Abstract
The availability of technology and tools enables the public to participate in the collection, contribution, editing, and usage of geographic information, a domain previously reserved for mapping agencies or companies. The data of Volunteered Geographic Information (VGI) systems, such as OpenStreetMap (OSM), is based on the availability of technology and participation of individuals. However, this combination also implies quality issues related to the data: some of the contributed entities can be assigned to wrong or implausible classes, due to individual interpretation of the submitted data, or due to misunderstanding about available classes. In this paper we propose two methods to check the integrity of VGI data with respect to hierarchical consistency and classification plausibility. These methods are based on constraint checking and machine learning methods. They can be used to check the validity of data during contribution or at a later stage for collaborative manual or automatic data correction.
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Keywords
- Administrative Unit
- Volunteer Geographic Information
- Constraint Check
- Data Quality Assurance
- Receiver Operation Characteristic
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)
Coleman, D.J., Georgiadou, Y., Labonte, J., et al.: Volunteered geographic information: the nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research 4(1), 332–358 (2009)
Feick, R., Roche, S.: Valuing volunteered geographic information (VGI): Opportunities and challenges arising from a new mode of GI use and production. In: Proceedings of the 2nd GEOValue Workshop, HafenCity University Hamburg, Germany, pp. 75–79 (2010)
Zook, M., Graham, M., Shelton, T., Gorman, S.: Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy 2(2), 7–33 (2010)
Girres, J.F., Touya, G.: Quality assessment of the french OpenStreetMap dataset. Transactions in GIS 14(4), 435–459 (2010)
Ludwig, I., Voss, A., Krause-Traudes, M.: A comparison of the street networks of Navteq and OSM in Germany. In: Advancing Geoinformation Science for a Changing World, pp. 65–84. Springer (2011)
Neis, P., Zielstra, D., Zipf, A.: The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007–2011. Future Internet 4(1), 1–21 (2011)
Goodchild, M.F., Li, L.: Assuring the quality of volunteered geographic information. Spatial Statistics 1, 110–120 (2012)
Haklay, M.: How good is volunteered geographical information? a comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning. B, Planning & Design 37(4), 682 (2010)
Neis, P., Zielstra, D., Zipf, A.: Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet 5(2), 282–300 (2013)
Mooney, P., Corcoran, P.: The annotation process in OpenStreetMap. Transactions in GIS 16(4), 561–579 (2012)
Elwood, S., Goodchild, M.F., Sui, D.Z.: Researching volunteered geographic information: Spatial data, geographic research, and new social practice. Annals of the Association of American Geographers 102(3), 571–590 (2012)
Mülligann, C., Janowicz, K., Ye, M., Lee, W.C.: Analyzing the spatial-semantic interaction of points of interest in volunteered geographic information. In: Egenhofer, M., Giudice, N., Moratz, R., Worboys, M. (eds.) COSIT 2011. LNCS, vol. 6899, pp. 350–370. Springer, Heidelberg (2011)
Vandecasteele, A., Devillers, R.: Improving volunteered geographic data quality using semantic similarity measurements. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1(1), 143–148 (2013)
Schmid, F., Kutz, O., Frommberger, L., Kauppinen, T., Cai, C.: Intuitive and natural interfaces for geospatial data classification. In: Workshop on Place-Related Knowledge Acquisition Research (P-KAR), Kloster Seeon, Germany (2012)
Schmid, F., Frommberger, L., Cai, C., Dylla, F.: Lowering the barrier: How the what-you-see-is-what-you-map paradigm enables people to contribute volunteered geographic information. In: Proceedings of the 4th Annual Symposium on Computing for Development, p. 8. ACM (2013)
Schmid, F., Frommberger, L., Cai, C., Freksa, C.: What you see is what you map: Geometry-preserving micro-mapping for smaller geographic objects with mapit. In: Geographic Information Science at the Heart of Europe, pp. 3–19. Springer International Publishing (2013)
Arteaga, M.G.: Historical map polygon and feature extractor. In: Schmid, F., Kray, C. (eds.) Proceedings of ACM MapInteract, 1st International Workshop on Map Interaction. ACM (2013)
Neis, P., Zipf, A.: Analyzing the contributor activity of a volunteered geographic information project: The case of OpenStreetMap. ISPRS International Journal of Geo-Information 1(2), 146–165 (2012)
Barron, C., Neis, P., Zipf, A.: A comprehensive framework for intrinsic OpenStreetMap quality analysis. Transactions in GIS 18 (2014)
Tobler, W.R.: A computer movie simulating urban growth in the detroit region. Economic Geography 46, 234–240 (1970)
Devogele, T., Parent, C., Spaccapietra, S.: On spatial database integration. International Journal of Geographical Information Science 12(4), 335–352 (1998)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus (2006)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco (2011)
Cover, T., Hart, P.: Nearest Neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Kohavi, R., et al.: A study of Cross-Validation and Bootstrap for accuracy estimation and model selection. In: Proc. International Joint Conference on Artificial Intelligence (IJCAI), pp. 1137–1145 (1995)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)
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Ali, A.L., Schmid, F. (2014). Data Quality Assurance for Volunteered Geographic Information. In: Duckham, M., Pebesma, E., Stewart, K., Frank, A.U. (eds) Geographic Information Science. GIScience 2014. Lecture Notes in Computer Science, vol 8728. Springer, Cham. https://doi.org/10.1007/978-3-319-11593-1_9
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DOI: https://doi.org/10.1007/978-3-319-11593-1_9
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