Exploring Web-Available Data for Macro-indicators of Humanitarian Intervention in the aftermath of Disasters. / Monaghan, Asmat.

2018. 466 p.

Research output: ThesisDoctoral Thesis

Unpublished

Documents

Abstract

In 2015 US$28bn was spent in international humanitarian assistance, of this US$10.8bn was raised through United Nations coordinated appeals; 45% short of the US$19.8bn needed by the United Nations. As pressure increases to do more with less, measuring the effectiveness of past humanitarian aid to inform the response to future disasters becomes an imperative. In the humanitarian domain, however, the difficulties and obstacles to obtaining authoritative empirical data to evaluate the effect of humanitarian intervention at the detail level are significant.
In response, this study explores the ability of Web-available curated data in providing macro-level indicators of humanitarian intervention in the aftermath of disasters. In so doing it identifies three macro-level indicators that suggest mean disaster survival rates when plotted by year, by humanitarian aid per person and by population growth may signpost the effectiveness of humanitarian intervention.
En-route to finding these macro-indicators, the study clarifies the need for domain-specific key data artefacts, referred to here as data scaffolds, to support viable data analysis; develops a Data Veracity framework (DVf) as a toolset to equitably and consistently evaluate the veracity of sourced data; defines a prototype Master Disaster Classification (MDC) model; and creates a baseline amalgamated Master Set of Global Disasters (MSGD). Finally, over and above the knowledge contribution of these created artefacts and the design theory that they support, this work provides a foundation for future research in the humanitarian and data science domain.
Original languageEnglish
QualificationPh.D.
Awarding Institution
Supervisors/Advisors
Award date1 Jun 2018
Publication statusUnpublished - 2018
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 30328085