Data harmonisation aspects in INSPIRE and beyond : use cases at pan-european level
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1 Data harmonisation aspects in INSPIRE and beyond : use cases at pan-european level Nathalie Delattre, IGN nathalie.delattre@ngi.be
2 INSPIRE data interoperability setting up common data specifications aimed to spatial data exchange common understanding common language cross-border data consistency provide data that are available. Expected result is : to have easy and interoperable access to existing data. 2
3 Data Harmonisation aspects in INSPIRE and beyond Data Interoperability level 1. Uniform Geodetic Reference System 2. Common data specifications and data model Data Matching level Data Harmonisation level 3. cross-border consistency ( at state boundaries) 4. Harmonisation in data content ( mandatory information) 5. Harmonisation in level of details (data density and selection criteria ) 6. Definition of European-wide classifications Roads Watercourses Land coverage 7. Handling transnational features Page 3
4 Use cases : EuroGeographics product,euroregionalmap Multifunctional topographical reference dataset Scale 1: Common and harmonised data model Authorized data sources from NMCAS (36 countries) Yearly update Production manager: IGN, Belgium + European Production Management Team Main European stakeholders: EC (Eurostat), EEA Common licensing and pricing condition
5 EuroRegionalMap (ERM) Thematic Layers: Administrative units Hydrography (rivers, lakes, sea, wetland, glacier) Transport Network (roads, railways, airports, ferry lines) Settlements (built-up areas) Vegetation and Soil (wood/forest, plantation, agricultural area) Miscellaneous objects (national parks, points of interest) Named Location (geographical names) Page 5
6 Chalenge in data harmonisation (1) fixing common state boundaries Set up rules for the agreement process between countries Before After Bleu: France ( BDCarto) Magenta: Allemagne (VG250) Commonly agreed 6
7 Chalenge in data harmonisation (3) determining common data content Data commonly provided ( mandatory, optional) TRANS RAILRDL AN010 Railway M EXS Existence Category M FCO Feature Configuration M GAW Gauge Width M LLE Location Level M NAMN1 Name in first national language O NAMN2 Name in second national language O NAMA1 Name in first national language (ASCII-7bit) O NAMA2 Name in second national language (ASCII-7bit) O NLN1 3-Char Language Code O NLN2 3-Char Language Code O RCO Railroad Code O RGC Railroad Gauge Category M RRA Railroad Power Source M RRC Railroad Categories M RSD Railway Speed Class M RSU Seasonal availability O TEN TransEuropean Transport Network M TUC Transportation Use Category M LEN Length in km M 7
8 Chalenge in data harmonisation (4) Harmonisation in data density no real rule, step by step approach based on consistency with other reference datasets (ECRINs river basins) Before after France France Spain Spain Page 8
9 Challenge in data harmonisation: (5):European-wide classification European définition is based on use cases (1) National classificatio ns (2) European proposal for mapping (3) TEN Legend Part of TEN-T network Limited Access/Motorway Primary Route Secondary Route Local Road 9
10 Challenge in data harmonisation (6) handling trans-national features Rules for unique identifiers ( creating European Unique Identifiers ) Germany France Switzerland 10
11 Producers and Users in data harmonisation process Data Users community: guidance Level of cross border data matching Data commonly available for Europe, For which use cases Which LoD Consistency with other reference datasets? which use cases Best compromise for harmonised data at European level ommon State boundaries Edge-matching rules Transnational features Mandatory data European classification Harmonised data density and Selection criteria Data modelling Data producers community: proposal Workshop IGN 25 mai
12 Producers and Users in data harmonisation process Continuous dialoguing Users give guidance,producers give proposals, Process negociated between users and producers best compromised result in matter of harmonisation Step by step approach This takes years Best solution for long term and sustainable maintenance and update of the data 12
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