Emergency Information Management and Telecommunications - Trainer's Guide - 1st Edition (Disaster Management Training Programme, 78 p.): PART 1: EMERGENCY INFORMATION MANAGEMENT SYSTEMS (2 hours): 29. Data analysis and information production

Emergency Information Management and Telecommunications - Trainer's Guide - 1st Edition (Disaster Management Training Programme, 78 p.)

PART 1: EMERGENCY INFORMATION MANAGEMENT SYSTEMS (2 hours)

		(introduction...)
		3. Part 1: Learning objectives
		4. Emergency information management
		5. Emergency information management as a ''cycle''
		6. Identifying needs
		7. Identifying needs by phase
		8. Information needs for contingency planning
		9. Early warning information for complex emergencies
		10. Information needs for emergency response
		11. Information needs for rehabilitation and recovery activities
		12. What we sometimes assume
		13. What is often the real situation
		14. Information needs for mitigation activities
		15. Gathering data: choice of techniques and phases of disaster
		16. Choice of data gathering technique depends on:
		17. Managing data-gathering
		18. Establishing a baseline
		19. Common sources of baseline data
		20. Common sources of baseline data
		21. Quantitative vs. qualitative methods of data gathering
		22. Quantitative, statistical sampling techniques
		23. Qualitative data-gathering techniques
		24. Inter-agency approach to data gathering
		25. Tools for data-gathering
		26. Geographic Information Systems
		27. Presentation of GIS information
		28. Other tools for data-gathering
		29. Data analysis and information production
		30. Tools for data analysis and information production
		31. Information dissemination: management concerns
		32. Coordination structures and information dissemination
		33. Use of the media in information dissemination
		34. Institutional memory
		35. Steps in building institutional memory capacity

29. Data analysis and information production

Figure

Stress that the data analysis function is carried out to produce information that can facilitate decision-making. Note that it is the job of the data analyst to filter out that which is unreliable, inaccurate, or of low priority. Ask participants to provide examples of actual "flash", "priority", and "routine" messages they have received in emergency situations.

· Cross-checking and verification
· Reliability of sources; potential biases
· Need for accuracy vs. speed
· Translating and structuring incoming messages
· Filtering and prioritization: flash, priority, routine