1. Disaster Inventories Workshop
EQUATION OF RISK
Number of expected people killed, other losses
Frequency & Magnitude (or intensity of hazard
Population living in the exposed area, infrastructure
Degree of population or infrastructure “fragility”
Risk = Hazard x Element exposed x Vulnerability*
Risk = Physical Exposure x Vulnerability
* UNDRO (1979), Natural Disasters and Vulnerability Analysis in Report of Expert Group Meeting
Maputo, Mozambique, August of 2008

2. Disaster Inventories Workshop
The Risk Triangle:
Risk is a combination of the interaction of hazard, exposure, and vulnerability, which can be represented by the three sides of a triangle.
RISK
Exposure
Vulnerability
Hazard
Reliable & Accurate
Data
Reliable & Accurate
Data
If any one of these sides increases, the area of the triangle increases, hence the amount of risk also increases.
If any one of the sides reduces, the risk reduces.
If we can eliminate one side there is no risk.
Reliable & Accurate
Data
Maputo, Mozambique, August of 2008

3. Disaster Inventories Workshop
Hazard: a natural or social-technological phenomena that produces damages to human lives, economic/social infrastructure and environment (earthquakes, floods, droughts, etc.)
Vulnerability: Degree of population or infrastructure “fragility” to hazards.
Risk: the probability of a certain level of loss to occur.
Maputo, Mozambique, August of 2008

4. Disaster Inventories Workshop
DISASTER RISK MANAGEMENT CYCLE
Maputo, Mozambique, August of 2008

5. Disaster Inventories Workshop
Prevention, Preparedness, Mitigation, Risk Reduction….
“Effective early warning and preparedness, land use planning and appropriate construction, risk assessment in projects and planning, community based risk management, insurance (financial and social) and asset protection through social safety nets among others dramatically reduce human exposure to hazard and susceptibility to harm. Action to reduce risks from natural disasters must be at the centre of development policy”
DFID Policy Briefing, Disaster risk reduction: a development concern, 2004.
Maputo, Mozambique, August of 2008

6. Disaster Inventories Workshop
Emergency: “The phase immediately after impact is characterized by the intense and serious disturbance […] and the minimum conditions necessary for the survival and functioning of the affected social unit are not satisfied
Recovery: Process of re-establishing acceptable and sustainable living conditions through the rehabilitation, repair and reconstruction of destroyed, interrupted or deteriorated infrastructure, goods and services and the reactivation or promotion of economic and social development in affected areas
Maputo, Mozambique, August of 2008

7. Disaster Inventories Workshop
UNDAC: mainly for response purposes (United Nations Disaster Assessment and Coordination). Being replaced by a series of more specialized assessments (UNOSAT, EC-IRA, WHO-RHA, etc.)
ECLAC: adopted for measuring direct and indirect economic and social impacts, divided by economical sectors (Economic Commission for Latin America and the Caribbean)
Maputo, Mozambique, August of 2008

8. Disaster Inventories Workshop
RISK ASSESSMENTS
“Risk assessment is the determination of quantitative or qualitative value of risk related to a concrete situation, location and a specific threat.”
Are targeted to specific hazards
Require large amounts of information
Involve complex modeling
May change over time
Urban or regional
Maputo, Mozambique, August of 2008

9. Disaster Inventories Workshop
Hazard probability (frequency)
Exposed population
Simple Risk Index
Maputo, Mozambique, August of 2008

10. Disaster Inventories Workshop
Some Applications of Risk Assessments
Identification of Priority areas (Hotspots)
Evaluation of urgency of action
Support for Preparedness, Risk Mitigation, EWS plans
Support for Policies/Regulations and investments
Strategic advantage for negotiation
Other applications
Maputo, Mozambique, August of 2008

11. Disaster Inventories Workshop
Mitigation actions
Specifics
Engineering, constructing measures
Map. Inventory of non-engineered buildings; Design standards, building codes; potential incentives (reduced insurance cost, land title, etc…)
Physical planning measures
Land–use and zoning regulations; map/inventory of lifelines facilities; location of population concentration; design of supply and transport networks
Economic measures
Unemployment, income distribution, poverty levels; degree of diversification; taxation and incentive policies; access to insurance
Management and institutional measures
Political will to implement mitigation measures; Government structures established to plan; prioritization of planning; responsibility assignments
Social measures
Commitment on public education; Participation of communities in decisions
Maputo, Mozambique, August of 2008

12. DesInventar: The Project
Some of the Hypothesis that inspired the project
Disasters are a problem of Development
Natural disasters are not so “natural”
Impact of Disasters is growing
Small and medium disasters impact is extremely high
Small and medium disasters occurrence patterns can show vulnerability
Maputo, Mozambique, August of 2008

