Are call detail records biased for sampling human mobility?

Gyan Ranjan · Hui Zang · Zhi-Li Zhang · Jean C. Bolot

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Human Mobility and Location-Based Analysis Data Management and Algorithms Opportunistic and Delay-Tolerant Networks

Abstract

Call detail records (CDRs) have recently been used in studying different aspects of human mobility. While CDRs provide a means of sampling user locations at large population scales, they may not sample all locations proportionate to the visitation frequency of a user, owing to sparsity in time and space of voice-calls, thereby introducing a bias. Also, as the rate of sampling is inherently dependent on the calling frequencies of an individual, high voice-call activity users are often chosen for conducting a meaningful study. Such a selection process can, inadvertently, lead to a biased view as high frequency callers may not always be representative of an entire population. With the advent of 3G technology and wide adoption of smart-phones, cellular devices have become versatile end-hosts. As the data accessed on these devices does not always require human initiation, it affords us with an unprecedented opportunity to validate the utility of CDRs for studying human mobility. In this work, we investigate various metrics for human mobility studied in literature for over a million cellular users in the San Francisco bay-area, for over a month. Our findings reveal that although the voice-call process does well to sample significant locations, such as home and work, it may in some cases incur biases in capturing the overall spatio-temporal characteristics of individual human mobility. Additionally, we motivate an "artificially" imposed sampling process, vis-a-vis the voice-call process with the same average intensity. We observe that in many cases such an imposed sampling process yields better performance results based on the usual metrics like entropies and marginal distributions used often in literature.

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References

2008Understanding individual human mobility patterns 2010Limits of Predictability in Human Mobility 2005The origin of bursts and heavy tails in human dynamics 2006The scaling laws of human travel 2012A Tale of Many Cities: Universal Patterns in Human Urban Mobility 2011Identifying Important Places in People’s Lives from Cellular Network Data 2009Measuring serendipity

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