On a recent mudslide in Washington

Landslide in Washington

There was a recent major mudslide in Washington. Our tool available at https://grait-dm.gatech.edu/demo-multi-source-integration/ collected the data from social networks about this event. The related data from Twitter, YouTube and Instagram is available here.

Motivation

Information technology (IT), and specifically ubiquitous wireless connectivity, has provided precious communications during and after the 2011 Tohoku Earthquake. Studies have shown the rapid propagation of very useful information, e.g., the gradual reopening of Tokyo Metro lines during the night of March 11, through social networks such as Twitter.

Big Data Will Transform Disaster Management, Again. We believe that the evolution of IT, under the concept of Big Data, will again transform disaster management. By “Big Data” we mean terabytes and petabytes of data related to a disaster than can be used to study the three main phases of disaster management: preparedness for, response to, and recovery from disasters. From IT point of view, Big Data is enabled by the convergence of several concurrently developed technologies: (1) powerful and inexpensive sensors, (2) ubiquitous wireless networks, and (3) affordable and distributed computing clouds. To achieve these goals, we propose the formation of the SAVI for Global Research on Applying Information Technology to Support Effective Disaster Management (GRAIT-DM).

Objectives

GRAIT-DM is a SAVI project for Global Research on Applying Information Technology to support effective Disaster Management.

The main goals of this SAVI are:

  1. the articulation of the research area covering Big Data Applied to Disaster Management, and
  2. the community-building efforts to create a critical mass and leverage synergies among existing disciplines related to disaster management and big data management.

The main SAVI activities focus on the community building aspects.

NSF support

The SAVI is primarily funded by National Science Foundation by CNS (1250260). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding agencies and participating companies.