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

2018. 466 p.

Research output: ThesisDoctoral Thesis

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@phdthesis{50a5b734d6af4fc1a867398ba9e6cf98,
title = "Exploring Web-Available Data for Macro-indicators of Humanitarian Intervention in the aftermath of Disasters",
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.",
keywords = "Data Science, Humanitarian Supply Networks, Humanitarian Aid, Data Scaffolds, Data Veracity Framework, Data Veracity Toolset, Data Veracity Model, Data Veracity Profile, Data Veracity Index, Macro-indicators of Outcome, Macro-indicators of Impact, Macro-indicators of Effect, Humanitarian Disasters, Disaster Survival Rate, Royal Holloway, Asmat Monaghan, Data Preparation, Design Science Research, Design Cycle, Design Theory, Data Scaffold, Data Analytics, Soft Data Veracity, Firm Data Veracity, Master Disaster Classification (MDC), Master Set of Global Disasters (MSGD)",
author = "Asmat Monaghan",
year = "2018",
language = "English",
school = "Royal Holloway, University of London",

}

RIS

TY - THES

T1 - Exploring Web-Available Data for Macro-indicators of Humanitarian Intervention in the aftermath of Disasters

AU - Monaghan, Asmat

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Data Science

KW - Humanitarian Supply Networks

KW - Humanitarian Aid

KW - Data Scaffolds

KW - Data Veracity Framework

KW - Data Veracity Toolset

KW - Data Veracity Model

KW - Data Veracity Profile

KW - Data Veracity Index

KW - Macro-indicators of Outcome

KW - Macro-indicators of Impact

KW - Macro-indicators of Effect

KW - Humanitarian Disasters

KW - Disaster Survival Rate

KW - Royal Holloway

KW - Asmat Monaghan

KW - Data Preparation

KW - Design Science Research

KW - Design Cycle

KW - Design Theory

KW - Data Scaffold

KW - Data Analytics

KW - Soft Data Veracity

KW - Firm Data Veracity

KW - Master Disaster Classification (MDC)

KW - Master Set of Global Disasters (MSGD)

M3 - Doctoral Thesis

ER -