (VGI) is challenging because it combines enormous data volume and heterogeneity with a spatial bias. When dealing with point data on a map, these characteristics can lead to point clutter, reducing the readability of the map product and misleading users to false interpretations of patterns in the data, e.g., regarding specific clusters or extreme values. With this work, we provide a framework that is able to generalize point data, preserving spatial clusters and extreme values simultaneously. The framework consists of an agent-based generalization model using predefined constraints and measures. We present the architecture of the model and compare the results with methods focusing on extreme value preservation as well as clutter reduction. As a result, we can state that our agent-based model is able to preserve elementary characteristics of point datasets, such as the point density of clusters, while also retaining the existing extreme values in the data. Full article

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or volunteered geographic information (VGI). Key challenges with such “opportunistic” data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time. Full article

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types. Full article

This study proposes planning for children’s independent mobility through geoinformation technologies by listening to children. This research assumes that children’s values and expectations must be considered in city planning. A bibliographic review identified 15 indicators which make spaces safe and attractive for children to circulate and play. Thematic maps of the indicators were prepared and integrated by a multicriteria analysis by the weights of the evidence according to the hierarchical importance of each variable. The definition of the weights considered the opinions of the children and technicians. The consultation with children was carried out by mapping volunteers (VGI), a consultation on hierarchy, the geodesign of ideas for the area, and an artistic workshop. In the technical study, the query applied the Delphi method. It used the VGI—Volunteered Geographic Information—web-based platform, where children recorded places of topophilia and topophobia, while technicians mapped the presence of 15 indicators. The set of information was made available on a web-based platform called SDI—Spatial Data Infrastructure—in which there are resources for a geodesign workshop where ideas for the area were elaborated through negotiation and cocreation. The product is a transformational design for the area through urban design and the parameterization of its uses. Full article

Natural hazards such as wildfires have proven to be more frequent in recent years, and to minimize losses and activate emergency response, it is necessary to estimate their impact quickly and consequently identify the most affected areas. Volunteered geographic information (VGI) data, particularly from the social media platform Twitter, now X, are emerging as an accessible and near-real-time geoinformation data source about natural hazards. Our study seeks to analyze and evaluate the feasibility and limitations of using tweets in our proposed method for fire area assessment in near-real time. The methodology involves weighted barycenter calculation from tweet locations and estimating the affected area through various approaches based on data within tweet texts, including viewing angle to the fire, road segment blocking information, and distance to fire information. Case study scenarios are examined, revealing that the estimated areas align closely with fire hazard areas compared to remote sensing (RS) estimated fire areas, used as pseudo-references. The approach demonstrates reasonable accuracy with estimation areas differing by distances of 2 to 6 km between VGI and pseudo-reference centers and barycenters differing by distances of 5 km on average from pseudo-reference centers. Thus, geospatial analysis on VGI, mainly from Twitter, allows for a rapid and approximate assessment of affected areas. This capability enables emergency responders to coordinate operations and allocate resources efficiently during natural hazards. Full article

OpenStreetMap (OSM) is among the most prominent Volunteered Geographic Information (VGI) initiatives, aiming to create a freely accessible world map. Despite its success, the data quality of OSM remains variable. This study begins by identifying the quality metrics proposed by earlier research to assess the quality of OSM building footprints. It then evaluates the quality of OSM building data from 2018 and 2023 for five cities within Québec, Canada. The analysis reveals a significant quality improvement over time. In 2018, the completeness of OSM building footprints in the examined cities averaged around 5%, while by 2023, it had increased to approximately 35%. However, this improvement was not evenly distributed. For example, Shawinigan saw its completeness surge from 2% to 99%. The study also finds that OSM contributors were more likely to digitize larger buildings before smaller ones. Positional accuracy saw enhancement, with the average error shrinking from 3.7 m in 2018 to 2.3 m in 2023. The average distance measure suggests a modest increase in shape accuracy over the same period. Overall, while the quality of OSM building footprints has indeed improved, this study shows that the extent of the improvement varied significantly across different cities. Shawinigan experienced a substantial increase in data quality compared to its counterparts. Full article