Volunteered Geographic Information Research Articles

GIScience I: The rise, fragmentation, and future of VGI

Since the inception of volunteered geographic information in 2007, this area of study has seen a proliferation of terms and concepts representing diverse forms of user-generated geographic data and systems. Despite the rich development of VGI (volunteered geographic information) in geography, recent trends indicate a disjointed research field. This progress report critically examines the trajectory of VGI, mapping its journey from an emergent set of practices to a fragmented research domain. Moving forward, it is up to the research community to either reignite the interaction and integration required to build a subdiscipline of GIScience or allow this research domain to extinguish.

A fuzzy rough approach to analyze the significance of semantic levels for building tags in OpenStreetMap

AbstractIn the realm of volunteered geographic information (VGI), the existence of comparable tags, attributes, and values across diverse categories of geographic objects gives rise to major categorization challenges such as conceptual overlap and indiscernibility. Enhancing the semantic data retrieval of VGI relies on the semantic quality of descriptive content annotated for tagging geographic objects. The main focus of this study is analyzing the descriptive content of OpenStreetMap to assess the significance of semantic levels. The proposed methodology relies on fuzzy rough set calculations to determine the degrees of dependency and significance of semantic levels. Three indicators, namely, the significance of semantic levels, decreasing the heterogeneity of attributes, and replicability were defined and assessed for a subset of building‐related tags. Analyzing building‐related tags in OpenStreetMap unveiled the higher significance for simple object, similarity, purpose, and function levels. The value of decreasing the heterogeneity of attributes was calculated at 63%, and the average replicability indicator of important attributes was doubled. Based on the results, the significance of semantic levels was deemed fit to enhance semantic homogeneity and replicability.

Combining environmental-socio-economic data with volunteer geographic information for mapping flood risk zones in Zhengzhou, Henan Province, China

Due to the occurrence of extreme weather phenomena, flooding caused by precipitation has become a frequent, widespread, and highly destructive natural disaster. For densely populated urban areas, efficient identification of people's emergency rescue needs and mapping of high flood risk areas are particularly important for decision makers to formulate disaster relief strategies. In this study, we employed a machine learning approach along with volunteer geographic information (VGI)1 data and environmental-socio-economic indicators to map the flood risk area in Zhengzhou. Firstly, the sentiment analysis tool based on artificial intelligence platform initially identified the public's emergency rescue needs. Secondly, the crowd-sourcing approach and geographical approach were used to evaluate the VGI data quality and the variable importance measures (VIMs)2 method to optimize the evaluation indicators. Lastly, the flood risk area was classified in four categories: flood environmental susceptibility, flood-induced risk, flood exposure risk, and flood mitigation capacity, subsequently resulting in a comprehensive risk map of Zhengzhou. The results showed that the accuracy of flood risk assessment increased from 85.7 % to 92.4 % after VGI quality evaluation. The most important indicators affecting flood risk assessment were population density, river system and nighttime light intensity. The comprehensive flood risk map better reflected the spatial distribution of high-risk areas in Zhengzhou. It allows emergency responders to quickly identify areas that require urgent attention.

Citizens as Environmental Sensors: Noise Mapping and Assessment on Lemnos Island, Greece, Using VGI and Web Technologies

Noise caused by industrial activities, urban life and traffic has a negative impact on people's health and quality of life. Environmental noise mapping is an important tool to study and address this problem. Through specialized measurement and analysis techniques, it is possible to identify areas at high risk of noise and under-stand the extent of the problem. This information can be used to develop strategies to protect the population exposed to high noise levels. In this paper, a method for collecting, processing, managing and mapping spatial noise data based on Volunteered Geographic Information (VGI) and web mapping technologies is present-ed. The proposed method uses noise pollution data collected from citizens using mobile devices. This includes both on-site measurements and information on users' subjective perceptions, providing a comprehensive picture of the problem. This enables authorities to take into account the individual perceptions and needs of citizens when planning noise mitigation measures, which has not been the case in the inter-national literature to date. To achieve this goal, a prototype of a free and open-source tool called "Noise Pollution" was developed. The proposed methodology and the resulting system are intended to help improve the quality of life in noisy environments and protect the health of citizens. They provide a practical tool for collecting and analyzing noise data and enable the development of noise mitigation strategies for the benefit of the population exposed to high noise levels.

