Special Issue "OpenStreetMap as A Multi-Disciplinary Nexus: Perspectives, Practices and Procedures"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2021).

Special Issue Editors

Dr. A. Yair Grinberger
E-Mail Website
Guest Editor
Department of Geography, The Hebrew University of Jerusalem, Mount Scopus, 9190501 Jerusalem, Israel
Interests: GIScience; volunteered geographic information; geographical representation; social aspects of geo-information; mobility analysis; spatial agent-based models; urban dynamics
Dr. Marco Minghini
E-Mail
Guest Editor
European Commission-Joint Research Centre (JRC), Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
Interests: crowdsourcing, volunteered geographic information and OpenStreetMap; land cover/land use validation; open source geospatial software; geospatial interoperability; Spatial Data Infrastructures
Special Issues, Collections and Topics in MDPI journals
Dr. Peter Mooney
E-Mail Website
Guest Editor
Department of Computer Science, Maynooth University, Maynooth, W23 F2H6 Co. Kildare, Ireland
Interests: volunteered geographic information; open source geospatial software; user-generated geodata; geocomputation; citizen science; spatial databases; web-based GIS
Dr. Levente Juhász
E-Mail Website
Guest Editor
GIS Center, Florida International University, Miami. FL, USA
Interests: GIScience; user-generated geodata; spatial analysis; geocomputation; citizen science; volunteered geographic information; geospatial web
Dr. Godwin Yeboah
E-Mail Website
Guest Editor
Institute for Global Sustainable Development, University of Warwick, Coventry CV4 7AL, UK
Interests: GIScience; citizen science; spatial analysis; geomatics; open-source; geocomputation; human–environment interactions; urban informatics; sustainable development

Special Issue Information

Dear Colleagues,

We are excited to invite you to submit a research paper to “OpenStreetMap as a multi-disciplinary nexus: Perspectives, Practices and Procedures”, a Special Issue of the ISPRS International Journal of Geo-Information. The aim of this Special Issue is to showcase both the ongoing innovation and the maturity of scientific investigations and research into OpenStreetMap, demonstrating how, as a research object, it converges multiple research areas together. Collecting contributions from multiple disciplines and domains, the Special Issue will show how the sum total of investigations of issues like VGI, geo-information, and geo-digital processes and representations can shed light on the relations between crowds, real-world applications, technological developments, and scientific research.

This Special Issue is primarily aimed at collecting papers that extend the research works presented in the Academic Track of State of the Map 2019, held in Heidelberg (Germany) on September 21–23, 2019. However, other original submissions aligned with the area of research are also highly welcome.

We expect empirical, methodological, or conceptual contributions addressing any scientific aspect related to OpenStreetMap, in particular, but not limited, to the following:

  • Extrinsic and intrinsic quality assessment of OpenStreetMap data
  • Analysis of contribution patterns in OpenStreetMap
  • Interactions between OpenStreetMap and other data sources
  • Analysis/comparison of available software for scientific purposes related to OpenStreetMap
  • New approaches to facilitate or improve data collection in OpenStreetMap (e.g., through gamification or citizen science approaches)
  • Bridging the communities: Creating better connections and collaborations between the scientific community and the OpenStreetMap community
  • Open research problems in OpenStreetMap and challenges for the scientific community
  • Cultural, political, and organizational aspects of data production and usage practices in OpenStreetMap
  • Literature reviews and theoretical papers on any of the listed topics or topics related to the scope of the Special Issue

Dr. A. Yair Grinberger
Dr. Marco Minghini
Dr. Peter Mooney
Dr. Levente Juhász
Dr. Godwin Yeboah
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • OpenStreetMap
  • Volunteered geographic information
  • User-generated geospatial content
  • Contribution patterns
  • Quality assessment
  • Collaborative mapping
  • Data conflation

Published Papers (13 papers)

