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WC Retro: Open Source Disaster Recovery

By Alex Steffen, posted on September 8, 2005.

How we respond to disasters is one of the key indicators by which we can judge progress towards a bright green world, especially since we can expect to see more and worse unnatural disasters and environmental refugees in coming decades.

Open Source Disaster Recovery: Case studies of networked collaboration is a very useful paper for getting a handle on the kind of responses we've long advocated here:

In the aftermath of recent disasters such as Hurricane Katrina in August of 2005 and the Southeast Asian tsunami of December 2004, efforts began at aiding recovery through the medium of the Web. In addition to the online endeavors of formal aid organizations, these recovery efforts included attempts to provide help through collaboration amongst distributed networks of volunteers. Although providing relief after a natural disaster is generally capital intensive and requires the resources of large organizations, some types of disaster assistance seem a prime target for these kinds of open source style projects. Data–driven relief, such as identifying resources, coordinating assistance to victims, publicizing services, and establishing communication standards are all areas of assistance where open collaboration might thrive.

The examples explored by the paper are ones readers will probably find familiar, involving as they do key players who are also Worldchanging contributors, like the South–East Asia Earthquake and Tsunami Blog and Katrina PeopleFinder. We are of course thrilled to be seeing our fellow Worldchangers' valuable work given the credit it deserves, but this paper also does the best job I've yet seen of explaining what they were, why they're important models, and what critical questions remain to be answered.

That kind of insightful questioning is important, because as we more to more collaborative models of disaster response and reconstruction (take, for instance Open Source Humanitarian Design) we have to be able to divide what works from what sounds good. I'd hope this is a field that sees much more study.
(photo: NatGeo)

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