Limits...
Modelling human mobility patterns using photographic data shared online.

Barchiesi D, Preis T, Bishop S, Moat HS - R Soc Open Sci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics , University College London, Gower Street , London WC1E 6BT, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Model of an individual's mobility. (a) Individual trajectory depicting the location of geo-tagged photos uploaded by one of the users in the Flickr database. (b) Different colours indicate clusters discovered by the DBSCAN algorithm. (c) Different colours identify hidden states learned by a Hidden Markov Model, while the contour plots indicate Gaussian distributions learned for each state. The thickness of lines between different clusters is proportional to the number of times the user has moved between the two states, as estimated by the Viterbi algorithm. Arrows indicate the relative proportion of incoming and outgoing movements from one hidden state to the other.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4555850&req=5

RSOS150046F1: Model of an individual's mobility. (a) Individual trajectory depicting the location of geo-tagged photos uploaded by one of the users in the Flickr database. (b) Different colours indicate clusters discovered by the DBSCAN algorithm. (c) Different colours identify hidden states learned by a Hidden Markov Model, while the contour plots indicate Gaussian distributions learned for each state. The thickness of lines between different clusters is proportional to the number of times the user has moved between the two states, as estimated by the Viterbi algorithm. Arrows indicate the relative proportion of incoming and outgoing movements from one hidden state to the other.

Mentions: Figure 1a shows the set of locations and the trajectory obtained from geo-tagged photos uploaded by a user, and figure 1b displays the result of clustering. Six distinct clusters are identified by the DBSCAN algorithm and are located, from south-west clockwise, around Bristol, northern Wales, Glasgow, North York Moors National Park, Norfolk and Suffolk.Figure 1.


Modelling human mobility patterns using photographic data shared online.

Barchiesi D, Preis T, Bishop S, Moat HS - R Soc Open Sci (2015)

Model of an individual's mobility. (a) Individual trajectory depicting the location of geo-tagged photos uploaded by one of the users in the Flickr database. (b) Different colours indicate clusters discovered by the DBSCAN algorithm. (c) Different colours identify hidden states learned by a Hidden Markov Model, while the contour plots indicate Gaussian distributions learned for each state. The thickness of lines between different clusters is proportional to the number of times the user has moved between the two states, as estimated by the Viterbi algorithm. Arrows indicate the relative proportion of incoming and outgoing movements from one hidden state to the other.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555850&req=5

RSOS150046F1: Model of an individual's mobility. (a) Individual trajectory depicting the location of geo-tagged photos uploaded by one of the users in the Flickr database. (b) Different colours indicate clusters discovered by the DBSCAN algorithm. (c) Different colours identify hidden states learned by a Hidden Markov Model, while the contour plots indicate Gaussian distributions learned for each state. The thickness of lines between different clusters is proportional to the number of times the user has moved between the two states, as estimated by the Viterbi algorithm. Arrows indicate the relative proportion of incoming and outgoing movements from one hidden state to the other.
Mentions: Figure 1a shows the set of locations and the trajectory obtained from geo-tagged photos uploaded by a user, and figure 1b displays the result of clustering. Six distinct clusters are identified by the DBSCAN algorithm and are located, from south-west clockwise, around Bristol, northern Wales, Glasgow, North York Moors National Park, Norfolk and Suffolk.Figure 1.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics , University College London, Gower Street , London WC1E 6BT, UK.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus