Capturing Real-Time Data in Disaster Response Logistics

Ano: 2016 | Volume: 9 | Número: 1

Autores: Kezban Yagci Sokat, Rui Zhou, Irina S. Dolinskaya, Karen Smilowitz, Jennifer Chan

Palavras-chave: humanitarian logistics, real-time data, classification, logistical modeling, Typhoon Haiyan

Resumo Inglês:

The volume, accuracy, accessibility and level of detail of near real-time data emerging
from disaster-affected regions continue to significantly improve. Integration of dynamically evolving
in-field data is an important, yet often overlooked, component of the humanitarian logistics models.
In this paper, we present a framework for real-time humanitarian logistics data focused on use in
mathematical modeling along with modeling implications of this framework. We also discuss how one
might measure the attributes of the framework and describe the application of the presented framework
to a case study of near real-time data collection in the days following the landfall of Typhoon
Haiyan. We detail our first-hand experience of capturing data as the post-disaster response unfolds
starting on November 10, 2013 until March 31, 2014 and assess the characteristics and evolution of data
pertaining to humanitarian logistics modeling using the proposed framework. The presented logistical
content analysis examines the availability of data and informs modelers about the current state of
near real-time data. This analysis illustrates what data is available, how early it is available, and how
data changes after the disaster. The study describes how our humanitarian logistics team approached
the emergence of dynamic online data after the disaster and the challenges faced during the collection
process, as well as recommendations to address these challenges in the future (when possible) from an
academic humanitarian logistics perspective.