The impact of biases in mobile phone ownership on estimates of human mobility
Journal of the Royal Society Interface
Department of Engineering and Public Policy, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15221, United States; College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, United States; Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, United States; Malaria Public Health Cluster, KEMRI-Wellcome Trust-University of Oxford Collaborative Programme, PO Box 43630-00100, Nairobi, Kenya; Nuffield Department of Clinical Medicine, University of Oxford, Churchill Hospital, Old Road, Oxford OX3 7LJ, United Kingdom; Center for Communicable Disease Dynamics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, United States
Mobile phone data are increasingly being used to quantify the movements of human populations for a wide range of social, scientific and public health research. However, making population-level inferences using these data is complicated by differential ownership of phones among different demographic groups that may exhibit variable mobility. Here, we quantify the effects of ownership bias on mobility estimates by coupling two data sources from the same country during the same time frame. We analyse mobility patterns from one of the largest mobile phone datasets studied, representing the daily movements of nearly 15 million individuals in Kenya over the course of a year. We couple this analysis with the results from a survey of socioeconomic status, mobile phone ownership and usage patterns across the country, providing regional estimates of population distributions of income, reported airtime expenditure and actual airtime expenditure across the country. We match the two data sources and show that mobility estimates are surprisingly robust to the substantial biases in phone ownership across different geographical and socioeconomic groups. © 2013 The Authors.
Demographic groups; Health research; Human mobility; Human population; Mobile phone datasets; Mobile phone datum; Mobility pattern; Socio-economic status; Cellular telephones; Economics; Estimation; Mobile phones; Population dynamics; Population statistics; Telephone sets; E-learning; article; human; income; Kenya; migration; mobile phone; ownership bias; social problem; social status; biological model; epidemiology; motor activity; physiology; socioeconomics; statistics; utilization review; Cellular Phone; Humans; Kenya; Models, Biological; Motor Activity; Selection Bias; Socioeconomic Factors