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Research

Article
On the Representativeness of OpenStreetMap for the Evaluation of Country Tourism Competitiveness
ISPRS Int. J. Geo-Inf. 2021, 10(5), 301; https://doi.org/10.3390/ijgi10050301 - 05 May 2021
Cited by 1 | Viewed by 710
Abstract
Since 2007, the World Economic Forum (WEF) has issued data on the factors and policies that contribute to the development of tourism and competitiveness across countries worldwide. While WEF compiles the yearly report out of data from governmental and private stakeholders, we seek [...] Read more.
Since 2007, the World Economic Forum (WEF) has issued data on the factors and policies that contribute to the development of tourism and competitiveness across countries worldwide. While WEF compiles the yearly report out of data from governmental and private stakeholders, we seek to analyze the representativeness of the open and collaborative platform OpenStreetMap (OSM) to the international tourism scene. For this study, we selected eight parameters indicative of the tourism development of each country, such as the number of beds or cultural sites, and we extracted the OSM objects representative of these indicators. Then, we performed a statistical and regression analysis of the OSM data to compare and model the data emitted by WEF with data from OSM. Our aim is to analyze the tourist representativeness of the OSM data with respect to official reports to better understand when OSM data can be used to complement the official information and, in some cases, when official information is scarce or non-existent, to assess whether the OSM information can be a substitute. Results show that OSM data provide a fairly accurate picture of official tourism statistics for most variables. We also discuss the reasons why OSM data is not so representative for some variables in some specific countries. All in all, this work represents a step towards the exploitation of open and collaborative data for tourism. Full article
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Article
Analysis of OpenStreetMap Data Quality at Different Stages of a Participatory Mapping Process: Evidence from Slums in Africa and Asia
ISPRS Int. J. Geo-Inf. 2021, 10(4), 265; https://doi.org/10.3390/ijgi10040265 - 14 Apr 2021
Cited by 1 | Viewed by 2024
Abstract
This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare [...] Read more.
This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84% in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums; the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required. Full article
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Article
Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions
ISPRS Int. J. Geo-Inf. 2021, 10(4), 251; https://doi.org/10.3390/ijgi10040251 - 09 Apr 2021
Cited by 5 | Viewed by 1491
Abstract
Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) [...] Read more.
Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model. Full article
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Article
The Impact of Community Happenings in OpenStreetMap—Establishing a Framework for Online Community Member Activity Analyses
ISPRS Int. J. Geo-Inf. 2021, 10(3), 164; https://doi.org/10.3390/ijgi10030164 - 14 Mar 2021
Cited by 3 | Viewed by 1671
Abstract
The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community [...] Read more.
The collaborative nature of activities in Web 2.0 projects leads to the formation of online communities. To reinforce this community, these projects often rely on happenings centred around data creation and curation activities. We suggest an integrated framework to directly assess online community member performance in a quantitative manner and applied it to the case study of OpenStreetMap. A set of mappers who participated in both field and remote mapping-related happenings was identified. To measure the effects of happenings, we computed attributes characterising the mappers’ contribution behaviour before and after the happenings and tested for significant impacts in relation to a control group. Results showed that newcomers to OpenStreetMap adopted a contribution behaviour similar to the contribution behaviour typical for the respective happening they attended: When contributing after the happening, newcomers who attended a remote mapping event tended to concentrate on creating new data with lower quality but high quantity in places foreign to their home region; newcomers who attended a field mapping event updated and enhanced existing local data with high accuracy. The behaviour of advanced mappers stayed largely unaffected by happenings. Unfortunately, our results did not reveal a positive effect on the community integration of newcomers through happenings. Full article
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Article
A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning
ISPRS Int. J. Geo-Inf. 2021, 10(3), 156; https://doi.org/10.3390/ijgi10030156 - 11 Mar 2021
Cited by 3 | Viewed by 868
Abstract
Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of experience or skills, which has raised concerns about quality. When reference data is not available to assess the quality of OSM data, intrinsic methods that assess the data and its [...] Read more.
Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of experience or skills, which has raised concerns about quality. When reference data is not available to assess the quality of OSM data, intrinsic methods that assess the data and its metadata can be used. In this study, we applied unsupervised machine learning for analysing OSM history data to get a better understanding of who contributed when and how in Mozambique. Even though no absolute statements can be made about the quality of the data, the results provide valuable insight into the quality. Most of the data in Mozambique (93%) was contributed by a small group of active contributors (25%). However, these were less active than the OSM Foundation’s definition of active contributorship and the Humanitarian OpenStreetMap Team (HOT) definition for intermediate mappers. Compared to other contributor classifications, our results revealed a new class: contributors who were new in the area and most likely attracted by HOT mapping events during disaster relief operations in Mozambique in 2019. More studies in different parts of the world would establish whether the patterns observed here are typical for developing countries. Intrinsic methods cannot replace ground truthing or extrinsic methods, but provide alternative ways for gaining insight about quality, and they can also be used to inform efforts to further improve the quality. We provide suggestions for how contributor-focused intrinsic quality assessments could be further refined. Full article
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Article
Development after Displacement: Evaluating the Utility of OpenStreetMap Data for Monitoring Sustainable Development Goal Progress in Refugee Settlements
ISPRS Int. J. Geo-Inf. 2021, 10(3), 153; https://doi.org/10.3390/ijgi10030153 - 10 Mar 2021
Cited by 1 | Viewed by 1261
Abstract
In 2015, 193 countries declared their commitment to “leave no one behind” in pursuit of 17 Sustainable Development Goals (SDGs). However, the world’s refugees have been routinely excluded from national censuses and representative surveys, and, as a result, have broadly been overlooked in [...] Read more.
In 2015, 193 countries declared their commitment to “leave no one behind” in pursuit of 17 Sustainable Development Goals (SDGs). However, the world’s refugees have been routinely excluded from national censuses and representative surveys, and, as a result, have broadly been overlooked in SDG evaluations. In this study, we examine the potential of OpenStreetMap (OSM) data for monitoring SDG progress in refugee settlements. We collected all available OSM data in 28 refugee and 26 nearby non-refugee settlements in the major refugee-hosting country of Uganda. We created a novel SDG-OSM data model, measured the spatial and temporal coverages of SDG-relevant OSM data across refugee settlements, and compared these results to non-refugee settlements. We found 11 different SDGs represented across 92% (21,950) of OSM data in refugee settlements, compared to 78% (1919 nodes) in non-refugee settlements. However, most data were created three years after refugee arrival, and 81% of OSM data in refugee settlements were never edited, both of which limit the potential for long-term monitoring of SDG progress. In light of our findings, we offer suggestions for improving OSM-driven SDG monitoring in refugee settlements that have relevance for development and humanitarian practitioners and research communities alike. Full article
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Article
The Sketch Map Tool Facilitates the Assessment of OpenStreetMap Data for Participatory Mapping
ISPRS Int. J. Geo-Inf. 2021, 10(3), 130; https://doi.org/10.3390/ijgi10030130 - 03 Mar 2021
Cited by 1 | Viewed by 1648
Abstract
A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this [...] Read more.
A worldwide increase in the number of people and areas affected by disasters has led to more and more approaches that focus on the integration of local knowledge into disaster risk reduction processes. The research at hand shows a method for formalizing this local knowledge via sketch maps in the context of flooding. The Sketch Map Tool enables not only the visualization of this local knowledge and analyses of OpenStreetMap data quality but also the communication of the results of these analyses in an understandable way. Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas. Full article
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Article
Worldwide Detection of Informal Settlements via Topological Analysis of Crowdsourced Digital Maps
ISPRS Int. J. Geo-Inf. 2020, 9(11), 685; https://doi.org/10.3390/ijgi9110685 - 16 Nov 2020
Cited by 3 | Viewed by 930
Abstract
The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion [...] Read more.
