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DATA FOR HUMANITERIAN AID
AKLILU TEKLESADIK
DSD-INT 2018 Data For Humanitarian Aid - Teklesadik
510 million square kilometers is the total surface of
the earth
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
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
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
• Introduction
• Data collection
• Data Integration
• IBF
OUTLINE
• Introduction
• Data collecten
• Data Integration
• IBF
INTRODUCTION
Event
Preparedness phase EWEA phase Response phase Recovery phase
INTRODUCTION
• Introduction
• Data collecten
• Data Integration
• IBF
• Introduction
• Data collecten
• Data Integration
• IBF
Preparedness phase EWEA phase Response phase Recovery phase
INTRODUCTION
• Introduction
• Data collecten
• Data Integration
• IBF
Preparedness phase EWEA phase Response phase Recovery phase
INTRODUCTION
Preparedness phase EWEA phase Response phase Recovery phase
INTRODUCTION
• Introduction
• Data collecten
• Data Integration
• IBF
• Other DS activities
Preparedness phase EWEA phase Response phase Recovery phase
INTRODUCTION
• Introduction
• Data collecten
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities Exposure
Hazard
(weather and
climate
events)
Disaster
Risk
Vulnerability &
Coping Capacity
!!
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
Deep Learning on satellite imagery
DATA COLLECTION: EXPOSURE DATA
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
DATA COLLECTION: VULNERABILITY
• Introduction
• Data collection
• Data Integration
• IBF
• Other DS activities
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
Travelling time to the nearest hospital.(coping
capacity indicator)
Which is calculated based on OSM data.
DATA COLLECTION: COPING CAPACITY
Damage and Needs Assessment reports
Digital newspaper repositories
DREFs
DATA COLLECTION: IMPACT DATA
• 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
DATA COLLECTION: DAMAGE ASSESMENT
DATA INTEGRATION
• Introduction
• Data collecten
• Data Integration
• IBF
• Other DS activities
HAZARD
AND
EXPOSURE
VULNERABILITY
LACK OF
COPING
CAPACITY
+++
DATA INTEGRATION
DATA
INTEGRATION
DATA
COLLECTIO
N &
COLLATION
HAZARD
AND
EXPOSURE
VULNERABILITY
LACK OF
COPING
CAPACITY
+++
DATA INTEGRATION
DATA
INTEGRATION
DATA
COLLECTIO
N &
COLLATION
DATA
VISUALISATI
ON
11
REGIONS
74
ZONES
689
WOREDAS
DATA INTEGRATION
• Introduction
• Data collecten
• Data Integration
• IBF
FORECAST BASED FINACNING - IBF
• Introduction
• Data collecten
• Data Integration
• IBF
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
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
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
=+
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
Exposure
Hazard
forecasted
IMPACT BASED FORECASTING- IBF
Impact
EXPERT KNOWLEDGE
Impact Curve Impact
IMPACT BASED FORECASTING- IBF
ELEMENTARY MODELLING
Exposure
Hazard
forecasted
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
• 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
SUPPORT@510.GLOBAL
Thank you

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DSD-INT 2018 Data For Humanitarian Aid - Teklesadik

  • 1. DATA FOR HUMANITERIAN AID AKLILU TEKLESADIK
  • 3. 510 million square kilometers is the total surface of the earth
  • 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. 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. 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
  • 7. • Introduction • Data collection • Data Integration • IBF OUTLINE
  • 8. • Introduction • Data collecten • Data Integration • IBF INTRODUCTION Event
  • 9. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF
  • 10. • Introduction • Data collecten • Data Integration • IBF Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION
  • 11. • Introduction • Data collecten • Data Integration • IBF Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION
  • 12. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  • 13. Preparedness phase EWEA phase Response phase Recovery phase INTRODUCTION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  • 14. DATA COLLECTION • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 15. DATA COLLECTION • Introduction • Data collection • Data Integration • IBF • Other DS activities Exposure Hazard (weather and climate events) Disaster Risk Vulnerability & Coping Capacity
  • 16. !! DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 17. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 18. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 19. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 20. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 21. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 22. DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 23. Deep Learning on satellite imagery DATA COLLECTION: EXPOSURE DATA • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 24. DATA COLLECTION: VULNERABILITY • Introduction • Data collection • Data Integration • IBF • Other DS activities
  • 25. 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
  • 26. Travelling time to the nearest hospital.(coping capacity indicator) Which is calculated based on OSM data. DATA COLLECTION: COPING CAPACITY
  • 27. Damage and Needs Assessment reports Digital newspaper repositories DREFs DATA COLLECTION: IMPACT DATA
  • 28. • 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
  • 30. DATA INTEGRATION • Introduction • Data collecten • Data Integration • IBF • Other DS activities
  • 33. 11 REGIONS 74 ZONES 689 WOREDAS DATA INTEGRATION • Introduction • Data collecten • Data Integration • IBF
  • 34. FORECAST BASED FINACNING - IBF • Introduction • Data collecten • Data Integration • IBF
  • 35. 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
  • 36. 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
  • 38. 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
  • 40. Impact Curve Impact IMPACT BASED FORECASTING- IBF ELEMENTARY MODELLING Exposure Hazard forecasted
  • 41. 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
  • 42. • 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