Robin Wilson1, Elisabeth Zu Erbach-Schoenberg1, Maximilian Albert2, Daniel Power3, Simon Tudge3, Miguel Gonzalez3, Sam Guthrie4, Heather Chamberlain1, Christopher Brooks1, Christopher Hughes5, Lenka Pitonakova3, Caroline Buckee6, Xin Lu7, Erik Wetter8, Andrew Tatem1, Linus Bengtsson9. 1. Flowminder Foundation, Stockholm, Sweden; Geography & Environment, University of Southampton, Southampton, UK. 2. Flowminder Foundation, Stockholm, Sweden; Faculty of Engineering & the Environment, University of Southampton, Southampton, UK. 3. Flowminder Foundation, Stockholm, Sweden; Electronics & Computer Science, University of Southampton, Southampton, UK. 4. Flowminder Foundation, Stockholm, Sweden; Politics & International Relations, University of Southampton, Southampton, UK. 5. Flowminder Foundation, Stockholm, Sweden; Electronics & Computer Science, University of Southampton, Southampton, UK; Oxford Internet Institute, University of Oxford, Southampton, UK. 6. Flowminder Foundation, Stockholm, Sweden; Center for Communicable Disease Dynamics, Harvard T.H. Chan School of Public Health, Boston, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA. 7. Flowminder Foundation, Stockholm, Sweden; Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden; College of Information System and Management, National University of Defense Technology, Changsha, China. 8. Flowminder Foundation, Stockholm, Sweden; Stockholm School of Economics, Stockholm, Sweden. 9. Flowminder Foundation, Stockholm, Sweden; Dept. of Public Health Sciences, Karolinska Institute, Sweden.
Abstract
INTRODUCTION: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. METHODS: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. RESULTS: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. DISCUSSION: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.
INTRODUCTION: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. METHODS: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. RESULTS: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. DISCUSSION: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.
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