TY  - JOUR
UR  - http://dx.doi.org/10.1098/rsos.150046
SN  - 2054-5703
A1  - Barchiesi, D
A1  - Preis, T
A1  - Bishop, S
A1  - Moat, HS
JF  - Royal Society Open Science
VL  - 2
IS  - 8
N1  - Copyright © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
PB  - ROYAL SOC
N2  - Humans are inherently mobile creatures. The way we move around our environment has consequences for a wide range of problems, including the design of efficient transportation systems and the planning of urban areas. Here, we gather data about the position in space and time of about 16?000 individuals who uploaded geo-tagged images from locations within the UK to the Flickr photo-sharing website. Inspired by the theory of Lévy flights, which has previously been used to describe the statistical properties of human mobility, we design a machine learning algorithm to infer the probability of finding people in geographical locations and the probability of movement between pairs of locations. Our findings are in general agreement with official figures in the UK and on travel flows between pairs of major cities, suggesting that online data sources may be used to quantify and model large-scale human mobility patterns.
ID  - discovery1503665
KW  - science & technology
KW  -  multidisciplinary sciences
KW  -  computational social science
KW  -  data science
KW  -  social media
KW  -  Flickr
KW  -  human mobility
KW  -  complex systems
KW  -  stock-market moves
KW  -  levy flights
KW  -  behavior
KW  -  world
KW  -  laws
AV  - public
Y1  - 2015/08//
EP  - 8
TI  - Modelling human mobility patterns using photographic data shared online
ER  -