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

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 Climatic Change (2016) WorldPop, Department of Geography and Environment, University of Southampton, Southampton, UK 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.

Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges

“…Hence, as CDRs are potentially accessible in near real-time, having the information regarding how many people can potentially be exposed in any disaster risk-prone area can help to improve a city preparedness and response capacity. For instance, using calling behaviour from 5.1 million Grameenphone users in two cities from Bangladesh, Lu, et al [42] investigated mobility patterns and anomalies in calling frequency during the cyclone Measham in May 2013. By analysing urban population movements before, during and after a hazard event, this type of study can be a significant asset for (i) pre-event risk reduction, (ii) in-time coping, and (iii) post-event responses.…”

Uncovering the Relationship between Human Connectivity Dynamics and Land Use

“…One of the richest data sources about human daily based activities is mobile phone data [9]. Many diverse applications with significant social impact are developed based on mobile phone data, such as urban sensing and planning [10,11], traffic engineering [12][13][14], predicting energy consumption [15], disaster management [16][17][18], epidemiology [19][20][21], deriving socio-economical indicators [22,23].…”

Big Data Processing, Analysis and Applications in Mobile Cellular Networks

“…As an example, every time a user makes via mobile phone interaction (SMS, call, internet), a call detail record (CDR) is created and stored by a mobile network operator. CDRs not only log the user activity for billing purposes and network management, but also provide opportunities for different applications such as urban sensing [5], transport planning [3,28], disaster management [38,46,64] socio-economic analysis [45,57] and monitoring epidemics of infectious diseases [10,11,36,62].…”