Generation of Historical Vulnerability Indices using a DesInventar Database

1. Generation of Historical Vulnerability Indices using a DesInventar Database Julio Serje, Deepa Chavali and Sujit Mohanty

2. Introduction • Concept • The InDisData project • Methodology and Tool - DesInventar • The Orissa Experience • Qualitative results

3. in·dex(în¹dèks´) noun pluralin·dex·es or in·di·ces (-dî-sêz´) a. Something that serves to guide, point out, or otherwise facilitate reference… b.A number derived from a formula, used to characterize a set of data… Excerpted from The American Heritage« Dictionary of the English Language, Third Edition ® 1996 by Houghton Mifflin Company..

4. Historical Vulnerability Will be defined and calculated based on: • Patterns: repeated periodic occurrence of losses • Trends: increasing magnitude of losses • Impact: high losses being caused by low magnitude events

5. The InDisData Project • A database of disasters to understand trends and patterns. • A systematic geo-referenced inventory of small, medium and large-scale disasters for past 30 years. • To rationalize decision making for disaster preparedness, as well as providing an objective base for vulnerability assessment and priority setting. • To support planning & policy decisions for disaster preparedness and mitigation.

6. Orissa Pilot Process • Data collected for 30 districts and 314 blocks from newspapers over a period of 32 years. • Data collected from media is compared with Government records. • Institutionalization with Government for sustainability. • Interpretation and analysis of the data shows new dimensions of risk & vulnerabilities of the State. • Orissa ‘Vulnerability Analysis Report’ is being prepared in association with ‘Center for Development Studies’.

7. DesInventar • A methodology • A tool • The previous experience in Latin America

8. DesInventar Methodology • Disaggregation of the effects • Geo-referenced data • Inclusion of Small and Medium Disasters

9. DesInventar The Software Tools Stand-alone and Web-enabled version http://www.desinventar.org

10. Preliminary Findings • Epidemics and cyclones are the greatest causes of deaths • Epidemics are highly associated with floods, but also occur as independent incidents. • Fire is the greatest cause of household destruction, comparable to Cyclone. • Floods affect people more than any other type of disaster.

11. Impact on Life Epidemics (19,963) Cyclone (20,449) Number of people killed in disasters in Orisa

12. Impact on Property Number of Houses Destroyed in Disasters Orissa Cyclone (376,285) Fire (436,212) Floods (135485)

13. Impact on Livelihood Number of people affected Drought(3’408,999) Cyclone(11’633,140) Flood (31’395,654) Rains (3’776,359)

14. Patterns: floods Total number of Victims and Affected by Floods in Orissa

15. Pattern: Epidemics People Killed by Epidemics in Orissa

16. Spatial Distribution of Disasters

17. Relation Floods-Epidemics Number of reports of floods and people killed by epidemics, 11 years, with apparently non-flood related epidemics.

18. Spatial Distribution of Floods and Epidemics

19. Relation Floods-Epidemics Number of reports in floods and people killed by epidemics, 11 years, in 5 less-flood prone districts. Districts of Koraput, Kandhamal, Kalahand, Rayadada and Gajapat

20. Trend: Epidemics Ascending trend of the effects of epidemics in Orissa.

21. Trend: Fire effects on Housing

22. Pattern: Fire Seasonal Seasonal Variation in Fire Pattern

23. Way forward: • Definition of a methodology to generate a numeric index based on trends, patterns and impact • Calculation of these indices for Orissa • Comparison of these indices against other vulnerability index • Fine tuning of the whole process • Use of the indices in Risk Assessment

24. InDisData is supported by: Ministry of Home Affairs National Institute of Disaster Management NIDM United Nations Development Programme UNDP The Network for Social Studies on Disaster Prevention in Latin America

25. THANK YOU