Crossover from exponential to power-law scaling for human mobility pattern in urban, suburban and rural areas

Liu, Hsing; Chen, Ying-Hsing; Lih, Jiann-Shing

doi:10.1140/epjb/e2015-60232-1

Cited by 10 publications

(11 citation statements)

References 21 publications

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“…The displacements and waiting times of human dynamics follow non-Poisson statistics, however, several e®ects like the population's heterogeneity and the motions spatial and temporal regularity make the interpretation and characterization of the observed heavy-tailed distributions more di±cult, moreover, the di®erent methods used for monitoring locations could a®ect the result of the analysis [2,4]. Numerous measurements and studies led to di®erent conclusions for the scaling laws and models of mobility [1][2][3][4][5][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Spectral Analysis of Fluctuations in Humans’ Daily Motion Using Location Data

2019

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The interpretation and characterization of universal scaling laws in human mobility and activity are subjects of active research. For the better understanding, instead of the statistical approach we have examined the temporal patterns of human daily motion using location data. The trajectories were measured continuously and with even sampling (1 measurement per minute), using GPS and Wi-Fi/mobile Internet signals of the participant's smartphone, we have analyzed the few-week-long signals of displacements between two subsequent samplings in frequency domain. Our results had shown that 1/f type noise is observable over the frequency of the daily rhythm of motion and its harmonics. We point out several technical questions about the measurement and data processing required for further detailed analysis. Furthermore, our new observation could help in the investigation of the underlying dynamics of human motion and opens several theoretical questions about the relationship between the spatial and temporal universality.

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Section: Introductionmentioning

confidence: 99%

Spectral Analysis of Fluctuations in Humans’ Daily Motion Using Location Data

2019

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“…With the development of Information and Communication Technologies 7 , the investigations' focus shifted from the traditional travel diary surveys 8 9 10 to several new data sources. In particular, it became possible to follow individual trajectories from mobile phone calls 11 12 13 , location-sharing services 14 15 16 and microblogging 17 , or directly extracted from public transport ticketing system 10 18 , global positioning system (GPS) tracks of taxis 10 19 20 21 22 23 , private cars 24 25 26 or single individuals 27 28 . For most data sources, the spatial position r is the most reliable quantity.…”

mentioning

confidence: 99%

A stochastic model of randomly accelerated walkers for human mobility

et al. 2016

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Recent studies of human mobility largely focus on displacements patterns and power law fits of empirical long-tailed distributions of distances are usually associated to scale-free superdiffusive random walks called Lévy flights. However, drawing conclusions about a complex system from a fit, without any further knowledge of the underlying dynamics, might lead to erroneous interpretations. Here we show, on the basis of a data set describing the trajectories of 780,000 private vehicles in Italy, that the Lévy flight model cannot explain the behaviour of travel times and speeds. We therefore introduce a class of accelerated random walks, validated by empirical observations, where the velocity changes due to acceleration kicks at random times. Combining this mechanism with an exponentially decaying distribution of travel times leads to a short-tailed distribution of distances which could indeed be mistaken with a truncated power law. These results illustrate the limits of purely descriptive models and provide a mechanistic view of mobility.

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“…Sensitivity analysis for measuring the effect of distance factor in Eq (2) on the model performance was further assessed. Moreover, some studies suggested to use exponential functions for modeling distance-decay relationship [ 27 ] [ 28 ] [ 29 ] [ 30 ]. We also compared the power-law distance-decay function with the exponential-decay functions on model performance.…”

Section: Methodsmentioning

confidence: 99%

“…Distance-decay relationship is often formulated as power-law or exponential functions in geographical literatures [ 3 ]. With an exponential-decaying function, the strength of interaction between nodes would decrease more dramatically with increasing distance [ 27 ] [ 28 ] [ 29 ] [ 30 ]. There are two major components that influence distance-decay relationship: the shape of distance-decay curves (power-law vs. exponential functions), and the parameters of the distance-decay functions.…”

Section: Methodsmentioning

confidence: 99%

Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network

Chin

Wen

2015

PLoS ONE

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A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms—Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)—that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.

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Crossover from exponential to power-law scaling for human mobility pattern in urban, suburban and rural areas

Cited by 10 publications

References 21 publications

Spectral Analysis of Fluctuations in Humans’ Daily Motion Using Location Data

Spectral Analysis of Fluctuations in Humans’ Daily Motion Using Location Data

A stochastic model of randomly accelerated walkers for human mobility

Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network

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