Imcs review 2013_04_v7
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  • By DOW was expectet 4 final deliverables, during the project time was decided and acepted to merge D3.3 and D3.4 deliverables into one document.
  • This information is from DOW In general this method is universal. In the steps it shows the classic system anallysis and design steps. Specific operational objectives include: Analyse existing data and metadata models for the selected themes used in different contexts and Member states as gathered in Task 2.3 Define platform-neutral conceptual models and metadata profiles for the selected themes Implement tools for the interactive construction of data models for existing data sets with the user communities of WP2 and the validation pilots of WP5 Design and develop transformation processes required on the data sets, including the transformation of existing data models into the INSPIRE model
  • This is win-win cooperation for both parties, habitats and inspire twg.
  • It is high level data theme definitions. Existing user data, experience where recieved from 7 pilot project partners using questionnaire. Analysis of existing data are used to define common conceptual data model. You must remember already existing legal documents and legislation to used data themes. For example Birds directive, HABITATS directive provides information about used classificators.
  • Skripts – atkārtojams cik biezi vien vajadzīgs, piemērots strukturētiem datiem un rutīnas darbībam Desktop – darbība, kur veicam vienu reizi un nav nepieciešams atkārtot.
  • Tas pats kas iepriekš, demonstrē desktop pieeju un serverī lietojamus atkartojamus procesus.
  • Harmonizacija = transformacija
  • Reclasification – change values according new claassicicator It is possible to solve this problem also with basic tools, just in more steps.
  • Simple data model description. Fields and value types.
  • OpenstreetMap data and FMI data

Imcs review 2013_04_v7 Imcs review 2013_04_v7 Presentation Transcript

  • Social Validation ofINSPIRE Annex III DataStructures in EU HabitatsWP3: Data and metadatamodellingIMCS, HSRSMaris Alberts, Ota Čerba, Karel Charvat,Peteris Bruns, Premysl VohnoutHABITATS Final ReviewLuxembourg, April 19 2013
  • WP3 Main objectivesDefine data and metadata models for the following INSPIREdata themes:16. Sea regions17. Bio-geographical regions18. Habitats and Biotopes19. Species distributionThe results should be in compliance with INSPIREdirective and possible INSPIRE data models (notexisting before the project started)
  • WP3 Specific operationalobjectives include– analysis of existing data and metadata models– definition of platform-neutral conceptual modelsand metadata profiles– survey of tools for the interactive construction ofdata models for existing data sets– design of data transformation processes– contribution to INSPIRE coresponding data modeldevelopment
  • Contribution to INSPIRE• To ensure HABITATS data model andmeta data profile compliance withcorresponding INSPIRE data themes.• IMCS joined INSPIRE TWG BR-HB-SDBR 17. Bio-Geographical regionsHB 18.Habitats and BiotopesSD 19.Species distribution
  • INSPIRE TWG BR-HB-SDExceptional case;single TWG worked with 3 data themes!Contribution of IMCS on behalf of HABITATS projectin TWG activities:– Physical TWG meetings: 2010 in Ispra, 2011Amsterdam, 2012 in Vienna,– participation in regular team teleconferences,– contribution to data model development anddocumentation,– Data model testing review analysis and final datamodel improvement according to comments.
  • HABITATS benefits fromparticipation in INSPIRE TWG Habitats project conceptual data model wasprepared short period before INSPIRE TWG wasestablished All HABITATS activities related to INSPIRE datathemes after establishment of TWG was reportedin TWG Feedback from TWG decisions guaranteedcomplete harmonization of HABITATS conceptualdata models with INSPIRE correspondingAnnexes.
