Advertisement

Estimating Completeness of VGI Datasets by Analyzing Community Activity Over Time Periods

  • Simon Gröchenig
  • Richard Brunauer
  • Karl Rehrl
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Due to the dynamic nature and heterogeneity of Volunteered Geographic Information (VGI) datasets a crucial question isu concerned with geographic data quality. Among others, one of the main quality categories addresses data completeness. Most of the previous work tackles this question by comparing VGI datasets to external reference datasets. Although such comparisons give valuable insights, questions about the quality of the external dataset and syntactic as well as semantic differences arise. This work proposes a novel approach for internal estimation of regional data completeness of VGI datasets by analyzing the changes in community activity over time periods. It builds on empirical evidence that completeness of selected feature classes in distinct geographical regions may only be achieved when community activity in the selected region runs through a well-defined sequence of activity stages beginning at the start stage, continuing with some years of growth and finally reaching saturation. For the retrospective calculation of activity stages, the annual shares of new features in combination with empirically founded heuristic rules for stage transitions are used. As a proof-of-concept the approach is applied to the OpenStreetMap History dataset by analyzing activity stages for 12 representative metropolitan areas. Results give empirical evidence that reaching the saturation stage is an adequate indication for a certain degree of data completeness in the selected regions. Results also show similarities and differences of community activity in the different cities, revealing that community activity stages follow similar rules but with significant temporal variances.

Keywords

Street Network Community Activity Feature Class Activity Stage Reference Dataset 
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.

Notes

Acknowledgments

This work was partly funded by the Austrian Federal Ministry for Transport, Innovation and Technology. We thank Pascal Neis for providing the delimitations of the twelve world regions defined in Neis et al. (2013).

References

  1. Arsanjani JJ, Helbich M, Bakillah M, Loos L (2013) The emergence and evolution of OpenStreetMap: a cellular automata approach. Int J Digit Earth 1–30Google Scholar
  2. Barron C, Neis P, Zipf A (2014) A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans GIS. doi: 10.1111/tgis.12073
  3. Corcoran P, Mooney P, Bertolotto M (2013) Analysing the growth of OpenStreetMap networks. Spat Stat 3:21–32CrossRefGoogle Scholar
  4. Girres J, Touya G (2010) Quality assessment of the french OpenStreetMap dataset. Trans GIS 14(4):435–459CrossRefGoogle Scholar
  5. Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69: 211–221Google Scholar
  6. Goodchild MF, Li L (2012) Assuring the quality of volunteered geographic information. Spat Stat 1:110–120CrossRefGoogle Scholar
  7. Hagenauer J, Helbich M (2012) Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int J Geogr Inf Sci 26(6):963–982CrossRefGoogle Scholar
  8. Haklay M, Weber P (2008) OpenStreetMap: user-generated street maps. IEEE Pervasive Comput 7(4):12–18CrossRefGoogle Scholar
  9. Haklay M (2010) How good is volunteered geographical information? a comparative study of OpenStreetMap and ordnance survey datasets. Environ Plan B, Plan Des 37(4):682–703CrossRefGoogle Scholar
  10. Haklay M, Basiouka S, Antoniou V, Ather A (2010) How many volunteers does it take to map an area well? the validity of linus’ law to volunteered geographic information. Cartographic J 47(4):315–322CrossRefGoogle Scholar
  11. Hecht R, Kunze C, Hahmann S (2013) Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int J Geo-Inf 2(4):1066–1091CrossRefGoogle Scholar
  12. ISO (2011) Geographic information—data quality (ISO/DIS 19157:2011)Google Scholar
  13. Mondzech J, Sester M (2011) Quality analysis of OpenStreetMap data based on application needs. Cartographica 46(2):115–126CrossRefGoogle Scholar
  14. Mooney P, Corcoran P, Winstanley AC (2010) Towards quality metrics for OpenStreetMap. In: 18th ACM SIGSPATIAL international conference on advances in geographic information systemsGoogle Scholar
  15. Neis P, Zielstra D, Zipf A (2012) The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007–2011. Future Internet 4(1):1–21Google Scholar
  16. Neis P, Zielstra D, Zipf A (2013) Comparison of volunteered geographic information data contributions and community development for selected world regions. Future Internet 5(2):282–300CrossRefGoogle Scholar
  17. OSM Wiki (2013a) Map features. http://wiki.openstreetmap.org/wiki/Map_Features. Accessed 14 Nov 2013
  18. OSM Wiki (2013b) Vienna OSM coverage. http://wiki.openstreetmap.org/wiki/Vienna_OSM_Coverage. Accessed 03 Dec 2013
  19. Rehrl K, Gröchenig S, Hochmair H, Leitinger S, Steinmann R, Wagner A (2012) A conceptual model for analyzing contribution patterns in the context of VGI. In: LBS 2012–9th symposium on location based services. Springer, BerlinGoogle Scholar
  20. Steinmann R, Brunauer R, Gröchenig S, Rehrl K (2013a) Wie aktiv sind freiwillige Mapper? In: Angewandte Geoinformatik 2013. Beiträge zum 25. AGIT-Symbosium Salzburg, pp 173–182Google Scholar
  21. Steinmann R, Gröchenig S, Rehrl K, Brunauer R (2013b) Contribution profiles of voluntary mappers in OpenStreetMap. In: Online proceedings of the international workshop on action and interaction in volunteered geographic information, 16th AGILE conferenceGoogle Scholar
  22. Strano E, Nicosia V, Porta S, Barthélemy M (2012) Elementary processes governing the evolution of road networks. Sci Rep 2:296Google Scholar
  23. Suh B, Convertino G, Chi EH, Pirolli P (2009) The singularity is not near: slowing growth of Wikipedia. In: WikiSym ’09 proceedings of the 5th international symposium on Wikis and open collaborationGoogle Scholar
  24. Zielstra D, Hochmair HH (2011) A comparative study of pedestrian accessibility to transit stations using free and proprietary network data. J Transp Res Board 2117:145–152CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simon Gröchenig
    • 1
  • Richard Brunauer
    • 1
  • Karl Rehrl
    • 1
  1. 1.Salzburg Research Forschungsgesellschaft mbHSalzburgAustria

Personalised recommendations