13. Maputo, Mozambique, August 18-23 of 2008
What is DesInventar
A data collection methodology
A preliminary analysis methodology
A set of Software Tools
DesInventar Contexts
As a Historical Disaster database
As a Post-disaster damage & loss data collection tool
Maputo, Mozambique, August of 2008

14. DesInventar Methodology:
… essentially proposes the collection of homogeneous data about disasters of all scales. The information compiled and processed is entered in a scale of time and referenced to a relatively small geographic unit.
Maputo, Mozambique, August of 2008

15. DesInventar Data Collection Methodology:
Concepts
Definitions
Glossary of Events and Effects
Recommendations & How to’s
Maputo, Mozambique, August of 2008

16. DesInventar Methodology:
Concepts:
Hazard
Vulnerability
Risk
Geography
Maputo, Mozambique, August of 2008

17. DesInventar Methodology:
Definition:
“Event” is defined as any social-natural phenomena that can be considered as a threat to life, properties, infrastructure and environment.
Maputo, Mozambique, August of 2008

18. DesInventar Methodology:
Definition:
“Disaster” is defined as the set of adverse effects caused by social-natural and natural phenomena on human life, properties, infrastructure and environment (an “Event”) within a specific geographic unit during a given period of time.
Maputo, Mozambique, August of 2008

19. DesInventar Methodology:
Geography:
multi-layered area units
Hierarchical structure (currently limited to three levels)
Usually Administrative boundaries
Challenge: Selecting the maximum resolution
Maputo, Mozambique, August of 2008

20. DesInventar Methodology:
GLOSARY OF TERMS: EVENTS
ACCIDENT
HAILSTORM
FLASH FLOOD (ALLUVION)
HEAT WAVE
AVALANCHE
LANDSLIDE
BIOLOGICAL DISASTER
LEAK
COASTLINE EROSION
LIQUEFACTION
DROUGHT
TSUNAMI
EARTHQUAKE
PLAGUE
ELECTRIC STORM
POLLUTION
EPIDEMIC
RAINS
VOLCANIC ERUPTION
SEDIMENTATION
EXPLOSION
SNOWSTORM
FAILURE
SPATE
FIRE
STORM
FLOOD
WINDSTORM
FOREST FIRE
STRUCTURE
FROST
SURGE
Maputo, Mozambique, August of 2008

21. DesInventar Methodology:
DEFINITIONS OF EFFECTS
Maputo, Mozambique, August of 2008

22. DesInventar Methodology:
Recommendations & How to’s:
Selection of Boundaries
Choosing the maximum resolution
Selecting Codes (and names)
The Period of the research
Selection of sources
Maputo, Mozambique, August of 2008

23. DesInventar Methodology:
Recommendations & How to’s:
When disaggregated data is unavailable
Discrepancies among sources
“Chained” events
When geographical units are split
Long duration events
Maputo, Mozambique, August of 2008

24. Preliminary Analysis Methodology:
Preliminary analysis is a set of SIMPLE operations that can be routinely applied to a DesInventar database that can provide very quickly with proxy indicators of Risk and help identifying patterns and trends.
Is called “Preliminary” because it doesn’t correlate the data with other possible sources of data such as demography, topography, land use, etc. It is a “self-contained” analysis.
Deeper analysis should be done after to further prove conclusions and establish causes.
Maputo, Mozambique, August of 2008

25. Preliminary Analysis Methodology:
Composition of disasters (type and effects)
Temporal analysis (trends and patterns)
Spatial distribution analysis (spatial patterns)
Cause-effect analysis
Statistical Analysis (mean, max, deviation, variance)
Maputo, Mozambique, August of 2008

26. Preliminary Analysis Methodology:
Composition Analysis:
Shows what types of disasters are affecting a region
Compares the effect of different types of events
Analysis is done on specific types of effects (human life, housing, agriculture, etc.)
Can be done for the entire area or specific sub-regions
Maputo, Mozambique, August of 2008

27. Preliminary Analysis Methodology:
Use of Composition Analysis:
Provides initial figures aggregated in time and space showing the total impact of disasters.
Helps focusing the rest of the analysis by identifying critical types of events
Maputo, Mozambique, August of 2008

28. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Composition of Disasters: Number of Reports
Maputo, Mozambique, August of 2008

29. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Composition of Disasters: Number of Deaths
Maputo, Mozambique, August of 2008

30. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Composition of Disasters: Number of houses damaged or destroyed
Maputo, Mozambique, August of 2008

31. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (incomplete)
Composition of Disasters: Number of Reports
Maputo, Mozambique, August of 2008

32. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (incomplete)
Composition of Disasters: Number of Deaths
Maputo, Mozambique, August of 2008

33. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (incomplete)
Composition of Disasters: Number of Houses Damaged or Destroyed
Maputo, Mozambique, August of 2008

34. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Temporal Analysis:
This type of analysis shows patterns of occurrence of disasters along time (for example the seasonality of atmospheric events) and trends of the occurrence and impact of disasters, calculated in terms of different effect variables, such as Number of deaths, Number of destroyed houses, number of reports etc.
Maputo, Mozambique, August of 2008

35. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Use of Temporal Analysis:
Provides input for time aspects of contingency plans, DRM, etc.
Follow up of effectiveness of Risk Mitigation Plans
Maputo, Mozambique, August of 2008

36. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Occurrence of Disasters: Number of Reports
Maputo, Mozambique, August of 2008

37. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Seasonality of Disasters: Number of Deaths
Maputo, Mozambique, August of 2008

38. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (incomplete)
Occurrence of Disasters: Number of Reports
Maputo, Mozambique, August of 2008

39. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (incomplete)
Trends in Disasters: Number of Deaths EXCLUDING TSUNAMI
Maputo, Mozambique, August of 2008

40. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Spatial Analysis:
This type of analysis shows patterns of occurrence of disasters over space, displayed as colored areas in terms of the number of reports and different effect variables, such as Number of deaths, Number of destroyed houses, etc.
Maputo, Mozambique, August of 2008

41. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Spatial Analysis:
Riskier and/or Vulnerable areas may be identified by isolated areas or clusters of areas with higher than average level of impact
It usually shows patterns of higher than average impact associated to geography elements (rivers, hill areas, etc)
Can be combined with temporary analysis to provide seasonal occurrence maps
Maputo, Mozambique, August of 2008

42. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Use of Spatial Analysis:
Provides Maps of proxy indicators of Risk (“proxy risk maps”) in absence of much higher cost, long term risk maps
Should be used as input layer to modelled risk maps
Can be used to validate and complement risk maps
DOES NOT REPLACE OTHER MODELLING-BASED RISK ASSESMENT MAPS or GIS SYSTEMS
Maputo, Mozambique, August of 2008

43. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Kanyakumari District
Maputo, Mozambique, August of 2008

44. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters:
Multi- Hazard Map of Number of Deaths
EXCLUDING TSUNAMI
Maputo, Mozambique, August of 2008

45. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters: Multi-hazard Map of Number of Reports EXCLUDING TSUNAMI
Maputo, Mozambique, August of 2008

46. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters:
Multi-Hazard Map of Number of Houses affected EXCLUDING TSUNAMI
Maputo, Mozambique, August of 2008

47. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters: FLOODS Number of Reports
Maputo, Mozambique, August of 2008

48. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters: FLOODS Number of Houses affected
Maputo, Mozambique, August of 2008

49. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
State level figures (Incomplete)
Patterns in Disasters: FLOODS Number of deaths
Maputo, Mozambique, August of 2008

50. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Statistical Analysis:
Provides Tabular form of data to support other types of analysis
Provides aggregates of data by multiple criteria with simple pivoting operations
Provides basic statistical measures (mean, variance, std. deviation, maximums, etc)
Provides information to be further processed by other systems (export of aggregated data)
Maputo, Mozambique, August of 2008

51. Examples of Preliminary Analysis With Tamil Nadu Disaster Data
Maputo, Mozambique, August of 2008

52. Potential Use of DesInventar
Discutir como sera implementado o Observatorio de Desastres em Mocambique. Como e quem o vai operar?
Quem serao os usuarios dos produtos do Observatorio. Como esses produtos serao acessados e disseminados?
Como sera realizado o processo de analise e sua relacao com a avaliacao do risco?
Discutir como sera implementado o processo de investigacao historica?
Outros pontos adicionais/sugestoes
Maputo, Mozambique, August of 2008

53. Potential Use of DesInventar
Input as vulnerability layer for Risk assessment models (‘proxy’ indicators)
Support for plans (Preparedness, Risk Mitigation, etc)
Follow-up of efficiency of these plans
Validation of Risk & Hazard Maps
Support for Policies/Regulations and investments
Strategic advantage for negotiation
Damage Assessment System in major disasters
Other applications
Maputo, Mozambique, August of 2008

54. Maputo, Mozambique, August 18-23 of 2008
DesInventar
THANK YOU
Maputo, Mozambique, August of 2008