Process Analysis in Humanitarian Voluntary Geographic Information: the case of the HOT Tasking Manager

Abstract. Geographic information is vital for organising humanitarian campaigns and helping those in need. The leading Humanitarian OpenStreetMap Team (HOT) organises projects to create the necessary geographical information and connect to the organisations that need to make decisions on the ground. This work provides insights into project management dynamics and volunteers’ interaction with user interfaces in Volunteered Geographic Information (VGI) in a humanitarian context. We do so by conducting a process analysis of 746 completed, fully validated, and archived projects in the HOT Tasking Manager (HOT-TM) over the past two years. The analysis encompasses a process discovery stage from the perspectives of control flow, time, organisation, and outcome of the mapping tasks that comprise a project. The findings offer valuable implications for future project planning and execution in similar contexts. Our process mining exploration of the task states found a clear path that involves mapping and validation operations with minor deviations. However, we did find a major bottleneck from the mapping to the validation phase, which could reflect that validation capabilities are a scarce resource. Proactive notification for validators, artificial intelligence adoption for task planning, user interface redesign, and strategies for better harnessing the collective intelligence of volunteers could improve the process.

Beyond extraction accuracy: addressing the quality of geographical named entity through advanced recognition and correction models using a modified BERT framework

ABSTRACT In the realm of geospatial services and applications, the accuracy of address information is of utmost importance. Traditional methods of data collection, being both labor-intensive and costly, have prompted researchers to turn to Volunteered Geographic Information (VGI) for the extraction of Geographical Named Entity (GNE).Notwithstanding, prior studies have predominantly concentrated on enhancing extraction accuracy, while often overlooking the critical aspect of GNE quality. This study addresses this gap by employing a multifaceted approach. Initially, a Geographical Named Entity Semantic Model (GNESM) was constructed by improving the BERT framework and conducting ablation experiments on multiple influencing factors to verify its feasibility. Based on GNESM, a Geographical Named Entity Recognition Model (GNERM) was constructed by incremental pre-training with social media text data and fine-tuning to achieve a recognition accuracy of 90.9%. Subsequently, a Geographical Named Entity Error Correction Model (GNEECM) was constructed by training GNESM with standard GNE data and incorporating error detection and correction modules, achieving a remarkable accuracy of 96.6% in error detection and correction tasks. The experimental results convincingly demonstrate that the proposed identification and correction methods outperform all compared methods. Through the identification and correction process, this study successfully obtained high-quality GNE data, providing a reference for expanding standard address libraries and subsequent research on geographic named entity.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

Assessment of using artificial intelligence in completeness of Volunteer Geographic Information. A case study for Open Street Map (OSM) landuse data.

Assessment of using artificial intelligence in completeness of Volunteer Geographic Information. A case study for Open Street Map (OSM) landuse data.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

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.

Rapid natural hazard extent estimation from twitter data: investigation for hurricane impact areas

Natural hazards have occurred more frequently in the past years and pose a severe risk to human life. Their extents and, thereby, the most heavily affected areas must be estimated at the earliest to limit damages or initiate rescue services. For such estimations, a widely available data source, which is comparatively responsive to short-time changes, is needed and provided by volunteered geographic information (VGI) data. Tropical cyclones are natural hazard events that can cause enormous spatially extended damage. In this study, we introduce Machine Learning approaches such as Extremely Randomized Tree (ET) and Geographically Weighted Regression for estimating hurricane-impacted regions from VGI data. In addition to the general approximate track extent estimation, we also evaluate the possibilities of temporal estimation of track development from VGI data. Different scenarios are evaluated, and we find that the results mainly depend on the choice of the geographical splits for training and test data for the underlying regression task. Suitable splits lead to R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 99% in the best cases with the ET model. The estimation results are satisfying when considering the temporal aspect and represent a use-case scenario. Such a combination of Machine Learning approaches and VGI is a simple and fast approach for early natural hazard estimation.

An MIU-based deep embedded clustering model for urban functional zoning from remote sensing images and VGI data

Urban functional zoning offers valuable insights into urban morphology and sustainable development. However, the conventional fixed spatial units, such as blocks and grids, cannot easily capture the morphological characteristics inherent in functional union and separation during urban evolution. In this paper, by taking advantage of remote sensing images and geospatial big data, we propose a minimum identification unit (MIU)-based urban functional zoning model. This approach integrates the deep embedded clustering of buildings to generate the spatial unit segmentation, and then identifies the urban function by generating semantic vectors with the Word2Vec model. The effectiveness of the proposed method was tested in the city of Wuhan in China. The results highlight that MIUs provide a more flexible and suitable unit for segmenting urban functional zones compared to traditional street blocks. The proposed method is a feasible way to deal with the semantic redundancy of volunteered geographic information (VGI) data when identifying urban function, and the quality issue only has a significant impact on the minor functional types. Moreover, the building clustering results can effectively reveal the fine-scale urban structure, especially for the administration, manufacturing, and residential types. This demonstrates the potential of our approach in enhancing the understanding of urban morphology and supporting sustainable urban development.

