Rapid Information and Communication Technology Assessment Team (RTAT): enabling the "hands and feet" to win the "hearts and minds"
Beeson, R. Travis
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Large-scale disasters severely damage local information and communication technology (ICT) infrastructure. This negatively impacts responders’ ability to communicate and collaborate with one another. As a result, humanitarian assistance (HA) response organizations cannot maintain situational awareness and efforts remain disjointed and inefficient. Out of the rubble of the Haiti earthquake, a cross-organizational collection of first responders created the Rapid ICT Assessment Team (RTAT) to conduct and share a holistic assessment of the ICT environment. However, RTAT has yet to solve the problem of efficiently and effectively collecting the ICT data and creating a shareable, common, ICT operational picture. Employing a campaign of experimentation (COE), this thesis analyzes RTAT with an Enterprise Architecture framework and Savvion process modeler and employs the Android based, mobile, spatial data collection applications Lighthouse and Open Data Kit (ODK) Collect to exploit the open source form builder ODK. RTAT founders, along with Bicol University and local volunteers, field tested the ODK forms with crowd sourcing techniques and when Typhoon Haiyan struck; they validated the organizational RTAT model and integrated assessments into the Pacific Disaster Center’s (PDC) DisasterAWARE collaborative website. This thesis highlights the disjointed rapid response ICT assessment community which lacks standard forms and unifying data standards. The COE validates using open source, spatial data collection tools and crowdsourcing techniques for even highly technical needs. However, the COE revealed programming logic limits of the ODK forms, and the imperfect back-end integration between RTAT and the PDC. Debates remain over the validity of qualitative, crowdsourced ICT assessments. Going forward, RTAT must refine its forms and lead the movement to harmonize HA community assessment data sets. Furthermore, future data collection tools must become operating system independent.
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