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

1. A Practical Approach Towards Quality Assessment of Spatial Data and how it can be automated Antti Jakobsson, MatthewBeare, Jorma Marttinen, EarlingOnstein, LysandrosTsoulos, Frederique Williams ICC Paris 2011

3. Started in September 2008 and was run for 30 months until March 2011

4. Coordinated by EuroGeographics with 20 project partnersLantmäteriet Statenskartverk Helsinki University of Technology IGN Belgium Interactive Instruments EDINA, University Edinburgh The FinnishGeodetic Institute National Land Survey of Finland 1Spatial Universität Berlin Kadaster Geodan Software Development & Technology Bundesamt für Kartographie und Geodäsie EuroGeographics Institute of Geodesy, Cartography and Remote Sensing IGN France Bundesamt für Eich- und Vermessungswesen National Agency for Cadastre and Real Estate Publicity Romania National Technical University of Athens

5. The ESDIN messages 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 Quality evaluation has to be done after every phase in SDIs, after the transformation process, edge-matching, generalization… but again these may be automated Use of common measures for Annex I, II and III themes is crucial so that usability can be evaluated 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 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changes these results should be re-evaluated – e.g. if road geometry is changed original positional accuracy result are no longer valid, or if level of details are not considered then completeness can not be reported

6. Data flow in SDIs QualityEvaluation QualityEvaluation Automation

7. Key sucesses of the ESDIN in context of quality Utilization of International and Open standards Common understanding of what quality means in respect to the target specifications and user requirements and How to measure it ! Provision of these results in metadata Automation of the quality evaluation services

8. Benefits Data consumers Data providers Better harmonisation; Improved spatial analysis; Confident decision making; Data that is trusted and usable. Early data error detection; Faster product turnaround; Reduced maintenance costs; Consistent evaluation procedures

9. ESDIN approach

10. ESDIN approach to quality

11. ESDIN approach to quality

12. Quality Spreadsheets

13. Sampling/Full Inspection

14. Testing plans 12

15. How to utilize the quality model Quality model will be transformed to a rule set and conformance levels ELF specifications will include these for the NMCAs Automated tools utilizing the rule and conformance levels

16. Quality requirements/Conformance levels To set the requirements use the quality measures To consider the nature of reality Feature vagueness Change rates Themes Suggested guidance for positional accuracy Suggestion on setting the classification of conformance levels

17. Positional accuracy

18. Quality evaluation Process 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 Step 2: Reporting the score for each measure in a report form for each measure Step 3: Comparing the result from step two to the defined conformance level In addition, two continuing steps can be done: Step 4: Summarizing the conformance results into one result for each for each data quality elements Step 5: Summarising the results from step 4 into one overall dataset result

19. Grading data example

20. ESDIN approach to quality

21. Where you utilize quality webservices? If you are a data provider for SDI For quality control during production (automated) called here conformance testing (this includes edge-matching and generalization) For quality evaluation after the production (semi-automated) If you are the SDI co-ordinator or data custodian For quality audit for process accreditation or data certification doing either conformance testing and/or quality evalution If you are customer or data user To evaluate usability using metadata information

24. Quality Evaluation Service Rule Builder:Intuitive user interface to author, agree and manage DQ measures. DQ Client Application: Accessible, easy to use, automatic Data Quality Evaluation Service Collaborative Web-based Rule Authoring Data Quality Evaluation Service SOAP HTTP DQ Rules Engine: W3C Web Services interface using open standards to describe & execute geospatial rule evaluation. Object Oriented Geospatial Rules Engine Web Services Interface Rule Repository: Data Quality Rules, derived and guided by Quality Model. Data for Evaluation Quality Measures Rulesets & Templates Database Business Rules Geospatial Data File Web Feature Service 22

25. Conclusions 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 Quality evaluation metadata should be available for automated conformance testing Introducing a quality model which uses a same principles for all Annex I themes -> we will suggest this a guideline for INSPIRE implementation Introducing comformance levels that can be evaluated using semi-automated or automated based on ISO standards Automation of quality evaluation and conformance testing can be done for all transformation related workflows including schema transformation, generalization and edge matching Significant saving potential in quality reporting and improvement of data

26. THANK YOU More information; www.esdin.eu