Capturing Real-Time Data in Disaster Response Logistics

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 case study of near real-time data collection in the days following landfall of Typhoon Haiyan. We detail our first-hand experience of capturing data as the post-disaster response unfolds starting November 10, 2013 to March 31, 2014 and assess the characteristics and evolution of data pertaining to humanitarian logistics modeling. We present a framework for real-time humanitarian logistics data focused on mathematical modeling, which reflects the integration of our recent experience, a survey of different communities producing data and disciplines using data, and a development of measures to evaluate quality of data. Additionally, we provide modeling implications of collected data based on our preliminary data analysis and the proposed framework. 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.