Digital Disaster Responders

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A talk on the rise of digital disaster responders following the Haiti earthquake. Explains the different ways to use the crowds to help out. First to be given in Iceland on October 22nd, 2010.

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  • Lots/Lack of dataLack of (processed) informationAt times no access to baseline dataNo data collection/information processes yet in placeNeed for rapid decision-making, prioritisation – also IM prioritisationOverwhelming demand for information from all sidesClients of IM products are both on-site/in country and “not yet on site”/about to deploy
  • When asked what was his most important discovery within the area of disaster mangement, Mileti said that 40 years of his work could be sumarized into this one finding.Mileti said we could define three phases of crisis. The first one is the Routine Emergency phase, something our fire figthers and police officers have to deal with every day. Then there are the disasters which overwhelm the daily responders and cause us to change our behaviour. Finally there are the catastrophees which luckliy only happen very seldom throughout our history.But lets look at these different phases and the behaviour of people during these phases. During the normal routine emergency phase, we look towards organizations such as the police to assist us in the problems we have. And when we interact with these organizations we related to the people we deal with through the roles they represent. In other words, when my house gets on fire, I call the fire department and the firemen will come and help me. I do not refer to them by their names or who they are as persons, in my mind they are representatives of the organization they work for.However during a disaster there is a radical change in our behaviour. The number of organizations involved increases and all of a sudden a coordinated approach is needed. But at this time the organizations become the problem. Political and sometimes financial motives of those organizations hinder them from working efficiently with each other. In the international humanitarian world we often see different UN organizations competing for the attention of the media and the donors instead of collaborating with other organizations involved in the same response. Very often the leadership of those organizations are playing a political game during these periods, something that can realy affect the efficiency of the response. But luckily there is a solution to this problem and in this case it is people. It is people at different levels of the organization which feel human emotions about those affected by the disaster and because of these human emotions are willing to break down and reach out of the organizational silos that they represent. It is through these kind of connection between people within the different organizations that work actually gets done. We must therefore learn to leverage and build up in advance those personal relationships between the people in these organizations.In the third phase, catastrophie, Mileti points out that society breaks down and the basic human instinct of survial kicks in. During this phase relationships no longer matter, only yourself matter. However he points out that lucklily this phase does not last for very long and only happens extreemly seldom.A key point to understand about his findings is that it is not a formal organizational declaration that transititions us between these different phases, but rather it is determined by the behaviour of the people involved.So keep this in mind as I go through the next few slides and discuss methods for effective coordination.
  • Digital Disaster Responders

