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Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen

Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen
Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen
Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm’s landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people’s preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.
climate change adaptation, migration, resilience, mobile network data, anomaly detection, disaster risk
1-15
Lu, Xin
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Wrathall, David J.
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Sundsøy, Pål Roe
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Nadiruzzaman, Md.
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Wetter, Erik
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Iqbal, Asif
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Qureshi, Taimur
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Tatem, Andrew J.
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Canright, Geoffrey S.
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Engø-Monsen, Kenth
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Bengtsson, Linus
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Lu, Xin
a681bac0-d6d1-4e8e-a642-4ce42ae2cc9d
Wrathall, David J.
ecd694c6-838c-4e24-b1cf-0005c700fcce
Sundsøy, Pål Roe
84d88a87-2120-4fb6-a48e-cc7176e3b5a8
Nadiruzzaman, Md.
454ee84a-7962-4a91-917e-df037b9e361f
Wetter, Erik
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Iqbal, Asif
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Qureshi, Taimur
c4da8b5a-aa68-4e66-9e64-ec36dfb35cde
Tatem, Andrew J.
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Canright, Geoffrey S.
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Engø-Monsen, Kenth
1487780b-85d1-4305-bd57-8673a462a509
Bengtsson, Linus
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Lu, Xin, Wrathall, David J., Sundsøy, Pål Roe, Nadiruzzaman, Md., Wetter, Erik, Iqbal, Asif, Qureshi, Taimur, Tatem, Andrew J., Canright, Geoffrey S., Engø-Monsen, Kenth and Bengtsson, Linus (2016) Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen. Climatic Change, 1-15. (doi:10.1007/s10584-016-1753-7).

Record type: Article

Abstract

Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm’s landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people’s preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.

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Accepted/In Press date: 8 July 2016
e-pub ahead of print date: 1 August 2016
Keywords: climate change adaptation, migration, resilience, mobile network data, anomaly detection, disaster risk
Organisations: Global Env Change & Earth Observation, WorldPop, Population, Health & Wellbeing (PHeW)

Identifiers

Local EPrints ID: 399186
URI: https://eprints.soton.ac.uk/id/eprint/399186
PURE UUID: 78eab8df-e57d-4109-b130-fd7fe17ae77d
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 09 Aug 2016 14:17
Last modified: 03 Dec 2019 01:38

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Contributors

Author: Xin Lu
Author: David J. Wrathall
Author: Pål Roe Sundsøy
Author: Md. Nadiruzzaman
Author: Erik Wetter
Author: Asif Iqbal
Author: Taimur Qureshi
Author: Andrew J. Tatem ORCID iD
Author: Geoffrey S. Canright
Author: Kenth Engø-Monsen
Author: Linus Bengtsson

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