Quality results esdin_ica


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How to measure quality in geoinformation. Presentation at the International Cartographic Conference, Paris 2011. Based on ESDIN project.

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Quality results esdin_ica

  1. 1. A Practical Approach Towards Quality Assessment of Spatial Data and how it can be automated<br />Antti Jakobsson, MatthewBeare, Jorma Marttinen, EarlingOnstein, LysandrosTsoulos, <br />Frederique Williams<br />ICC Paris 2011<br />
  2. 2. Kort & Matrikelstyrelsen<br />Whatwas ESDIN<br /><ul><li>Project partially funded by eContentplus programme
  3. 3. Started in September 2008 and was run for 30 months until March 2011
  4. 4. Coordinated by EuroGeographics with 20 project partners</li></ul>Lantmäteriet<br />Statenskartverk<br />Helsinki University of Technology <br />IGN Belgium<br />Interactive Instruments <br />EDINA, University Edinburgh <br />The FinnishGeodetic Institute <br />National Land Survey of Finland <br />1Spatial<br />Universität Berlin<br />Kadaster<br />Geodan Software Development &<br /> Technology <br />Bundesamt für Kartographie <br /> und Geodäsie <br />EuroGeographics<br />Institute of Geodesy, Cartography <br /> and Remote Sensing <br />IGN France <br />Bundesamt für Eich- und<br /> Vermessungswesen <br />National Agency for Cadastre and<br /> Real Estate Publicity Romania<br />National Technical University <br /> of Athens <br />
  5. 5. The ESDIN messages<br />Most of the quality assurance processes needed in SDIs can be automated bringing significant savings to the producers and improving quality for users” – and we have demonstrated how this can be done<br />Quality evaluation has to be done after every phase in SDIs, after the transformation process, edge-matching, generalization… but again these may be automated<br />Use of common measures for Annex I, II and III themes is crucial so that usability can be evaluated<br />Need for setting basic conformance levels for INSPIRE at target level-of-details if harmonized products for cross-border/pan-European/global use is required, minimum for INSPIRE would be meeting logical consistency<br />Quality results are dependent on product specification, if transformation process changes these results should be re-evaluated – e.g. if road geometry is changed original positional accuracy result are no longer valid, or if level of details are not considered then completeness can not be reported <br />
  6. 6. Data flow in SDIs<br />QualityEvaluation<br />QualityEvaluation<br />Automation<br />
  7. 7. Key sucesses of the ESDIN in context of quality<br />Utilization of International and Open standards<br />Common understanding of what quality means in respect to the target specifications and user requirements and <br />How to measure it !<br />Provision of these results in metadata<br />Automation of the quality evaluation services<br />
  8. 8. Benefits<br />Data consumers<br />Data providers<br />Better harmonisation;<br />Improved spatial analysis;<br />Confident decision making;<br />Data that is trusted and usable.<br />Early data error detection;<br />Faster product turnaround;<br />Reduced maintenance costs;<br />Consistent evaluation procedures<br />
  9. 9. ESDIN approach<br />
  10. 10. ESDIN approach to quality<br />
  11. 11. ESDIN approach to quality<br />
  12. 12. Quality Spreadsheets<br />
  13. 13. Sampling/Full Inspection<br />
  14. 14. Testing plans<br />12<br />
  15. 15. How to utilize the quality model<br />Quality model will be transformed to a rule set and conformance levels<br />ELF specifications will include these for the NMCAs<br />Automated tools utilizing the rule and conformance levels<br />
  16. 16. Quality requirements/Conformance levels<br />To set the requirements use the quality measures<br />To consider the nature of reality<br />Feature vagueness<br />Change rates<br />Themes<br />Suggested guidance for positional accuracy<br />Suggestion on setting the classification of conformance levels<br />
  17. 17. Positional accuracy<br />
  18. 18. Quality evaluation Process<br />Step 1: Applying the data quality measure to the data to be checked. The procedure for this is described in the the ISO19113/19114 standards<br />Step 2: Reporting the score for each measure in a report form for each measure<br />Step 3: Comparing the result from step two to the defined conformance level<br />In addition, two continuing steps can be done:<br />Step 4: Summarizing the conformance results into one result for each for each data quality elements<br />Step 5: Summarising the results from step 4 into one overall dataset result<br />
  19. 19. Grading data example<br />
  20. 20. ESDIN approach to quality<br />
  21. 21. Where you utilize quality webservices?<br />If you are a data provider for SDI<br />For quality control during production (automated) called here conformance testing (this includes edge-matching and generalization)<br />For quality evaluation after the production (semi-automated)<br />If you are the SDI co-ordinator or data custodian<br />For quality audit for process accreditation or data certification doing either conformance testing and/or quality evalution<br />If you are customer or data user<br />To evaluate usability using metadata information <br />
  22. 22.
  23. 23.
  24. 24. Quality Evaluation Service<br />Rule Builder:Intuitive user interface to author, agree and manage DQ measures.<br />DQ Client Application:<br />Accessible, easy to use, automatic Data Quality Evaluation Service <br />Collaborative<br />Web-based<br />Rule Authoring<br />Data Quality<br />Evaluation<br />Service<br />SOAP<br />HTTP<br />DQ Rules Engine:<br />W3C Web Services interface using open standards to describe & execute geospatial rule evaluation.<br />Object Oriented<br />Geospatial Rules Engine<br />Web<br />Services<br />Interface<br />Rule Repository:<br />Data Quality Rules, derived and guided by Quality Model.<br />Data for Evaluation<br />Quality Measures<br />Rulesets &<br />Templates<br />Database<br />Business<br />Rules<br />Geospatial<br />Data File<br />Web Feature<br />Service<br />22<br />
  25. 25. Conclusions<br />It is important that INSPIRE will give a platform for data quality information; minimum data quality comformance levels set and then ability to report other user community related conformance levels<br />Quality evaluation metadata should be available for automated conformance testing<br />Introducing a quality model which uses a same principles for all Annex I themes -> we will suggest this a guideline for INSPIRE implementation<br />Introducing comformance levels that can be evaluated using semi-automated or automated based on ISO standards <br />Automation of quality evaluation and conformance testing can be done for all transformation related workflows including schema transformation, generalization and edge matching<br />Significant saving potential in quality reporting and improvement of data<br />
  26. 26. THANK YOU<br />More information; www.esdin.eu<br />