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DSD-INT 2018 Data For Humanitarian Aid - Teklesadik

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Presentation by Aklilu Dinkneh Teklesadik (Red Cross 510 Team) at the Data Science Symposium 2018, during Delft Software Days - Edition 2018. Thursday 15 November 2018, Delft.

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DSD-INT 2018 Data For Humanitarian Aid - Teklesadik

  1. 1. DATA FOR HUMANITERIAN AID AKLILU TEKLESADIK
  2. 2. 510 million square kilometers is the total surface of the earth
  3. 3. Shape the future of humanitarian aid by converting data into understanding, and put it in the hands of humanitarian relief workers, decision makers and people affected, so that they can better prepare for and cope with disasters and crises. MISSION
  4. 4. Shape the future of humanitarian aid by converting data into understanding, and put it in the hands of humanitarian relief workers, decision makers and people affected, so that they can better prepare for and cope with disasters and crises. MISSION
  5. 5. Shape the future of humanitarian aid by converting data into understanding, and put it in the hands of humanitarian relief workers, decision makers and people affected, so that they can better prepare for and cope with disasters and crises. MISSION
  6. 6. • Introduction • Data collection • Data Integration • IBF OUTLINE
  7. 7. • Introduction • Data collecten • Data Integration • IBF INTRODUCTION Event
  8. 8. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF
  9. 9. • Introduction • Data collecten • Data Integration • IBF Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION
  10. 10. • Introduction • Data collecten • Data Integration • IBF Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION
  11. 11. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  12. 12. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  13. 13. DATA COLLECTION • Introduction • Data collection • Data Integration • IBF • Other DS activities
  14. 14. DATA COLLECTION • Introduction • Data collection • Data Integration • IBF • Other DS activities Exposure Hazard (weather and climate events) Disaster Risk Vulnerability & Coping Capacity
  15. 15. !! DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  16. 16. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  17. 17. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  18. 18. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  19. 19. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  20. 20. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  21. 21. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  22. 22. Deep Learning on satellite imagery DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  23. 23. DATA COLLECTION: VULNERABILITY • Introduction • Data collection • Data Integration • IBF • Other DS activities
  24. 24. DATA COLLECTION: VULNERABILITY wall material per municipality . WALL TYPE ROOF TYPE wood Concrete Mud bricks Thatch CLASS 1 CLASS 5 CLASS 2 Corrugated iron CLASS 3 CLASS 6 CLASS 4
  25. 25. Travelling time to the nearest hospital.(coping capacity indicator) Which is calculated based on OSM data. DATA COLLECTION: COPING CAPACITY
  26. 26. Damage and Needs Assessment reports Digital newspaper repositories DREFs DATA COLLECTION: IMPACT DATA
  27. 27. • Data from local and national level • Damage and Needs Assessments, such as from National Disaster Management agencies or NGOs • Digital newspaper repositories • DREFs • Data from global repositories (often derived from national databases) • EM-DAT • Desinventar • Preventionweb • Humanitarian Data Exchange DATA COLLECTION: IMPACT DATA
  28. 28. DATA COLLECTION: DAMAGE ASSESMENT
  29. 29. DATA INTEGRATION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  30. 30. HAZARD AND EXPOSURE VULNERABILITY LACK OF COPING CAPACITY +++ DATA INTEGRATION DATA INTEGRATION DATA COLLECTIO N & COLLATION
  31. 31. HAZARD AND EXPOSURE VULNERABILITY LACK OF COPING CAPACITY +++ DATA INTEGRATION DATA INTEGRATION DATA COLLECTIO N & COLLATION DATA VISUALISATI ON
  32. 32. 11 REGIONS 74 ZONES 689 WOREDAS DATA INTEGRATION • Introduction • Data collecten • Data Integration • IBF
  33. 33. FORECAST BASED FINACNING - IBF • Introduction • Data collecten • Data Integration • IBF
  34. 34. Humanitarian finance is available mainly when a disaster strikes and suffering is almost guaranteed. But climate-related risks are rising Challenge Climate change Population Poverty Exposure Hazard (weather and climate events) Disaster Risk Vulnerability & Coping Capacity FORECAST BASED FINACNING - IBF • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  35. 35. Forecast-based financing (FBF) releases humanitarian funding based on forecast information TO TAKE PREDEFINED ACTIONS TO reduce risks, The Innovation We can forecast climate-related risks (with uncertainty) with a lead time Humanitarian actions could be implemented in this lead time window Opportunity FORECAST BASED FINACNING - IBF • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  36. 36. VULNERABILITY LACK OF COPING CAPACITY =+ SHORT- TERM HAZARD FORECAST FBF IMPACT FORECAST WIND SPEED FORECAST % HOUSES DAMAGED PREDICTED % OF HOUSES WITH WEAK WALLS/ROOFS FBF APPROACH PHILIPPINES TYPHOON EXAMPLE DUMMY =+
  37. 37. IMPACT BASED FORECASTING- IBF Input (explanatory variables) Output (loss and damage) Composite index approach (overlay) Based on experience, usually in relation to one input indicator (wind speed, water level) at specific locations. Often estimates, no absolute values Elementary modelling (rule-based) Data analysis is done to e.g. determine thresholds. Simple damage-hazard curves with one input variable. Often for infrastructural damage. Statistical modelling (without relying on rule-based) Multiple indicators also for e.g. urban vs rural. Also for e.g. crop damage modelling
  38. 38. Exposure Hazard forecasted IMPACT BASED FORECASTING- IBF Impact EXPERT KNOWLEDGE
  39. 39. Impact Curve Impact IMPACT BASED FORECASTING- IBF ELEMENTARY MODELLING Exposure Hazard forecasted
  40. 40. Damage-hazard curve: identify underlying causes of impacts (vulnerabilities) Destruction of Houses Bad quality construction materials (wall and roof) Poverty Vulnerability Indicators IMPACT BASED FORECASTING- IBF ELEMENTARY MODELLING
  41. 41. • Collect for historical typhoons data on loss and damage (output) & several explanatory variables such as wind speed, wall/rooftypes (input) • Make statistical model to predict damage and improve model performance • For upcoming typhoon collect same inputs (forecasted wind speed) and and apply model to predict output (damage) Note the change of forecasted typhoon track in 12hrs time. Damage prediction can only be as good as the weather forecast! IMPACT BASED FORECASTING- IBF STATISTICAL MODELLING/MACHINE LEARNING
  42. 42. SUPPORT@510.GLOBAL Thank you

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