A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics

ABSTRACT Understanding the participation characteristics of VGI (volunteered geographic information) contributors is important as their active participation and consistent data contribution are key to success of any VGI project. Existing studies on this matter focused primarily on deriving and interpreting participation patterns of contributor groups. There is a lack of investigation into the participation pattern of individual contributors, which can be complementary to a comprehensive understanding of VGI contributors’ participation characteristics. Building and using a custom web-based geovisualization framework, this study explores the individual-level participation characteristics of VGI contributors from the perspectives of spatial, temporal, thematic, and social interaction patterns. I conducted geovisual exploratory data analysis on VGI datasets from the iNaturalist biodiversity citizen science project to gain intuitions on the clustering and variabilities of participation patterns in iNaturalist, detect participation pattern shifts over time and form explanation hypotheses, and assess and develop metrics to measure participation. The geovisualization framework is expected to be generally applicable to other VGI datasets for exploring individual-level contributor participation patterns. This work is among the first efforts to explore individual-level VGI participation characteristics through geovisualization and geovisual analytics.

Spatiotemporal evolution analysis of OpenStreetMap buildings in the Yangtze River Delta of China based on Tree-like model

OpenStreetMap (OSM) is one of the most successful and well-known projects in the field of Volunteered Geographic Information (VGI). It has not only become supplement to basic mapping services and professional surveying data, but also a popular free spatial dataset in urban analysis. However, the completeness of OSM building data in China was relatively low in the past. As a result, there is a relatively small number of studies on OSM building data in China, especially in terms of spatial patterns and evolution of OSM building data. In response to this research gap, this article proposes an OSM building data evolution model that combines gravity model and tree model. The spatial distribution pattern and evolution trend of OSM building data in the Yangtze River Delta region of China from 2014 to 2020 were systematically studied, providing insights and references for the future development of OSM building data in China. The research results indicate: (1) From 2014 to 2020, OSM building data in various cities in the Yangtze River Delta region grew rapidly, with an average annual growth rate of 30%, but there were significant spatial differences; (2) In terms of space, due to the influence of urban economy, population, and education level, OSM buildings mainly evolve and develop along large cities, forming a local radial development structure around large cities. Shanghai-Suzhou-Wuxi-Nanjing-Xuzhou/Hefei is the main evolutionary path of OSM building data in the Yangtze River Delta region; (3) In terms of time series, the gap between cities gradually decreases with the development of time, and OSM buildings gradually expand and develop from large cities to small and medium-sized cities, with spatial differences gradually narrowing. However, when OSM buildings have a certain scale, the city size will limit the development of OSM buildings to some extent.

Surf Tourism in Cold Water Destinations: Discovering destinations impacted by changing waterfront usage through VGI mapping

Surf destinations have been ascribed to tropical locations in the past but are moving into more cold-water climates, such as the Maine coast. In order for sustainable policy to be created in places like this. understanding which beaches are impacted by increased surf tourism is the place to start. This was accomplished through an analysis of social media photos using Voluntary Geographic Information mapping techniques. Density maps were created using Flickr/SmugMug photos from Maine with surfing tags in five-year increments from 2004-2024. The analysis indicates an increased spread of surfing throughout the coast with a concentration in certain locations indicating these destinations should be the focus of future research.

Gönüllü coğrafi bilgi kullanılarak Dilek Yarımadası’ndaki doğa temelli rekreasyonel aktivitelerin haritalanması

Korunan alanlar, açık hava rekreasyonu ve doğaya dayalı turizm için başlıca destinasyonlar haline gelmiştir. Korunan alanların sürdürülebilir ve etkin yönetimi için rekreasyonel aktivitelerin konumsal ve zamansal verilerine ihtiyaç duyulmaktadır. Günümüzde kitle kaynaklı platformlardan elde edilen gönüllü coğrafi bilgi, doğa temelli rekreasyonel aktivitelerin zamansal ve konumsal analizi için kullanılmaktadır. Bu çalışmada, Küresel Navigasyon Uydu Sistemleri (GNSS) tabanlı gönüllü coğrafi bilgiler aracılığıyla Dilek Yarımadası’ndaki doğa temelli rekreasyonel aktivitelerin konumsal modellerinin haritalanması amaçlanmıştır. Wikiloc’tan elde edilen 653 adet doğa yürüyüşü, 59 adet dağ bisikleti ve 22 adet yol bisikleti aktivitesinin GNSS izleri kullanılarak Dilek Yarımadası’nın rekreasyonel kullanımı analiz edilmiştir. Çalışmanın sonuçları, GNSS tabanlı gönüllü coğrafi bilgilerin, doğa temelli rekreasyonel aktivitelerin haritalanmasında etkili bir araç olduğunu ve geleneksel veri kaynaklarına alternatif veya tamamlayıcı bir kaynak olarak kullanılabileceğini göstermiştir.

