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ISESS 2005 Tools and Techniques

ISESS 2005 Tools and Techniques. Guidelines for good practises in GIS. Andrea Schukraft, Roman Lenz. - Outline-. Part 1: GIS data quality – a problem in planning processes? Part 2: What really is GIS data quality? Part 3: Steps for improvements

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ISESS 2005 Tools and Techniques

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  1. ISESS 2005Tools and Techniques Guidelines for good practises in GIS Andrea Schukraft, Roman Lenz

  2. - Outline- Part 1: GIS data quality – a problem in planning processes? Part 2: What really is GIS data quality? Part 3: Steps for improvements Part 4: The handbook for GIS data quality (management) Andrea Schukraft, Roman Lenz Page 2

  3. GIS data quality – a problem in planning processes? - Example 1 - The research project WAVES (TUM) in Brasil Beispiel mit freundlicher Genehmigung von Planungsbüro Voerkelius, Landshut Andrea Schukraft, Roman Lenz Page 3

  4. GIS data quality – a problem in planning processes? - Example 2 - Landscape plan on a municipality level Are the wet soils and potential habitats mapped correctly? The farmer said: no! (Scale...) Beispiel mit freundlicher Genehmigung von Planungsbüro Voerkelius, Landshut Andrea Schukraft, Roman Lenz Page 4

  5. GIS data quality – a problem in planning processes? - Example 3 - Data capture (digitising) from analogous basic data Different location errors of a waste water tube... again the context matters! (absolute vs. relative position) Beispiel mit freundlicher Genehmigung von Planungsbüro Voerkelius, Landshut Andrea Schukraft, Roman Lenz Page 5

  6. GIS data quality – a problem in planning processes? - Example 4 - Quality of the geometries Multiparts Andrea Schukraft, Roman Lenz Page 6

  7. What really is GIS data quality? - Reality and maps - • The map as • representation of the earth • as map sheet • representing only partial aspects • as a flash light representation • The earth • as globe • with people, land use, ... • in a permanent change Andrea Schukraft, Roman Lenz Page 7

  8. What really is GIS data quality? - Reality and maps - Quality is (after DIN) defined as „the completeness of attributes of a specific unit (product, service) according to its suitability, to fullfill fixed and presupposed needs" (DIN ISO 8402, 1992). There are no bad or good data! There are only data, which are suitable for a specific purpose!!! Andrea Schukraft, Roman Lenz Page 8

  9. Soft transitions or fuzzy borders in nature  distinct lines in maps • cartographic treatment • age of data • method of data capture • conversion into a more suitable data format • ... What really is GIS data quality? - Reasons for deviations/differences from reality - Andrea Schukraft, Roman Lenz Page 9

  10. What really is GIS data quality? - Categories of Spatial data quality - Precision (or resolution) Accuracy (and actuality) = degree of detail that can be displayed in space, time or theme = discrepancy between the encoded and actual value of a particular attribute Quality in Spatial Data Consistency Completeness = the absence of apparent contradictions in a database = lack of errors of omissions in a database Andrea Schukraft, Roman Lenz Page 10

  11. What really is GIS data quality? - A classification matrix of data quality - Andrea Schukraft, Roman Lenz Page 11

  12. What really is GIS data quality? - Meta-data - Meta-data are data about data • For example: • Description of Geo-data • Information about data quality • Information about data format (shape, tif, raster, ...) • Information about the spatial reference system (e. g. Gauß-Krüger) • Additional information (e. g. description of table contents) • Availability and sources (public or restricted, price, ...) • Contact address Andrea Schukraft – EDV Im Grünen Bereich - a.schukraft@edvimgruenenbereich.de Seite 12

  13. Minimum-Info What really is GIS data quality? - Meta-data - Title Source of data Map-Scale Date of publication Key words Purpose of data capture Actualitye.g mapping date Short summary Andrea Schukraft, Roman Lenz Page 13

  14. What really is GIS data quality? - Meta-data - Why do we need Meta-data? • Geo-data investigations • Description of own data, e.g. in the office etc. • Re-use of data e.g. after a longer period • After transmission of data, meta-data are an important information for the receiver • Institutions, who distribute/sell data, describe those in a catalog. The descriptions are meta-data • Notice: “science” or “theoretical” standards (like FGDC or ISO 19115) vs. reality… Andrea Schukraft, Roman Lenz Page 14

  15. Steps for improvements How do we solve the problems in practise/reality? Andrea Schukraft, Roman Lenz Page 15

  16. Quick check of spatial precision - Derive precision from origin - Precision of the resulting data set Original data Analysis

  17. Sample Implementation - Visualize precision -

  18. Steps for improvements - Example 4 - Quality of the geometries Andrea Schukraft, Roman Lenz Page 18

  19. Steps for improvements - Example 5 - Modelling flooding Andrea Schukraft, Roman Lenz Page 19

  20. Guidelines for good practices in GIS Handbook on GIS data quality (management) Andrea Schukraft, Roman Lenz Page 20

  21. Guidelines for good practices in GIS • Handbook - Andrea Schukraft, Roman Lenz Page 21

  22. Guidelines for good practices in GIS • Contents - Andrea Schukraft, Roman Lenz Page 22

  23. - Example 1 - Example 1: Quality of geometries • Quality criteria for geometries will be fixed, e.g. • for comparison of competing offers e.g. in public calls • for own data captures • for data transfer between several project partners Andrea Schukraft, Roman Lenz Page 23

  24. - Example 1 - Example 1: Quality of geometries Is it really necessary, to define and demand such trivial criteria? Yes !!! Andrea Schukraft, Roman Lenz Page 24

  25. - Example 2 - Example 2: Meta-data How did these results occur? Andrea Schukraft, Roman Lenz Page 25

  26. - Example 2 - Example 2: Meta-data Andrea Schukraft, Roman Lenz Page 26

  27. - Example 3 - Example 3: Pre-definition of digitising map-scale • Why are pre-definitions necessary? • in a large scale, too many points will be digitised, and the data set gets too big • in a small scale, too little • points will be digitised, and • hence the data set will not • be precise enough Andrea Schukraft, Roman Lenz Page 27

  28. - Example 3 - Example 3: Digitising Map-scale When is it meaningful to pre-define the map-scale for digitising? • - if different people are digitising • if data are captured over a longer period • if a third party is capturing and digitising the data Andrea Schukraft, Roman Lenz Page 28

  29. ISESS 2005Tools and Techniques Guidelines for good practises in GIS: www.rundertischgis.de Thanks for listening! Andrea Schukraft, Roman Lenz

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