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World Congres on Disaster Management New Delhi

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This is a presentation on Disaster Inventory, presented during the World Congress on Disaster Management in New Delhi

This is a presentation on Disaster Inventory, presented during the World Congress on Disaster Management in New Delhi

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  • 1. Generation of Historical Vulnerability Indices using a DesInventar Database Sujit Mohanty Manager-Disaster Information Systems World Congress on Disaster Management New Delhi
  • 2. Introduction
    • Concept
    • The InDisData project
    • Methodology and Tool - DesInventar
    • The Orissa Experience
    • Qualitative results
  • 3. in·dex (în¹dèks´) noun
    • plural in·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
    • Patterns : repeated periodic occurrence of losses
    • Trends : increasing magnitude of losses
    • Impact : high losses being caused by low magnitude events
    Will be defined and calculated based on:
  • 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
    • http://www.desinventar.org
  • 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
  • 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 Number of people killed in disasters in Orisa Epidemics (19,963 ) Cyclone (20,449)
  • 12. Impact on Property Number of Houses Destroyed in Disasters Orissa Fire (436,212) Cyclone (376,285) Floods (135485)
  • 13. Impact on Livelihood Number of people affected Flood ( 31’395,654) Cyclone(11’633,140) Drought(3’408,999) 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

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