Cross-Institute Datathon: Mobilisation and Emergency Responses to Natural Disasters, 21 July 2015
The Institute for Life Sciences, Southampton Marine and Maritime Institute and the Web Science Institute held a one day Cross-Institute Datathon on 21 July 2015, the aim of which was to develop interdisciplinary data analysis skills across the University.
CROSS INSTITUTE DATATHON: Mobilisation and Emergency Responses to Natural Disasters
Being able to manage and forecast natural disasters and medical epidemics has become an important topic as we become an even more inter-connected world. Before the wide-spread use of digital technology, the only way to monitor, analyse and understand issues such as the spread of an infectious virus or where disaster response teams need to deploy aid, was to use on-the-ground information, or by word of mouth. More often than not, the analysis would be performed post-event, thus limiting the responsiveness. However, given the prolific growth of Internet- and Web-enabled technologies and devices, researchers and scientists are now examining how the data that these new forms of digital devices produce can be used to support humanitarian issues. Take for instance Google Flu Trends, a tool that uses the data produced from humans searching on Google and combines it with other sources of data in order to forecast the next outbreak and spread of flu. Similarly, there are various crowdsourcing platforms being developed which enables individuals within disaster zones to provide on-the-ground mapping information to provide an up-to-date view of areas that require help, or even, in the case of the Haiti and Nepal earthquakes, provide a high-resolution map of the city within 24 hours. Natural disasters and extreme events are much more frequent that one assumes. The resilience of coastal communities (in particular) in different parts of the world to cyclones, typhoons, tsunamis and hurricanes and the manner in which life should bounce back to being at least as good as it used to be before the event needs to be studied. We also need to study population movement and the spread of disease after a major disaster to help prepare better for the next one. These are just some examples of how integrating data from different types of datasets can be used can provide insight into world-wide issues.