Presented by Adele Waugaman, Harvard Humanitarian Initiative Fellow & Independent Consultant. October 15, 2012. Washington, D.C. "DC Design Week: Design for Disaster Relief," held in partnership with AIGA DC.

1. Technology & Disaster Relief
Credits: UN Foundation
Adele Waugaman
@Tech4Dev
www.adelewaugaman.net

2. Rising Incidence of Disasters
UN/Evan Schneider

3. Traditional Disaster
Preparedness & Relief
Interventions
Source: Lucas Oleniuk, Toronto Star Source: OCHA

4. Rising Access to ICTs
Cell-Life

5. Disaster Relief & ICTs
UN/Evan
Schneider
TSF

6. Case Study: Crowdsourcing
Geolocation Information
Using Crowdsourcing to Locate Hospitals on Maps
One of more difficult Facts: Examples Haiti operation Relief 2.0 Activities in the health
Fast problems during the of Disaster was determining the location of
facilities. OCHA turned to the volunteer and technical communities to map these facilities, asking Crisis
2010 Haiti Earthquake Response
Mappers and Sahana—examples of the Volunteer & Technical Communities—if they could crowdsource
the effort to geo-locate 105 health facilities thatLocate Hospitals The Maps to the crowd went
Using Crowdsourcing to had no location data. on request
One of more difficult problems during the Haiti operation was determining the location of health
out at 2:40AM on January 22. Approximately volunteer and technical communities to map these facilities,
facilities. OCHA turned to the 35
asking Crisis Mappers and Sahana—examples of the Volunteer & Technical Communities—if
hours later, the team working on the problem had located the de facto list of 102 of the 105 missing
they could data into the Sahana to geo-locate 105 health facilities verified each
hospitals, inputting all the crowdsource the effortdisaster management system. They had that had no location
facility by having an OpenStreetMap member went out at hospital or clinicJanuary 22. Approximately 35
data. The request to the crowd locate the 2:40AM on on high-resolution satellite
imagery at 15cm resolution and verify working on the problem located at the submitted coordinates. the 105
hours later, the team that health facility was had located the de facto list of 102 of
missing hospitals, data formats, data into the Sahana disaster management system. They
Sahana made the data available in openinputting all thewhich became one of the best resources for
had verified each facility by having an OpenStreetMap member locate the hospital or clinic on
health facility data for the next satellite imagery at 15cmunique individualsverify that health facility was
high-resolution month. More than 8,000 resolution and visited the site or pulled
locatedfrom the feed. Crowdsourcing transformedtheproject that normally would require several days into just
at the submitted coordinates. Sahana made a data available in open data
formats, which became one of the best resources for health facility data for the
over one day.
next month. More than 8,000 unique individuals visited the site or pulled
from the feed. Crowdsourcing transformed a project that normally would require
several days into just over one day.
Translating Creole through Mission 4636
Mission 4636 facilitated the collaboration that a team at Stanford established with
members of the Haitian diaspora to translate thousands of messages from Creole
into English. Haitian Creole has only 12 million speakers worldwide, 9

7. facilities. OCHA turned to the volunteer and technical communities to map these facilities,
asking Crisis Mappers and Sahana—examples of the Volunteer & Technical Communities—if
they could crowdsource the effort to geo-locate 105 health facilities that had no location
Case Study: Crowdsourcing
data. The request to the crowd went out at 2:40AM on January 22. Approximately 35
hours later, the team working on the problem had located the de facto list of 102 of the 105
Translations
missing hospitals, inputting all the data into the Sahana disaster management system. They
had verified each facility by having an OpenStreetMap member locate the hospital or clinic on
high-resolution satellite imagery at 15cm resolution and verify that health facility was
located at the submitted coordinates. Sahana made the data available in open data
Translating Creole through the 4636 Shortcode
formats, which became one of the best resources for health facility data for the
next month. More than 8,000 unique individuals visited the site or pulleda team at Stanford established with
Mission 4636 facilitated the collaboration that
from the feed. Crowdsourcing transformedHaitian diaspora towould require
members of the a project that normally translate thousands of messages from Creole
several days intointo English. Haitian Creole has only 12 million speakers worldwide, 9
just over one day.
million of which live in Haiti. In the first days of the disaster, Rob Munro (a
Translating Creole through Mission 4636
Mission 4636 facilitated the collaboration that a team Stanford)established with
computational linguist at at Stanford recruited Creole speakers from Facebook and other
members of the Haitian diaspora to sites, asking them to begin translating tweets, SMS messages, and
public web translate thousands of messages from Creole
into English. Haitian Facebook posts million speakers worldwide, 9
Creole has only 12 coming from Haiti.
million of which live in Haiti. In the first days of the disaster, Rob Munro (a computational linguist at Stanford)
recruited Creole speakers from Facebook and other public web sites, asking them to begin translating tweets, SMS
More than 130,000 text messages went through 4636 during the first month of the
messages, and Facebook posts coming from Haiti. More than 130,000 text messages went through 4636 during
response. At its peak, the effort had 1,200 Haitians translating thousands of
the first month of the response. At its peak, the effort had 1,200 Haitians translating thousands of
messages per day, usually within day,minutes of their arrival. By February, their arrival. By February, Rob began
messages per 4.5 usually within 4.5 minutes of Rob began a partnership with
Crowdflower, a social venture for microtasking that was working social venture another social venturethat partners
a partnership with Crowdflower, a with Samasource, for microtasking with was working
on the ground who could recruit Haiti citizens to perform translation services from Haiti. Theon the ground who could
with Samasource, another social venture with partners effort transitioned to this
professional translation platform soon thereafter.
recruit Haiti citizens to perform translation services from Haiti. The effort transitioned
to this professionalin Haiti Provides a Vital Link
Cell Connectivity translation platform soon thereafter.
According to a report by the US Institute of Peace, “approximately 85 percent of Haitian
households had access to mobile phones at the time of the earthquake, and although 70 percent
of the cell phone towers in Port-au-Prince had been destroyed in the disaster, they were quickly
repaired and mostly back online before the 4636 number was operational.” Haitians sent
hundreds of thousands of messages out via SMS to Twitter, Facebook, Ushahidi, the

8. Challenges with Linking Volunteer
Networks & Aid Agencies
• Protocol
• Capacity building (training, setting expectations)
• Standards (protection, privacy, data security)
• Push v pull
• Funding
• Sustainability (workflow transition)
• Relevance (engagement of local actors)
OCHA Humanitarian Graphics

9. Case Study: Libya

10. Citizen-Centered Disaster
Relief Design
DataDyne.org
GSMA

11. Challenges with Direct Citizen
Engagement in Humanitarian Assistance
• Coordination
• Feedback
• Protocol
• Capacity building (training, setting expectations)
• Standards (protection, privacy, data security)
• Push v pull
• Funding
• Sustainability (workflow transition)
• Relevance (engagement of local actors)
WFP/John Wreford
WFP/John Wreford