Digital Disaster Responders

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Information about Digital Disaster Responders
Technology

Published on October 22, 2010

Author: gislio

Source: slideshare.net

Description

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.

Digital Disaster Responders Gísli Ólafsson Disaster Management Advisor Email: gislio@live.com Blog:http://blog.disasterexpert.org Twitter: @gislio

Disasters

Images of people in need

The longing to lend a hand

But not everyone can go there

The Birth Of Digital Disaster Responders

Five types of crowd involvement

1. Advocacy

Social Media

Text „HAITI“ to 90999 Campaign

Provide advise to affected population

2. Crowdsourcing Situation Information

Mission 4636

Information Economics

3. Crowd Generated Maps

Open Street Maps

4. Crowd Generated Solutions

CrisisCamp Haiti

Big World – Small World

30 second learning curve

Data Collection Data Collation/ Processing Data Analysis Information Dissemination Decisions 5. Crowd Based Information Management

Information Management – on the ground

Connectivity

Sources of Information

noise becomes data when it has a cognitive pattern data becomes information when its assembled into a coherent whole which can be related to other information information becomes knowledge when its integrated with other information in a form that is useful for making decisions and determining actions, knowledge becomes understanding when related to other knowledge in a manner useful in anticipating, judging and acting, understanding becomes wisdom when its informed by purpose, ethics, principals, memory and projection Dee Hock, 1996 Why we need information

noise becomes data when it has a cognitive pattern

data becomes information when its assembled into a coherent whole which can be related to other information

information becomes knowledge when its integrated with other information in a form that is useful for making decisions and determining actions,

knowledge becomes understanding when related to other knowledge in a manner useful in anticipating, judging and acting,

understanding becomes wisdom when its informed by purpose, ethics, principals, memory and projection

DATA VOLUME = # of Forms Collected x Number/Type of Questions x Periodicity (Frequency of data collection) Data Volume

Mechanical Turks

Translation Frameworks

Swift River

Collaborative Analysis

The Way Forward

Facilitated Knowledge Management Collaborative Workgroups Social Networks Evolution

Discussion Boards Comments Podcasting Shared Calendars Microblogging Versioning Profiles Document Libraries Team Sites Tags Blogs Task Lists Wikis Surveys Ratings EnterpriseCollaboration Capabilities SocialComputing Technologies DisasterCommunities The Need for Communities

The crowd and cloud based crisis informationmanagement has to be a joint effort wherewe combine multiple technologies to solve the issues ahead. A Common Cause

Governance Policies

Legal, Compliance & Security

Confusion

Twitter:@gislio@DavidClinchNews@poplifegirl@patrickmeier@whiteafrican@edjez@andrejverity@crisismappers@crisiscamp@ushahidiWebsites/Blogs: http://blog.disasterexpert.nethttp://crisiscamp.org/http://www.rhok.org/http://www.crisismappers.net/http://ushahidi.orghttp://storyful.netFurther resources

Twitter:

@gislio

@DavidClinchNews

@poplifegirl

@patrickmeier

@whiteafrican

@edjez

@andrejverity

@crisismappers

@crisiscamp

@ushahidi

http://blog.disasterexpert.net

http://crisiscamp.org/

http://www.rhok.org/

http://www.crisismappers.net/

http://ushahidi.org

http://storyful.net

Source: xkcd, http://xkcd.com/386/ And Finally...

Backup slides

On and off workloads (e.g. batch job)Over provisioned capacity is wasted Time to market can be cumbersome Successful services needs to grow/scale Keeping up w/growth is big IT challenge Complex lead time for deployment“On and Off “ “Growing Fast“ Inactivity Period Compute Compute Average Usage Usage Average Time Time Services with micro seasonality trends Peaks due to periodic increased demandIT complexity and wasted capacity Unexpected/unplanned peak in demand Sudden spike impacts performance Can’t over provision for extreme cases “Unpredictable Bursting“ “Predictable Bursting“ Optimal Cloud Workload Patterns Compute Compute Average Usage Average Usage Time Time

On and off workloads (e.g. batch job)

Over provisioned capacity is wasted

Time to market can be cumbersome

Successful services needs to grow/scale

Keeping up w/growth is big IT challenge

Complex lead time for deployment

Services with micro seasonality trends

Peaks due to periodic increased demand

IT complexity and wasted capacity

Unexpected/unplanned peak in demand

Sudden spike impacts performance

Can’t over provision for extreme cases

Organizations 2.0

Normal (routine emergencies) People relate as rolesPeople are the problemOrgs are the solutionDisaster Relate as PeopleOrgs are the problemPeople are the solutionCatastrophe Society breaks downNo relationships – survivalOnly happened a few times (Hiroshima)E=mc2 of disasters

People relate as roles

People are the problem

Orgs are the solution

Relate as People

Orgs are the problem

People are the solution

Society breaks down

No relationships – survival

Only happened a few times (Hiroshima)

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