  • Data usage use cases• Regional data are used regionally• Global data are used regionally• Regional data are used cross-regionally (hereworks INSPIRE)• Regional data are used globally (here worksINSPIRE)• Global data are used globally (here worksINSPIRE)
  • Regional data used regionallyThere is not direct requirement for INSPIREdata modelsLocal data models could be widerLocal data models reflect regional needs andalso regional decision processesIf data are not shared outside of region (but inmany cases it is necessary), in principle globalstandards are not neededStandards are needed in case of more datasuppliers, to guarantee data consistence
  • Global data used regionallyGlobal data are in some content something like de factostandardsIn some cases it is necessary to be possible transform datainto such models, which is required by regional decisionprocessesThe global model has to cover regional decision needs(GMES case for example)Open problems: the transformation happens either on fly or aspretransformed data snapshotLanguage problem in the case on fly
  • Regional data used crossregionallyIn the case of cross border regions dataharmonization faces extreme challenges.In many cases, for example tourism, we need todeal simultaneously with several INSPIRE relateddata themes. This is much more complex taskthan single data theme case.In some applications data model could be broaderthan corresponding INSPIRE definition.Open problem – how to manage multi lingualproblems
  • Regional data used globally• Probably most relevant case for INSPIRE datamodel• The idea is to combine several local data sets intosingle standardized data set• The regional data has to be transformed (in manycases simplified) into global data model• Relevant cases are tourism, transport, education,research, environment protection, riskmanagement, strategic decision• Language problem
  • Global data used globally• Global data are either standard or de factostandard.• It is expected that in the case of public sectordata INSPIRE compliance will be guaranteed.• Concrete application areas may requirespecific transformation. Transformation couldbe based on Feature Encoding or Styled LayerDescriptor (SLD)
  • T3.1 Conceptual data models
  • Task 3.1 Conceptual dataTask 3.1 Conceptual datamodelsmodels•Deliverable 3.1 Conceptual data models– Contact person / Task leader: Ota Čerba, Karel Charvat (HSRS)•Subtasks– Comparative analysis of data and metadata models currently usedin EU countries and between project partners.– Information sources:• Content from project partners,• Reports from national and EU level INSPIRE TWGs,• Other EU projects (eg. Plan4all, HUMBOLDT).– Comparison of national legislations and definition sets of items formetadata sharing.– Description of conceptual models for single country to reachcommon agreement across EU.
  • Task 3.1Task 3.1 Results andconclusions• Collected information about each project partner –data descriptions and documentation, SDI state ofthe art and data examples necessary for detailedanalysis.• As defined data models are compatible withINSPIRE TWGs draft data models in M9 they areslightly different from final INSPIRE data models.• HABITATS deliverable 3.1 Conceptual data models(finished M9) was contributed to NSPIRE TWG. Infuther project work was used INSPIRE TWG actualdata model versions.
  • T3.2 Metadata profile
  • TaskTask 3.2 Metadata profile3.2 Metadata profile• Deliverable 3.2 Metadata profile– Contact person / Task leader: GregorioUrquía(TRAGSATEC)•Delivery status:– Draft M9 (Delivered)– Final M15 (Delivered)•Subtasks– Comparative analysis of data and metadata modelscurrently used in EU countries.– Information sources:• Collect content from project partners,• Collect and analyse reports from national and EU levelINSPIRE TWGs,• Comparison of available national legislations and definitionsets of items for metadata sharing.
  • T3.3 Data models andtransformation processes
  • Task 3.3 Data models andTask 3.3 Data models andtransformation processtransformation processInitially by DOW was planned two seperated deliverables:- T3.3 Tools for interactive data modelling- T3.4 Transformation process design (Overview of task)After M15 consortium aggreed to merge T3.3 and T3.4.Contact person / Task leader: Peteris Bruns (IMCS), Jachym Cepicky / Karel Charvat(HSRS)Authors: Jachym Čepický (HSRS), Jan Jezek (HSRS), Karel Charvat (HSRS), MarisAlberts IMCS), Peteris Bruns (IMCS), Ota Čerba (HSRS)
  • Task 3.3 Data models andTask 3.3 Data models andtransformation processtransformation processInput and information sources:Content from project partners (questionnaires, inputs to deliverable),Reports from EU level, INSPIRE TWG BR-HB-SD,Information from D3.1, D3.2 and INSPIRE TWG BR-HB-SD discussions,Analysis of good practice from other EU projects such as Plan4all, Humboldtetc.Main tasks:To assist end users to describe their current models using interactivemodelling toolsTo develop training materials and provide trainig sessions for users.To designs the main transformation processes required for data sets, basedon the requirements activities of Task 2.3
  • Data transformation trainingsData transformation trainingsTraining materials prepared and afterwards used inworkshops for data transformationBasic data transformation training principles aredemonstrated on practical examples describing theprocess of data transformation using simple tools. Thefollowing operations were trained:– Simple value mapping and extraction– Spatial data file manipulations– Geometry manipulations– How to create repeatable transformation scripts for routine or batchtransformation tasksAdvanced transformations were only demonstrated usingadvanced systems and tools.