Geographic proximity and homophily effects drive social interactions within VGI communities: an example of iNaturalist

ABSTRACT Social interactions among online community members contributing to volunteered geographic information (VGI) are a key element and often crucial to VGI project success. Existing studies on VGI lack investigations into the patterns of social interactions within VGI communities and the drivers that may have shaped these patterns. This study bridges this gap by analyzing social interaction patterns in the iNaturalist citizen science project and exploring potential driving factors through social network analysis. The relationships between potential drivers of social interactions in iNaturalist (i.e. geographic distance, species taxon composition similarity, and land cover composition similarity) and the establishment, intensity, and clustering of interactions were examined. The results revealed that geographic proximity, common interests in species taxon categories, and shared preferences in observation environments are major drivers of inter-contributor species identification interactions in iNaturalist. These findings are supported by theories that explain the social forces behind social link formation. Geographic distance represents physical proximity, whereas species taxon composition similarity and land cover similarity reflect homophily effects. iNaturalist and many other VGI communities are spatially embedded social networks. The discovered interaction drivers in iNaturalist generally align with those in spatial social networks and are expected to be generalizable to VGI communities beyond iNaturalist.

Spatial-temporal behaviour of hikers in the southeastern margin of Qinghai-Tibet Plateau: insights from volunteered geographic information

Understanding the macroscopic spatial-temporal behaviour of hikers plays an important role in targeted planning, marketing and management for regional hiking tourism development. The southeastern margin of Qinghai-Tibet Plateau, with abundant outdoor tourism resources and several World Heritage Sites, was chosen as the study area, and the development characteristics of hiking tourism and the spatial-temporal behaviour of its hikers were explored by seasonal intensity index, kernel density analysis, line density analysis and K-means clustering with volunteered geographic information (VGI). The results showed that hikers differed significantly from traditional tourists in terms of travel time, travel mode and destination choice, which provide vital implications for sustainable policy-making and tourism management in general and in the southeastern margin of Qinghai-Tibet Plateau in particular.

THE POTENTIAL OF CROWDSOURCED ASSET MAPPING TECHNOLOGIES FOR SUPPORTING DEMENTIA CAREGIVERS

Abstract It is well-established that caregivers of people living with dementia (PLWD) often report having difficulty finding information about education and support that is available to them in their community. Caregiver support groups have demonstrated to be an effective approach to caregiver education through mutual support, though many caregivers are unable to participate in caregiver support groups due to time and geographic constraints. Crowdsourced technologies that rely on volunteered geographic information (VGI) are a viable way of community members to provide mutual support on a variety of issues, including disaster response, traffic conditions, health, and travel. These technologies often rely on a mobile app where users upload VGI that are of interest to others who use the app. There are benefits and drawbacks of crowdsourced technologies. For example, they may be an easy way for community members to share needed information, though there may be challenges in the quality of data that users provide. This presentation will report findings from a project that examined dementia caregivers’ interest in using crowdsourced mapping technologies to share information about services and resources in their community that they have found helpful. Data were collected from a diverse sample of dementia caregivers living in Alabama through an online survey that was administered between June and November of 2022. Overall, it was found that caregivers were interested in using a crowdsourcing technology for support and education. Additional findings provide guidance for developing a crowdsourcing protocol that is based on caregivers’ reported information needs.

Evaluating OSM Building Footprint Data Quality in Québec Province, Canada from 2018 to 2023: A Comparative Study

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.

Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Timely understanding of affected areas during disasters is essential for the implementation of emergency response activities. As one of the low-cost and information-rich volunteer geographic information, social media data can reflect geographic events through human behavior, which is a powerful supplementary source for fine-grained flood monitoring in urban areas. However, the value of social media data has not been fully exploited as potential location and water depth information may be embedded in both text and images. In this study, we propose a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on Sina Weibo. First, we construct a novel fine-grained location corpus specifically for urban flooding contexts. The corpus summarizes characteristics of address descriptions in flood-related Weibo texts, including standard address entities and spatial relationship entities, based on the named entity recognition (NER) model. Then, water depth information in texts and images is obtained based on different deep learning modules and fused at the decision level. Specifically, in text analysis module, we summarize and extract diverse descriptions of water depth, and in image analysis module, we develop a water level hierarchical mapping method. Finally, we analyze the spatio-temporal distribution characteristics and variation patterns of the extracted information to enhance situational awareness. Taking the urban flood occurred in Anhui, China as a case study, we find that the variation of flooding hotspot areas in Sina Weibo and rainfall centers show a significant spatial and temporal consistency, and the fusion of text and image-based information can facilitate dynamic perception of flood processes. The framework presented in this study provides a feasible way to implement refined situational awareness and spatio-temporal evolution analysis of urban floods at the city level in time.