24 research outputs found
Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data
Through seven publications this dissertation shows how anonymized mobile
phone data can contribute to the social good and provide insights into human
behaviour on a large scale. The size of the datasets analysed ranges from 500
million to 300 billion phone records, covering millions of people. The key
contributions are two-fold:
1. Big Data for Social Good: Through prediction algorithms the results show
how mobile phone data can be useful to predict important socio-economic
indicators, such as income, illiteracy and poverty in developing countries.
Such knowledge can be used to identify where vulnerable groups in society are,
reduce economic shocks and is a critical component for monitoring poverty rates
over time. Further, the dissertation demonstrates how mobile phone data can be
used to better understand human behaviour during large shocks in society,
exemplified by an analysis of data from the terror attack in Norway and a
natural disaster on the south-coast in Bangladesh. This work leads to an
increased understanding of how information spreads, and how millions of people
move around. The intention is to identify displaced people faster, cheaper and
more accurately than existing survey-based methods.
2. Big Data for efficient marketing: Finally, the dissertation offers an
insight into how anonymised mobile phone data can be used to map out large
social networks, covering millions of people, to understand how products spread
inside these networks. Results show that by including social patterns and
machine learning techniques in a large-scale marketing experiment in Asia, the
adoption rate is increased by 13 times compared to the approach used by
experienced marketers. A data-driven and scientific approach to marketing,
through more tailored campaigns, contributes to less irrelevant offers for the
customers, and better cost efficiency for the companies.Comment: 166 pages, PHD thesi
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