  • Basic transformationsBasic transformationsBasic script for automateddata merge of datamaintained as multiplefilesBasic data merge of datamaintained in multiple filestructure using free andopensource desktopapplicationTwo solutions for data merging
  • Basic transformationsBasic transformationsBasic SQL script forgeometry extraction frommultigeometry, further canbe used as one setep inlarger data transformationprocessBasic geometry extractionfrom multigeometry usingfree and opensourcedesktop applicationTwo solutions for data extracting
  • Advanced transformationsAdvanced transformationsData Specifications 2.0Habitats andbiotopesHarmonizationFMI Data
  • Advanced transformation –scheme (1)Open SHP fileand its schemeSave finalSHP fileReclassificationFMI → EUNISNew datamodel
  • New data mode (1)Existing FMI data model +referenceHabitatTypeId: CharacterStringreferenceHabitatTypeScheme: ReferenceHabitatTypeSchemeValuelocalSchemeURI: URIlocalNameValue: CharacterStringgeometry: polygonreferenceHabitatTypeId: eunis_valuereferenceHabitatTypeScheme: eunislocalSchemeURI: link_to_FMI_classificationlocalNameValue: FMI_classification_value
  • Data model mapping (1)
  • Taxonomy – reclassification(FMI → EUNIS) (1)0 Pine → G3.42,"4","Middle European [Pinus sylvestris] forests"1 Oak → G1.87,"4","Medio-European acidophilous [Quercus] forests"2 Beech-oak → G1.82,"4","Atlantic acidophilous [Fagus] - [Quercus] forests"3 Oak-beech → G1.82,"4","Atlantic acidophilous [Fagus] - [Quercus] forests"4 Beech → G1.6,"3","[Fagus] woodland"5 Fir-beech → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"6 Spruce-beech → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"7 Beech-spruce → G4.6,"3","Mixed [Abies] - [Picea] - [Fagus] woodland"8 Spruce → G3.1D,"4","Hercynian subalpine [Picea] forests"9 Dwarp pine → F2.45,"4","Hercynian [Pinus mugo] scrub"
  • Reclassification (1)
  • FMI DataINSPIRE / HabitatsData
  • Advanced transformation (2)CQLfilter
  • Advanced transformation (3) –OntologyDescriptionNomenclaturesDerivedtransformationrules
  • Description in ontology (3)
  • Examples of transformedstructures
  • Results and conclusionsTransformation of data from multiple data models intoa unified data model provides an opportunity tocompare and use data in a wider area and frommultiple data providers. It stipulates the economy inthrough research, cross-border cooperation and otherimportant areas.We provide successful experimentation with on flycoordinate transformation
  • Results and conclusionsThe experimentation demonstrate, that in most cases isnot realistic on fly data model transformation (itis in relation with other relevant projects likeHUMBOLT, Plan4all, EuroGEOSS or implementation ofCzech INPSIRE portal or Czech Cadastre). There is needfor replication of so called INSPIRE data or there couldbe used brokerage model like in EUROGEOSS project,which provide harmonization of data. There is alsoclear need to thing about other approaches thencurrently WMS, WFS based services
  • Results and conclusionsThere is important to understand, why we areharmonizing data? What is main purpose of datatransformation?It is necessary only in cases, that data are sharedacross regions and countries. If pilots are workingstandalone, then standardization is not necessaryOn other side we have recognize, that INSPIRE datamodels are only some minimal set, covering basicneeds. We have expect, that inside of differentcommunities will be necessary to extend this models,to fulfill requirements, of communities.Every used data model has cover needs of users
  • Results and conclusionsThe conditions for data harmonization of the HABITATSpilots differ due to the facts that:Partners has different needsIn some cases partners were more data users, then datasupplierspartners have different levels of development and GItechnologies;partners have a different level of expertise in GItechnologies;partners have a different understanding of spatial data andmodels.Different conditions impose certain restrictions andrequirements for the harmonization process of data andmetadata.
  • Results and conclusionsHabitats contribute to INSPIRE Standardizationprocess.Habitats made recommendation for differenttechnologies, which could be used in certain context ofdata harmonizationHabitats provide training of stakeholders for dataharmonizationHabitats made demonstration of harmonization dataon more pilotsHabitats design advanced ontology based concept fordata harmonization in relation to solved tasks
  • Thanks!