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Visualisation of big data in agriculture and rural development

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International Data Week 2018

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Visualisation of big data in agriculture and rural development

  1. 1. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 1 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732064 This project is part of BDV PPP VISUALISATION OF BIG DATA IN AGRICULTURE AND RURAL DEVELOPMENT Karel Charvat, Karel Jedlicka, Tomas Reznik, Vojtech Lukas, Raitis Berzins, Raul Palma, Dmitrij Kozuch, Karel Charvat Jr. SciDataCon 2018: The Digital Frontiers of Global Science Gaborone 6th of November
  2. 2. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 2 In cooperation with
  3. 3. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 3 BIG DATA Variety (managing integration of all the heterogeneous data from the past - using Linked (Open) Data and semantics/ontologies etc. - and data access, queries, reporting etc. for data preparation). Descriptive analytics and classical query/reporting (performance data, transactional data, attitudinal data, descriptive data, behavioral data, location-related data, interactional data, from many different sources) Velocity (managing real time/sensor data from the present - complex event processing, Apache Kafka/Storm etc.) Monitoring and real-time analytics - pilot services (in need of Velocity processing - and handling of real-time data from the present) - trigging alarms, actuators etc. Volume (mining all the data with respect to prediction and forecasting for the future - using various types of machine learning and inductive statistical methods).
  4. 4. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 4 RURAL COMMUNITIES Citizens including young people Farmers Foresters Fisheries Food producersWood producers Local Government Development agencies, etc Visitors, tourists ICT companies Researchers Advisors Developers Analytics Economist
  5. 5. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 5 ROLE OF INTERMEDIATORS INTERMEDIATORS ICT COMPANIES RESEARCHERS ADVISORS DEVELOPERS ANALYTICS ECONOMIST ETC Communities • Citizens including young people • Farmers • Foresters • Fisheries • Food producers • Wood producers • Small and medium enterprises • Local Government • Development agencies, etc • Visitors, tourists WISDOM KNOWLEDGE INFORMATION DATA
  6. 6. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 6 Handle, Integrate and vizualise heterogenous data • Utilization of FOODIE data model for integration of heterogenous farm related vector data and build robust system fro quiring and visualization of heterogenous data • Support easy search and access to EO data stored in different repositories • Build robust system for downloading and processing EO data from Sentinel 2 and Landsat • Build advisory system for farmers helping them with preparing recommendation for nitrogen application based on integration farm and EO data Extension and work o fieldremote eye farm
  7. 7. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 7 3D visualization
  8. 8. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 8 3D visualization
  9. 9. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 9 Data Integration using RDF Linked Data as Federated Layer • Input datasets: • EU datasets • Smart Points of Interest - SPOI • Open Land Use - OLU • Open Transport Map - OTM • Open Czech datasets: • LPIS data • Water bodies • Erosion zones • Soil Maps • Farm (private) datasets - Farm Rostenice: • Field boundaries • Crop maps • Yield records • Model specification: • Reuse FOODIE ontology + general & Czech specific extensions
  10. 10. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 10 Data Integration using RDF Linked Data as Federated Layer • RDF data generation • Transformation of source datasets into RDF • Shapefiles, relational data • Main task: mapping specification • Data linking • Discovery of links • Geospatial integration via sparql queries • Data exploitation • HSLayers via Sparql endpoint (demo) • Other clients (e.g., Metaphactory)
  11. 11. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 11 Example Czech pilot
  12. 12. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 12 Delimiting of Agro-Climatic Zones • Elevation as a factor influencing temperature • Input ~ sparse lattice (with approx. 4x4 km sampling) of temperatures estimated on earth‘s surface reduction to sea level: T0 = Ts + k * Es /100 • Densification of the 4x4 km lattice T0 temperature to 25 x 25 m spacing lattice using spline interpolation. • Applying the elevation factor to calculate temperatures back on surface, but on laticce with 25x25 m spacing. Ts = T0 - k * Es /100
  13. 13. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 13 Delimiting of Agro-Climatic Zones
  14. 14. This document is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under agreement No 732064. It is the property of the DataBio consortium and shall not be distributed or reproduced without the formal approval of the DataBio Management Committee. Find us at www.databio.eu. 14 Thank you for your attention! W www.databio.eu W www.euxdat.eu E charvat@lesprojekt.cz, E info@databio.eu agriXchange / DataBio @DataBio_eu DataBioProject

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