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

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


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.


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.



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).


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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

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