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Improving Volunteered Geographic Information Quality Using a Tag Recommender System: The Case of OpenStreetMap

Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Studies have analyzed the quality of volunteered geographic information (VGI) datasets, assessing the positional accuracy of features and the semantic accuracy of the attributes. While it has been shown that VGI can, in some contexts, reach a high positional accuracy, these studies have also highlighted a large spatial heterogeneity in positional accuracy and completeness, but also concerning the semantics of the objects. Such high semantic heterogeneity of VGI datasets becomes a significant obstacle to a number of possible uses that could be made of the data. This paper proposes an approach for both improving the semantic quality and reducing the semantic heterogeneity of VGI datasets. The improvement of the semantic quality is achieved by using a tag recommender system, called OSMantic, which automatically suggests relevant tags to contributors during the editing process. Such an approach helps contributors find the most appropriate tags for a given object, hence reducing the overall dataset semantic heterogeneity. The approach was implemented into a plugin for the Java OpenStreetMap editor (JOSM) and different examples illustrate how this plugin can be used to improve the quality of VGI data. This plugin has been tested by OSM contributors and evaluated using an online questionnaire. Results of the evaluation suggest a high level of satisfaction from users and are discussed.

Keywords

  • Volunteered geographic information (VGI)
  • Semantic similarity
  • Data quality
  • OpenStreetMap (OSM)

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Notes

  1. 1.

    OpenStreetMap Website: http://openstreetmap.org/ (Accessed 3 October 2014).

  2. 2.

    OpenStreetMap Statistics: http://wiki.openstreetmap.org/wiki/Stats (Accessed 3 October 2014).

  3. 3.

    DBPedia Website: http://dbpedia.org/ (Accessed 3 October 2014).

  4. 4.

    GeoNames Website: http://www.geonames.org/ (Accessed 3 October 2014).

  5. 5.

    OSM Wiki: http://wiki.openstreetmap.org/wiki/Map_Features (Accessed 3 October 2014).

  6. 6.

    OSM Wiki Proposed Features “Abandoned”: http://wiki.openstreetmap.org/wiki/Category:Proposed_features_%22Abandoned%22 (Accessed 3 October 2014).

  7. 7.

    OSMantic Wiki page: https://wiki.openstreetmap.org/wiki/JOSM/Plugins/OSMantic (Accessed 3 October 2014).

  8. 8.

    Editor usage statistics: http://wiki.openstreetmap.org/wiki/Editor_usage_stats (Accessed 3 October 2014).

  9. 9.

    OpenStreetMap Presets: https://josm.openstreetmap.de/wiki/TaggingPresets (Accessed 3 October 2014).

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Acknowledgments

This research was funded by the Natural Science and Engineering Research Council of Canada (NSERC) through the second author’s NSERC Discovery Accelerator Supplement Program. Authors also thank Dr. Andrea Ballatore for sharing his results, Vincent Privat for his helpful feedback on JOSM, Daniel Bégin for his early tests of the plugin and the 30 OSM contributors that participated in the evaluation of the plugin.

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Correspondence to Arnaud Vandecasteele .

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Vandecasteele, A., Devillers, R. (2015). Improving Volunteered Geographic Information Quality Using a Tag Recommender System: The Case of OpenStreetMap. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M. (eds) OpenStreetMap in GIScience. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-14280-7_4

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