Scaling Behavior of Human Mobility Distributions

Paul, Tuhin; Stanley, Kevin G.; Osgood, Nathaniel D.; Bell, Scott; Muhajarine, Nazeem

doi:10.1007/978-3-319-45738-3_10

Cited by 10 publications

(9 citation statements)

References 26 publications

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“…Because of its reliance on cellular call records, as in subsequent works [ 2 – 4 ], their dataset was subject to bias both by its focus on a particular demographic, and adherence to a particular spatial and temporal resolution. More recent empirical research has established that the estimated predictability of human mobility is contingent on the scale [ 5 ] and structure [ 6 , 7 ] of the data, and underlying mobility model assumptions [ 8 ]. While Song et al made a foundational contribution to quantifying mobility predictability in complex systems, their results are only applicable to the population and spatio-temporal resolution [ 8 ] represented in the data.…”

Section: Introductionmentioning

confidence: 99%

Multiscale entropy rate analysis of complex mobile agents

2018

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Accurate prediction of the motion of objects is a central scientific goal. For deterministic or stochastic processes, models exist which characterize motion with a high degree of reliability. For complex systems, or those where objects have a degree of agency, characterizing motion is far more challenging. The information entropy rate of motion through a discrete space can place a limit on the predictability of even the most complex or history-dependent actor, but the variability in measured encountered locations is inexorably tied to the spatial and temporal resolutions of those measurements. This relation depends on the path of the actor in ways that can be used to derive a general law in closed form relating the mobility entropy rate to different spatial and temporal resolutions, and the path properties within each cell along the path. Correcting for spatial and temporal effects through regression yields the path properties and a measure of mobility entropy rate robust to changes in dimension, allowing comparison of mobility entropy rates between datasets. Employing this measure on empirical datasets yields novel findings, from the similarity of taxicabs to drifters, to the predictable motions of undergraduates, to the browsing habits of Canadian moose.

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Section: Introductionmentioning

confidence: 99%

Multiscale entropy rate analysis of complex mobile agents

2018

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“…Even for those preserved users, 2-hour time span of consecutive location samples is too coarse-grained and may omit individual movement lasting less than two hours, leading to underestimated range of movement, inaccurate waiting-time estimation, and low rate of convergence of statistics and model parameters. Recently, the extensive use of GPS enables researchers to study human mobility at a finer granularity than before and some works try to reveal how temporal resolution impacts the observations of human mobility by GPS[ 23 , 24 ]. However, due to privacy issues, GPS data sets usually contain a limited number of people and conclusions based on GPS may be biased.…”

Section: Introductionmentioning

confidence: 99%

Temporal understanding of human mobility: A multi-time scale analysis

Liu¹,

Yang²,

Zhao³

et al. 2018

PLoS ONE

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The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at a large-scale (3,542,235 individuals) and finer-granularity (7.5min). Next, we investigated statistical variations and biases of mobility models caused by different time scales (from 7.5min to 32h), and found that the time scale does influence the mobility model, which indicates a deep coupling of human mobility and time. We further show that mobility behaviors like transportation modes contribute to the diversity of human mobility, by exploring several novel and refined features (e.g., motion speed, duration, and trajectory distance). Particularly, we point out that 2-hour sampling adopted in previous works is insufficient to study detailed motion behaviors. Our work not only offers a macroscopic and microscopic view of spatial-temporal human mobility, but also applies previously unavailable features, both of which are beneficial to the studies on phenomena driven by human mobility.

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“…2014, Yoo et al . 2015), and replacing traditional paper-based travel diaries in travel surveys (Shen and Stopher 2014, Paul et al . 2016).…”

Section: Introductionmentioning

confidence: 99%

“…We propose in this paper a spatially and temporally explicit metric that quantifies the extent to which data have captured the regularity in routine time-activities. Activity space by definition refers to the local areas within which people travel during their daily activities (Hägerstrand 1975, Mazey 1981, Paul et al . 2016), and is considered a defining concept of human spatial behaviours in geography (Golledge 1997, Paul et al .…”

Section: Introductionmentioning

confidence: 99%

How many days are enough?: capturing routine human mobility

Stanley

Yoo

Paul

et al. 2018

International Journal of Geographical Information Science

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Wedding mobile phone sensor technology and human spatial behaviour has great potential. The ubiquity of Global Positioning Systems (GPS) technology has made gathering data about human mobility simpler, more precise, and with higher fidelity, providing minute-by-minute records of the locations of cohorts from dozens of participants. While this data provides a strong basis for Geographic Information Science research, it also constitutes an invasion of the participants’ privacy and can provide more information than researchers require to answer their questions. As an ethical and practical consideration, researchers should gather only as much data as they need. In this paper, we take three weeks of GPS traces from over a hundred student participants in mobile phone-based tracking studies and show that fewer than 14 days of data is necessary to establish complete activity spaces. We define ‘complete’ as the point at which marginal information gains become negligible according to a pairwise temporal analysis of the Kullback–Leibler (KL) divergence of the spatial (bivariate) histogram through time. For the fixed level of information difference, observable in the data, impacts due to individual variability, population composition, and spatial resolution are evident. However, all populations at each level of resolution examined in the paper demonstrated convergence to low divergence levels occurred within a matter of days, and to negligible information gain in less than two weeks. The methods described in the paper represent a novel metric useful to understand the interaction between measurements and information in human mobility.

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Scaling Behavior of Human Mobility Distributions

Cited by 10 publications

References 26 publications

Multiscale entropy rate analysis of complex mobile agents

Multiscale entropy rate analysis of complex mobile agents

Temporal understanding of human mobility: A multi-time scale analysis

How many days are enough?: capturing routine human mobility

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