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Big data for humanitarian assistance and disaster response
Decker, Kevin M.
Frost, Eric G.Coast, MarkRobinson, Kevin
viii, 36 p. : ill.
We now live in a world in which vast amounts of data are created and stored on a constant and ongoing basis. The effects of globalization along with the advent of internet and mobile communications has created an opportunity for more effective analysis and response to crises through the use of big data analysis and network science theory. The world economies once separated by geography, culture, and language have become increasingly interconnected as the ability to transport products and communicate ideas has become easier. As the communication and physical divides continue to shrink, the need to address problems in the world more effectively and efficiently increases. The onset, pace, and scope of global disasters is growing faster than our collective ability to respond to such shocks. A byproduct of our world becoming interconnected is that disasters which directly affect a specific region also may have a global effect which transcends national borders. What started as a natural disaster in a given geographic area can evolve into an economic shock that hits regions not directly affected by the initial disaster. These secondary shocks invariably damage the regions least able to effectively manage them. The analysis of large data sets of complex systems, or big data analysis, allows for the study of trends and prediction of outcomes for future action. Applying network science theory to a large data set adds depth to this analysis by examining clusters of related entities and the value of their linkages. This dual approach allows for the identification of macrolevel trends through big data analysis and integral structural linkages within the data set via network science analysis. Using this technique, development and humanitarian aid can then be distributed in a more effective manner, getting aid to the areas that can benefit from it the most.
Includes bibliographical references (p. 34-36).
Division of Undergraduate Studies
Master of Science (M.S.) San Diego State University, 2013
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