Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence: Syrian refugee crisis as a case study

Abstract

Purpose

The purpose of this paper is to propose an effective knowledge elicitation method and representation scheme that empowers humanitarian assistance/disaster relief (HA/DR) analysts and experts to create analytic models without the aid of data scientists and methodologists while addressing the issues of complexity, collaboration, and emerging technology across a diverse global network of HA/DR organizations.

Design/methodology/approach

The paper used a mixed-methods research approach, with qualitative research and analysis to select the model elicitation method, followed by quantitative data collection and evaluation to test the representation scheme. A simplified analytic modeling approach was created based on emerging activity-based intelligence (ABI) analytic methods.

Findings

Using open source data on the Syrian humanitarian crisis as the reference mission, ABI analytic models were proven capable in modeling HA/DR scenarios of physical systems, nonphysical systems, and thinking.

Practical implications

As a data-agnostic approach to develop object and network knowledge, ABI aligns with the objectives of modeling within multiple HA/DR organizations.

Originality/value

Using an analytic method as the basis for model creation allows for immediate adoption by analysts and removes the need for data scientists and methodologists in the elicitation phase. Applying this highly effective cross-domain ABI data fusion technique should also supplant the accuracy weaknesses created by traditional simplified analytic models.

Citation

Widener, D.V., Mazzuchi, T.A. and Sarkani, S. (2018), "Simplifying humanitarian assistance/disaster relief analytic models using activity-based intelligence: Syrian refugee crisis as a case study", Disaster Prevention and Management, Vol. 27 No. 1, pp. 60-73. https://doi.org/10.1108/DPM-06-2017-0134