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Icm sem tech_master


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Icm sem tech_master

  1. 1. Case studyLinked-data forLinked-data forIntegrated CatchmentIntegrated CatchmentManagementManagementIan DickinsonEpimorphics GuilbertEnvironment
  2. 2. Agendacontext and aims– catchment management data– visionintegrated catchment linked-data projectconclusion
  3. 3. A better place for people and wildlife
  4. 4. Catchment management
  5. 5. Data overviewwater bodiesrisk assessmentsclassification resultsreasons for failurepredicted outcomesactions
  6. 6. 1. Data & evidence,consultations, localknowledge, model outputsand plans collated in to ashared central system “LocalCommunity CPS”Local Community CatchmentPlanning SystemLocal Community CatchmentPlanning SystemMonitoringMonitoring LocalKnowledgeLocalKnowledge ActionsActions2. Contents of LocalCommunity CPSpublished as LinkedData alongside EAand researchdatasets3. Linked Data (machinereadable data) could beautomatically combined byapplications such as the EVO,CCM Hub and any number ofweb appsCCMHUBSlide used by kind permission of Michelle Walker, Rivers Trust
  7. 7. ICM: proof-of-concept project
  8. 8. ICM proof-of-concept project16 weeks durationproject team:– 1 FTE app dev– 0.4 FTE userresearch– 0.5 FTE data7400 water bodies7.8m triplesagile principles– four iterations– 2-3 week sprints– stakeholderreviewalpha/staging siteorganizationscale
  9. 9. From data to linked open datadata modellingextractiontransformationpublicationpresentationinterpretationdownloadsource dataSQLJavaApache Fusekiexplorer applicationElda
  10. 10. Modelling: considerationsevery constant becomes a URIplan for changere-use vocabulariescomplete is better than simple
  11. 11. Data complexityWaterBodySurfaceWater GroundWaterRiverOrLake Transitional CoastalRiver LakeSurfaceWaterTransfer Canal SSSI_Ditch
  12. 12. Data complexityWaterBodySurfaceWater GroundWaterRiverOrLake Transitional CoastalRiver LakeSurfaceWaterTransfer Canal SSSI_Ditch
  13. 13. Data transformationin: CSVout: RDF triplesiterative, so automate!
  14. 14. Data publishingBaseline goal:– provide access to the dataPractical considerations:– Just “follow-your-nose” linked data?– or SPARQL?– or an API?– ….
  15. 15. Published data: SPARQL? 
  16. 16. Published data: linked-data API
  17. 17. Published data: linked-data API
  18. 18. Published data: linked-data API
  19. 19.  
  20. 20. ICM data explorerpresent the data in a meaningful wayprovide meaningful and useful interactions
  21. 21. Data explorer key featuressearch– by name, catchment, location, ...show classification itemsfilter by properties– e.g. classification valuemap and tabular outputbasic reportsdownload data
  22. 22. Data explorer applicationSpecificunderstandingof user goalsand taskGenericdata-driveninterfacedata explorer
  23. 23. Data explorer applicationInterpretationandreportingExtractanddownloaddata explorer
  24. 24. Data explorer applicationEasy fornovicesto getstartedNot toofrustratingand slowfor experiencedusersdata explorer
  25. 25. Typical user enquiry“Please show me all:– rivers and lakes– near Glastonbury– that had overall ecological classification asmoderate, poor or bad– between 2009 and 2012.”
  26. 26. Dialogue movescorrespondingSPARQLqueryselectedRDFresourcescorrespondingSPARQLqueryselectedRDFresourcesinteractionstatelocation,classifications,water-body types,...interactionstateadd year constraint
  27. 27. Demo
  28. 28. Initial learningswriting SPARQL by doing– in context– with feedbackhard to balance different user needs– explore vs. guide– real user inputdownload– important– RDF to useful CSV is hard
  29. 29. Dissemination
  30. 30. Conclusions & next stepsformal evaluation– involve partner organizations eg Rivers Trust“generated excitement”– key engagement tool for catchment managementinformation– summer 2014 draft river basin management plansbig picture– reference spine for integrating data from otherenvironmental stakeholders
  31. 31. Photo courtesy of grisleyreg License CC BY-NA 3.0Questions?