Your SlideShare is downloading. ×
Spatial ICTs for risk identification and risk reduction:Three geographic scales and three challenges
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Spatial ICTs for risk identification and risk reduction: Three geographic scales and three challenges


Published on

International Day for Disaster Reduction at the World Bank …

International Day for Disaster Reduction at the World Bank

Disaster Risk Management in the Information Age

A joint training workshop by GICT, GFDRR, infoDev and LCSUW to mark the International Day for Disaster Reduction

1 Like
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. Spatial ICTs for risk identification and risk reduction: Three geographic scales and three challenges Uwe Deichmann Development Research Group World Bank, Washington DC <> International Day on Disaster Risk Reduction at the World Bank Disaster Risk Management in the Information Age October 8-9, 2008
  • 2. ICTs are widely used, but challenges remain
    • Successful shift from disaster response to risk reduction
    • Bank support for risk analysis and risk management at all spatial scales
    • Spatial ICTs play a central role
    • GIS, GPS, remote sensing – linked by internet and other communication technologies
    • But: Technology is not the main problem. The bottlenecks are institutional!
  • 3. Bank initiatives at three geographic scales
    • Global natural disaster risk
    • Country catastrophic risk assessment
    • Local risk identification
    • Awareness raising, priority setting, screening tool
    • Improving baseline information, methodologies, tools
    • Support specific interventions: mitigation & transfer
  • 4. The standard risk assessment model applies across spatial scales Damages Losses Mitigation or risk transfer policy analysis, costs/benefits e.g., average annual losses, loss exceedance curves damage ratios Hazard probability Exposure Vulnerability people, assets social/econ/phys conditions geophysical drivers
  • 5. Combining information on hazards … Severe Storms, 1981 - 2000 World Bank/Columbia University: Natural Disaster Hotspots Study 2005 based on storm track data compiled by UNEP-GRID Geneva Cyclone Frequency Global Analysis: Natural Disaster Risk Hotspots
  • 6. … and exposure … Population distribution
  • 7. … to generate risk profiles Multi-hazard mortality risk hotspots Updated global analysis forthcoming in the UN/WB Global Assessment Report on Disaster Risk Reduction 2009
  • 8. Country catastrophic risk assessment
    • Operational risk assessments
      • E.g., Central America Probabilistic Risk Assessment
      • National level assessments in hotspot countries
    • Knowledge management: tools and guidance
      • MIRISK open source tool for risk assessment and guidelines on what to do about it
      • “ Guidance Note for Common Country Catastrophic Risk Assessment Methodology (C3RAM)”, GFDRR
      • Post disaster information sharing: “Using Data for Disaster Response” (PREM/GFDRR)
  • 9. Local risk identification: Use of very high resolution satellite data
    • Image derived physical risk factors and exposure data
    • Complements GPS field data collection
    • Supports local risk identification
    • Case studies: Legaspi (Phl) and Sana'a (Yem)
  • 10. Challenges
    • Capacity
      • Insufficient at local levels
      • Leading to highly centralized disaster management
    • Coordination
      • Inter-agency coordination within countries
      • Internationally (UN/national/NGOs) during disaster response
    • Content
      • Data and tools: limited access and black box models
      • Data readiness
  • 11. What to do
    • Capacity
      • Learn from decentralization of other government functions
      • Invest in learning at the local level
    • Coordination
      • Use mix of incentives and enforcement while minimizing coordination costs (e.g., spatial data infrastructure)
      • High level agreements on binding protocols for IT use during disaster response
    • Content
      • Invest in data and analytical tools as public goods
      • Ensure data readiness well before disaster strikes