Search Results (145)

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

The spatial and semantic data of geographic addresses are extremely important for citizens, governments, and companies. The addresses can georeference environmental, economic, security, health, and demographic parameters in urban areas. Additionally, address components can be used by users to locate any point of interest (POI) with location-based systems (LBSs). For this reason, errors in address data can affect the geographic location of events, map representations, and spatial analyses. Thus, this paper presents the development of an algorithm for evaluating the quality of semantic and geographic information in any geospatial address dataset. The reference datasets are accessible using open data platforms or spatial data infrastructure (SDI) and volunteered geographic information (VGI), and both have been compared with commercial datasets using geocoding web services. Address quality analysis was developed using several open-source data science code libraries combined with spatial databases and geographic information systems. In addition, the quality of geographic addresses was evaluated by carrying out normalized tests in accordance with International Geospatial Standards (ISO 19157). Finally, this methodology assesses the quality of authorized and VGI address datasets that can be used for geocoding any relevant information in specific urban areas. Full article

Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model’s recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names. Full article

The latest advances in spatial information technology have led to the emergence of Volunteered Geographic Information (VGI) as enrichment to existing spatial data sources. Additionally, Decision Support Systems (DSS) are among the fields that have seen major advances. Volunteered Geographic Information (VGI) has great potential as a valuable data source to decision support systems. Several studies have been proposed to integrate VGI data into DSS. However, as VGI data may have different levels of quality, integrating VGI data with poor quality may affect the decision-making process. In fact, VGI data with poor quality. that are obsolete or incomplete, could, if integrated into a spatial DSS, lead to inappropriate analysis results. This paper presents an approach that aims to enhance spatial DSS analysis and exploitation by integrating high quality VGI data that are appropriate to the user requirements, and that have a good indicator completeness and time relevance. The approach introduces a conceptual framework that evaluates VGI data quality and integrates only high quality VGI data into spatial DSS. The proposed approach is experimented on a road maintenance project in Grand-Tunis. We develop the Map-Report prototype, and we evaluate the efficiency of our approach in enhancing data analysis and exploitation in spatial DSS by reducing the error rate and providing accurate and precise analysis results. Full article

Due to COVID-19 restrictions, many restaurants were forced to discontinue in-person service, either by locking down or finding alternative methods of operation. Despite the fact that, in the United States of America, digital restaurants have already been established for many years, in Greece, this phenomenon became popular during the pandemic. These delivery-only companies operate exclusively online, allowing customers to place orders from restaurants without a physical location. This has revolutionized the process of ordering food, as customers can browse digital menus, view images, and utilize other options provided by digital food technology. As a result, customers have had new experiences with food thanks to digital eateries during the pandemic. This research study is quantitative and utilized a questionnaire distributed to 1097 participating consumers over the internet. The sample was selected using straightforward random sampling, where each member of the population had an equal and unique chance of participating in the survey. The data were collected over a period of 2 months. Full article

Extreme weather events, such as flooding, are expected to increase in frequency and intensity. Therefore, the prediction of extreme weather events, assessment of their local impacts in urban environments, and implementation of adaptation measures are becoming high-priority challenges for local, regional, and national agencies and authorities. To manage these challenges, access to accurate weather warnings and information about the occurrence, extent, and impacts of extreme weather events are crucial. As a result, in addition to official sources of information for prediction and monitoring, citizen volunteered geographic information (VGI) has emerged as a complementary source of valuable information. In this work, we propose the formulation of an approach to complement the impact-based weather warning system that has been introduced in Sweden in 2021 by making use of such alternative sources of data. We present and discuss design considerations and opportunities towards the creation of a visual analytics (VA) pipeline for the identification and exploration of extreme weather events and their impacts from VGI texts and images retrieved from social media. The envisioned VA pipeline incorporates three main steps: (1) data collection, (2) image/text classification and analysis, and (3) visualization and exploration through an interactive visual interface. We envision that our work has the potential to support three processes that involve multiple stakeholders of the weather warning system: (1) the validation of previously issued warnings, (2) local and regional assessment-support documentation, and (3) the monitoring of ongoing events. The results of this work could thus generate information that is relevant to climate adaptation decision making and provide potential support for the future development of national weather warning systems. Full article

Volunteered geographic information (VGI) plays an increasingly crucial role in flash floods. However, topic classification and spatiotemporal analysis are complicated by the various expressions and lengths of social media textual data. This paper conducted applicability analysis on bidirectional encoder representation from transformers (BERT) and four traditional methods, TextRank, term frequency–inverse document frequency (TF-IDF), maximal marginal relevance (MMR), and linear discriminant analysis (LDA), and the results show that for user type, BERT performs best on the Government Affairs Microblog, whereas LDA-BERT performs best on the We Media Microblog. As for text length, TF-IDF-BERT works better for texts with a length of <70 and length >140 words, and LDA-BERT performs best with a text length of 70–140 words. For the spatiotemporal evolution pattern, the study suggests that in a Henan rainstorm, the textual topics follow the general pattern of “situation-tips-rescue”. Moreover, this paper detected the hotspot of “Metro Line 5” related to a Henan rainstorm and discovered that the topical focus of the Henan rainstorm spatially shifts from Zhengzhou, first to Xinxiang, and then to Hebi, showing a remarkable tendency from south to north, which was the same as the report issued by the authorities. We integrated multi-methods to improve the overall topic classification accuracy of Sina microblogs, facilitating the spatiotemporal analysis of flooding. Full article

The primary objective of this study was to examine the quality of volunteered geographic information (VGI) data for flood mapping of Hurricane Harvey. As a crowdsourcing platform, the U-Flood project mapped flooded streets in the Houston metro area. This research examines the following: (1) If there are any significant differences in water depth (WD) among the hydraulic and hydrologic (H&H) model, the Federal Emergency Management Agency (FEMA) reference floodplain map, and the VGI? (2) Are there any significant differences in the inundated areas between the floodplain modeled by the VGI and hydraulic simulation? This study used HEC-RAS to simulate flood inundation maps and validated the results with high water marks (HWM) and the FEMA-modeled floodplain after Hurricane Harvey. The statistical results showed that there were significant differences in the WD, the inundated road count, and the length inside/outside of HEC-RAS-modeled floodplain. The results also showed that a less consistent decreasing trend between the U-Flood data and the modeled floodplain over time and space. This study empirically evaluated the data quality of the VGI based on observed and modeled data in flood monitoring. The findings from this study fill the gaps in the literature by assessing the uncertainty and data quality of VGI, providing insights into using supplementary data in flood mapping research. Full article