The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMICs) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities—from individual neighborhoods to global regions—that can coordinate local community knowledge with political agency, technical capability, and further research. Full article
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Article
Leveraging OSM and GEOBIA to Create and Update Forest Type Maps
ISPRS Int. J. Geo-Inf. 2020, 9(9), 499; https://doi.org/10.3390/ijgi9090499 - 21 Aug 2020
Cited by 3 | Viewed by 1257
Abstract
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The [...] Read more.
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation. Full article
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Article
Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators
ISPRS Int. J. Geo-Inf. 2020, 9(9), 498; https://doi.org/10.3390/ijgi9090498 - 21 Aug 2020
Cited by 4 | Viewed by 2697
Abstract
Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of [...] Read more.
Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities. Full article
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Article
Change Detection from Remote Sensing to Guide OpenStreetMap Labeling
ISPRS Int. J. Geo-Inf. 2020, 9(7), 427; https://doi.org/10.3390/ijgi9070427 - 02 Jul 2020
Cited by 1 | Viewed by 3036
Abstract
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in [...] Read more.
The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively. Full article
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Article
Quality Verification of Volunteered Geographic Information Using OSM Notes Data in a Global Context
ISPRS Int. J. Geo-Inf. 2020, 9(6), 372; https://doi.org/10.3390/ijgi9060372 - 06 Jun 2020
Cited by 3 | Viewed by 2256
Abstract
Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity [...] Read more.
Although the data obtained from volunteered geographic information (VGI) are inherently different from public surveys, the quantity of the data are vast and the quality of the data are often poor. To improve the quality of VGI data, the positional accuracy and diversity and interaction of the number of users involved in the regional generation of the data are important. This research proposes a new approach for the accumulation of OpenStreetMap (OSM) data by using OSM Notes and attempts to analyze the geographical distribution and the characteristics of the contents of the contributions, quantitatively and qualitatively. Subsequently, the results demonstrated regional differences in OSM Notes, but it provided users with an understanding of the new features of quality management in OSM, even in regions where OSM activities are not necessarily active. In addition, it was also possible to discover new factors such as the time transition required for the correction and contribution of anonymous users. These results are expected to serve as a tool for users to communicate with each other to resolve data bugs that exist in OSM and provide future researchers with examples of user interaction in global OSM activities. Full article
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Article
Cartographic Vandalism in the Era of Location-Based Games—The Case of OpenStreetMap and Pokémon GO
ISPRS Int. J. Geo-Inf. 2020, 9(4), 197; https://doi.org/10.3390/ijgi9040197 - 26 Mar 2020
Cited by 11 | Viewed by 6103
Abstract
User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As [...] Read more.
User-generated map data is increasingly used by the technology industry for background mapping, navigation and beyond. An example is the integration of OpenStreetMap (OSM) data in widely-used smartphone and web applications, such as Pokémon GO (PGO), a popular augmented reality smartphone game. As a result of OSM’s increased popularity, the worldwide audience that uses OSM through external applications is directly exposed to malicious edits which represent cartographic vandalism. Multiple reports of obscene and anti-semitic vandalism in OSM have surfaced in popular media over the years. These negative news related to cartographic vandalism undermine the credibility of collaboratively generated maps. Similarly, commercial map providers (e.g., Google Maps and Waze) are also prone to carto-vandalism through their crowdsourcing mechanism that they may use to keep their map products up-to-date. Using PGO as an example, this research analyzes harmful edits in OSM that originate from PGO players. More specifically, this paper analyzes the spatial, temporal and semantic characteristics of PGO carto-vandalism and discusses how the mapping community handles it. Our findings indicate that most harmful edits are quickly discovered and that the community becomes faster at detecting and fixing these harmful edits over time. Gaming related carto-vandalism in OSM was found to be a short-term, sporadic activity by individuals, whereas the task of fixing vandalism is persistently pursued by a dedicated user group within the OSM community. The characteristics of carto-vandalism identified in this research can be used to improve vandalism detection systems in the future. Full article
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