Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information

  • Christoph Mülligann
  • Krzysztof Janowicz
  • Mao Ye
  • Wang-Chien Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6899)


With the increasing success and commercial integration of Volunteered Geographic Information (VGI), the focus shifts away from coverage to data quality and homogeneity. Within the last years, several studies have been published analyzing the positional accuracy of features, completeness of specific attributes, or the topological consistency of line and polygon features. However, most of these studies do not take geographic feature types into account. This is for two reasons. First, and in contrast to street networks, choosing a reference set is difficult. Second, we lack the measures to quantify the degree of feature type mis-categorization. In this work, we present a methodology to analyze the spatial-semantic interaction of point features in Volunteered Geographic Information. Feature types in VGI can be considered special in both, the way they are formed and the way they are applied. Given that they reflect community agreement more accurately than top-down approaches, we argue that they should be used as the primary basis for assessing spatial-semantic interaction. We present a case study on a spatial and semantic subset of OpenStreetMap, and introduce a novel semantic similarity measure based on the change history of OpenStreetMap elements. Our results set the stage for systems that assist VGI contributors in suggesting the types of new features, cleaning up existing data, and integrating data from different sources.


Point Process Semantic Similarity Semantic Distance Volunteer Geographic Information Point Pattern Analysis 
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.


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  1. 1.
    Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)CrossRefGoogle Scholar
  2. 2.
    Zielstra, D., Zipf, A.: A comparative study of proprietary geodata and volunteered geographic information for Germany. In: 13th AGILE International Conference on Geographic Information Science (2010)Google Scholar
  3. 3.
    Mooney, P., Corcoran, P., Winstanley, A.C.: Towards quality metrics for openstreetmap. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 514–517. ACM, New York (2010)Google Scholar
  4. 4.
    Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth 3(3), 231–241 (2010)CrossRefGoogle Scholar
  5. 5.
    Scheider, S., Possin, J.: Affordance-based algorithms for categorization of road network data. Technical report. University of Münster, Germany (2010)Google Scholar
  6. 6.
    Werder, S., Kieler, B., Sester, M.: Semi-automatic interpretation of buildings and settlement areas in user-generated spatial data. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2010, pp. 330–339. ACM, New York (2010)Google Scholar
  7. 7.
    Trame, J., Keßler, C.: Exploring the lineage of volunteered geographic information with heat maps. In: GeoViz 2011, Hamburg, Germany (2011)Google Scholar
  8. 8.
    Janowicz, K., Keßler, C., Schwarz, M., Wilkes, M., Panov, I., Espeter, M., Bäumer, B.: Algorithm, Implementation and Application of the SIM-DL Similarity Server. In: Fonseca, F.T., Rodríguez, A., Levashkin, S. (eds.) GeoS 2007. LNCS, vol. 4853, pp. 128–145. Springer, Heidelberg (2007)Google Scholar
  9. 9.
    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), 231–241 (2010)CrossRefGoogle Scholar
  10. 10.
    O’Sullivan, D., Unwin, D.: Geographic Information Analysis. Wiley, Chichester (2010)CrossRefGoogle Scholar
  11. 11.
    Kuhn, W.: Volunteered geographic information and GIScience. In: NCGIA, UC Santa Barbara, pp. 13–14 (2007)Google Scholar
  12. 12.
    Elwood, S.: Geographic information science: emerging research on the societal implications of the geospatial web. Progress in Human Geography 34(3), 349–357 (2010)CrossRefGoogle Scholar
  13. 13.
    Coleman, D., Georgiadou, Y., Labonte, J.: Volunteered Geographic Information: the nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research 4, 332–358 (2009)Google Scholar
  14. 14.
    Ahlqvist, O., Shortridge, A.: Characterizing land cover structure with semantic variograms. In: Progress in Spatial Data Handling, pp. 401–415 (2006)Google Scholar
  15. 15.
    Cressie, N.: Statistics for Spatial Data (Wiley Series in Probability and Statistics). Wiley-Interscience, Hoboken (1993)Google Scholar
  16. 16.
    Ripley, B.: The second-order analysis of stationary point processes. Journal of Applied Probability 13(2), 255–266 (1976)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Daley, D., Vere-Jones, D.: An introduction to the theory of point processes. Springer Series in Statistics (1988)Google Scholar
  18. 18.
    Besag, J.: Contribution to the discussion of Dr. Ripley’s paper. JR Stat. Soc. B 39, 193–195 (1977)MathSciNetGoogle Scholar
  19. 19.
    Diggle, P., Chetwynd, A., Häggkvist, R., Morris, S.: Second-order analysis of space-time clustering. Statistical Methods in Medical Research 4(2), 124 (1995)Google Scholar
  20. 20.
    Rodríguez, A., Egenhofer, M.: Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure. International Journal of Geographical Information Science 18(3), 229–256 (2004)CrossRefGoogle Scholar
  21. 21.
    Li, B., Fonseca, F.: Tdd - a comprehensive model for qualitative spatial similarity assessment. Spatial Cognition and Computation 6(1), 31–62 (2006)CrossRefGoogle Scholar
  22. 22.
    Raubal, M., Adams, A.: The semantic web needs more cognition. Semantic Web Journal 1(1-2), 69–74 (2010)Google Scholar
  23. 23.
    Schwering, A., Raubal, M.: Spatial relations for semantic similarity measurement. In: Akoka, J., Liddle, S.W., Song, I.Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.J., Kolp, M., Trujillo, J., Kop, C., Mayr, H. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 259–269. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Van Eck, N.J., Waltman, L.: How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology 60, 1635–1651 (2009)Google Scholar
  25. 25.
    Peters, H.P.F., van Raan, A.F.J.: Co-word-based science maps of chemical engineering. part i: Representations by direct multidimensional scaling. Research Policy 22(1), 23–45 (1993)CrossRefGoogle Scholar
  26. 26.
    Rip, A., Courtial, J.: Co-word maps of biotechnology: An example of cognitive scientometrics. Scientometrics 6, 381–400 (1984), doi:10.1007/BF02025827CrossRefGoogle Scholar
  27. 27.
    Zitt, M., Bassecoulard, E., Okubo, Y.: Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science. Scientometrics 47, 627–657 (2000), doi:10.1023/A:1005632319799CrossRefGoogle Scholar
  28. 28.
    Goovaerts, P.: Geostatistics for natural resources evaluation. Oxford University Press, USA (1997)Google Scholar
  29. 29.
    Janowicz, K.: The role of space and time for knowledge organization on the semantic web. Semantic Web Journal 1(1-2), 25–32 (2010)Google Scholar
  30. 30.
    Ye, M., Shou, D., Lee, W.C., Yin, P., Janowicz, K.: On the semantic annotation of places in location-based social networks. In: ACM SIGKDD (forthcoming, 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Mülligann
    • 1
  • Krzysztof Janowicz
    • 2
  • Mao Ye
    • 3
  • Wang-Chien Lee
    • 3
  1. 1.Institute for GeoinformaticsUniversity of MünsterGermany
  2. 2.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA
  3. 3.Department of Computer Science and EngineeringPennsylvania State UniversityUSA

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