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.

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

29. DATA COLLECTION: DAMAGE ASSESMENT

30. DATA INTEGRATION
• Introduction
• Data collecten
• Data Integration
• IBF
• Other DS activities

31. HAZARD
AND
EXPOSURE
VULNERABILITY
LACK OF
COPING
CAPACITY
+++
DATA INTEGRATION
DATA
INTEGRATION
DATA
COLLECTIO
N &
COLLATION

32. HAZARD
AND
EXPOSURE
VULNERABILITY
LACK OF
COPING
CAPACITY
+++
DATA INTEGRATION
DATA
INTEGRATION
DATA
COLLECTIO
N &
COLLATION
DATA
VISUALISATI
ON

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

37. 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
=+

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

39. Exposure
Hazard
forecasted
IMPACT BASED FORECASTING- IBF
Impact
EXPERT KNOWLEDGE

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

43. SUPPORT@510.GLOBAL
Thank you