Harmonization Process: Role of Data Providers. Rui Reis, Instituto Geográfico Português Dolors Barrot, Institut Cartogràfic de Catalunya

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1 Harmonization Process: Role of Data Providers Rui Reis, Instituto Geográfico Português Dolors Barrot, Institut Cartogràfic de Catalunya Gis4eu a Cooperative project to apply the INSPIRE directive - Krakow, June 22, 2010

2 GIS4EU DP Local Regional National Reling Aggregation Conclusions

3 DP Main activities Harmonization process State of the art data design Reling Aggregation Validation and dissemination Design Rules Implementation Rules Implementation Cognitive Implementation Validation Reling Aggregation Conclusions

4 data design Conceptual s Analysis Conceptual s Requirements Reling Aggregation Conclusions

5 DP: Impact analysis - Benefits data design Data revision -> test DP s datasets specs First contact with INSPIRE technical documents INSPIRE contribution Reling Aggregation Conclusions

6 DP: Impact analysis - Drawbacks data design Multilingual approaches GIS4EU DP mainly Mapping Agencies Reling Aggregation Conclusions

7 Reling Data s Matching Transformation Source data sets Rules Reled datasets Reling Aggregation Conclusions

8 Reling - Process Data Full and formal description of data s Lists of features and attributes Code lists Feature relations (UML diagrams) Structure and contents of Metadata Reling Aggregation Conclusions

9 Reling - Process Matching: rules definition In-depth knowledge of data s and their context Define rules to transforming DP s datasets and metadata to common data (INSPIRE) Transform: rules implementation Use of data reling software Reling Aggregation Conclusions

10 DP: Impact analysis - Benefits Reling Data revision -> test DP s datasets specs INSPIRE conformance level of DP datasets Personnel training about INSPIRE Directive Cost estimation Reling Aggregation Conclusions

11 DP: Impact analysis - Drawbacks Reling Exact matching is not the most frequent situation. INSPIRE data s were not the final ones Metadata (structure and contents) A gradual adaptation of current Member States data s can be foreseen in order to fulfil the INSPIRE Data Specification in the near future. Reling Aggregation Conclusions

12 Aggregation Cross-issues Merging & Degradation Transformation Reled datasets Rules Aggregated data set Reling Aggregation Conclusions

13 Aggregation - Process Cross-issues Identify datasets characteristics Determine common scales Data visibility criteria Define common portrayal Identify pairs of topologically related datasets Multiple representations of world phenomena Geometry matching issues Agree common representation Reling Aggregation Conclusions

14 Aggregation - Process Cross-issues Identify Cross-border inconsistencies Inconsisten cy Rule D3.6 Elements outside the administr ative area Overlap and gap surfaces Overlap curve and surface Gap curves and surface Topology Overlap curves Gap curves Inconsisten cy because different scale Different identifier Different names RVEN- INSIEL X RLIG-CGE X X X X RLIG-RPIE VUGK-FOMI X X Reling Aggregation Conclusions

15 Aggregation - Process Merging & degradation: rules definition Define generalization rules to be applied at each dataset feature Agree on merging rules to be applied to solve inconsistencies Transform: rules implementation Use of software tools to harmonize reled datasets Reling Aggregation Conclusions

16 DP: Impact analysis - Benefits Aggregation Know-how on the harmonization processes Cost estimation Reling Aggregation Conclusions

17 DP: Impact analysis - Drawbacks Aggregation Absence of recommendations in the INSPIRE documents to deal with different levels of detail Lack of a procedure (not technical) to solve crossborder inconsistencies Tools for automatic generalization

18 Conclusions GIS4EU project has been a real scenario of the situation that INSPIRE will face when the Implementing Rules enter into force. The methodology used in the project and the experience achieved is a starting point to deal with INSPIRE adoption. Reling Aggregation Conclusions

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