Disaster Response and Recovery Using Spatial Analysis and Clustering

The aftermath of recent disasters like earthquakes and tsunamis has highlighted the shortcomings of the present disaster response and recovery mechanism. Large amount of random data is generated during and after a disaster. Deriving meaningful insights from this data can help in effective disaster management practices like preparation, response and recovery. In this paper we propose a model that performs spatial analysis and clustering on the real time as well as static data. The various data used for analytics are disaster data, population density, resource information data and social feeds. The Nepal earthquake of 25th April, 2015, is used as a case study example. The output of this model is layered maps and priority clusters. These maps and clusters can help the rescue teams and the disaster victims in the disaster response and recovery process