    1. 1. Digital Disaster Responders<br />Gísli Ólafsson<br />Disaster Management Advisor<br />Email: gislio@live.com<br />Blog:http://blog.disasterexpert.org<br />Twitter: @gislio<br />
    2. 2. Disasters<br />
    3. 3. Images of people in need<br />
    4. 4. The longing to lend a hand<br />
    5. 5. But not everyone can go there<br />
    6. 6. The Birth Of Digital Disaster Responders<br />
    7. 7. Five types of crowd involvement<br />
    8. 8. 1. Advocacy<br />
    9. 9. Social Media<br />
    10. 10. Text „HAITI“ to 90999 Campaign<br />
    11. 11. Provide advise to affected population<br />
    12. 12. 2. Crowdsourcing Situation Information<br />
    13. 13. Mission 4636<br />
    14. 14. Information Economics<br />
    15. 15. 3. Crowd Generated Maps<br />
    16. 16. Open Street Maps<br />
    17. 17. 4. Crowd Generated Solutions<br />
    18. 18. CrisisCamp Haiti<br />
    19. 19. Big World – Small World<br />
    20. 20. 30 second learning curve<br />
    21. 21. Data Collection<br />Data Collation/<br />Processing<br />Data Analysis<br />Information Dissemination<br />Decisions<br />5. Crowd Based Information Management<br />
    22. 22. Information Management – on the ground<br />
    23. 23. Connectivity<br />
    24. 24. Sources of Information<br />
    25. 25. <ul><li> noise becomes data when it has a cognitive pattern
    26. 26. data becomes information when its assembled into a coherent whole which can be related to other information
    27. 27. information becomes knowledge when its integrated with other information in a form that is useful for making decisions and determining actions,
    28. 28. knowledge becomes understanding when related to other knowledge in a manner useful in anticipating, judging and acting,
    29. 29. understanding becomes wisdom when its informed by purpose, ethics, principals, memory and projection</li></ul> Dee Hock, 1996<br />Why we need information<br />
    30. 30. DATA VOLUME = <br /># of Forms Collected x Number/Type of Questions x Periodicity (Frequency of data collection)<br />Data Volume<br />
    31. 31. Mechanical Turks<br />
    32. 32. Translation Frameworks<br />
    33. 33. Swift River<br />
    34. 34. Collaborative Analysis<br />
    35. 35. The Way Forward<br />
    36. 36. Facilitated<br />Knowledge Management<br />Collaborative<br />Workgroups<br />Social<br />Networks<br />Evolution<br />
    37. 37. Discussion Boards<br />Comments<br />Podcasting<br />Shared Calendars<br />Microblogging<br />Versioning<br />Profiles<br />Document Libraries<br />Team Sites<br />Tags<br />Blogs<br />Task Lists<br />Wikis<br />Surveys<br />Ratings<br />EnterpriseCollaboration<br />Capabilities<br />SocialComputing<br />Technologies<br />DisasterCommunities<br />The Need for Communities<br />
    38. 38. The crowd and cloud based crisis informationmanagement has to be a joint effort wherewe combine multiple technologies to solve the issues ahead.<br />A Common Cause<br />
    39. 39. Governance Policies<br />
    40. 40. Legal, Compliance & Security<br />
    41. 41. Confusion<br />
    42. 42. <ul><li>Twitter:
    43. 43. @gislio
    44. 44. @DavidClinchNews
    45. 45. @poplifegirl
    46. 46. @patrickmeier
    47. 47. @whiteafrican
    48. 48. @edjez
    49. 49. @andrejverity
    50. 50. @crisismappers
    51. 51. @crisiscamp
    52. 52. @ushahidi</li></ul>Websites/Blogs:<br /><ul><li>http://blog.disasterexpert.net
    53. 53. http://crisiscamp.org/
    54. 54. http://www.rhok.org/
    55. 55. http://www.crisismappers.net/
    56. 56. http://ushahidi.org
    57. 57. http://storyful.net</li></ul>Further resources<br />
    58. 58. Source: xkcd, http://xkcd.com/386/<br />And Finally...<br />
    59. 59. Backup slides<br />
    60. 60. <ul><li>On and off workloads (e.g. batch job)
    61. 61. Over provisioned capacity is wasted
    62. 62. Time to market can be cumbersome
    63. 63. Successful services needs to grow/scale
    64. 64. Keeping up w/growth is big IT challenge
    65. 65. Complex lead time for deployment</li></ul>“On and Off “<br />“Growing Fast“ <br />Inactivity<br />Period <br />Compute <br />Compute <br />Average Usage<br />Usage<br />Average<br />Time <br />Time <br /><ul><li>Services with micro seasonality trends
    66. 66. Peaks due to periodic increased demand
    67. 67. IT complexity and wasted capacity
    68. 68. Unexpected/unplanned peak in demand
    69. 69. Sudden spike impacts performance
    70. 70. Can’t over provision for extreme cases </li></ul>“Unpredictable Bursting“ <br />“Predictable Bursting“ <br />Optimal Cloud Workload Patterns<br />Compute <br />Compute <br />Average Usage<br />Average Usage<br />Time <br />Time <br />
    71. 71. Organizations 2.0<br />
    72. 72. Normal (routine emergencies)<br /><ul><li>People relate as roles
    73. 73. People are the problem
    74. 74. Orgs are the solution</li></ul>Disaster<br /><ul><li>Relate as People
    75. 75. Orgs are the problem
    76. 76. People are the solution</li></ul>Catastrophe<br /><ul><li>Society breaks down
    77. 77. No relationships – survival
    78. 78. Only happened a few times (Hiroshima)</li></ul>E=mc2 of disasters<br />

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