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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

Aggregate model of mobility. (a) Probability of an individual's location derived from data uploaded by all the users in the Flickr dataset. The plot depicts the natural logarithm of p(x) as defined in equation (4.1). This describes the likelihood of finding a Flickr user in each geographical location and, since the dataset contains photos uploaded in the UK, it resembles the shape of the UK. The points in the map are local maxima identified with a maximum filter and thresholding, and correspond to the location of main UK cities. The names indicated in black indicate cities that do not appear in the list of the 20 most populous UK cities. (b) Aggregate transition probability between pairs of main UK cities. The line widths are proportional to p(xd,xo)+p(xo,xd) as defined in equation (4.2) and represent the probability of observing a transition between any two pairs of cities, aggregated over all the users in the dataset.
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RSOS150046F2: Aggregate model of mobility. (a) Probability of an individual's location derived from data uploaded by all the users in the Flickr dataset. The plot depicts the natural logarithm of p(x) as defined in equation (4.1). This describes the likelihood of finding a Flickr user in each geographical location and, since the dataset contains photos uploaded in the UK, it resembles the shape of the UK. The points in the map are local maxima identified with a maximum filter and thresholding, and correspond to the location of main UK cities. The names indicated in black indicate cities that do not appear in the list of the 20 most populous UK cities. (b) Aggregate transition probability between pairs of main UK cities. The line widths are proportional to p(xd,xo)+p(xo,xd) as defined in equation (4.2) and represent the probability of observing a transition between any two pairs of cities, aggregated over all the users in the dataset.

Mentions: Having analysed the trajectory of a single user, we now focus on deriving aggregate results for all the users in the dataset. This will allow us to infer general patterns that describe the probability of finding any Flickr user in a given geographical area, and the probability of transition between pairs of areas. Figure 2a displays the function derived in equation (4.1) that describes the log-likelihood of finding a Flickr user in a given geographical location. The silhouette of Great Britain and Northern Ireland are clearly visible, along with areas of high probability corresponding to main UK cities. To obtain a set of points corresponding to maximum values of the function p(x), we employ a maximum filter. This is a commonly used tool in image processing that operates on a two-dimensional function by applying a sliding rectangular window of dimensions (dx,dy), selecting the local maximum within each window, and setting to zero all the other values. Since some areas do not contain notable local maxima (for example, regions located in open sea), we also thresholded the local maxima retaining only the ones with probability greater than a level ϕ.Figure 2.


Modelling human mobility patterns using photographic data shared online.

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

Aggregate model of mobility. (a) Probability of an individual's location derived from data uploaded by all the users in the Flickr dataset. The plot depicts the natural logarithm of p(x) as defined in equation (4.1). This describes the likelihood of finding a Flickr user in each geographical location and, since the dataset contains photos uploaded in the UK, it resembles the shape of the UK. The points in the map are local maxima identified with a maximum filter and thresholding, and correspond to the location of main UK cities. The names indicated in black indicate cities that do not appear in the list of the 20 most populous UK cities. (b) Aggregate transition probability between pairs of main UK cities. The line widths are proportional to p(xd,xo)+p(xo,xd) as defined in equation (4.2) and represent the probability of observing a transition between any two pairs of cities, aggregated over all the users in the dataset.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSOS150046F2: Aggregate model of mobility. (a) Probability of an individual's location derived from data uploaded by all the users in the Flickr dataset. The plot depicts the natural logarithm of p(x) as defined in equation (4.1). This describes the likelihood of finding a Flickr user in each geographical location and, since the dataset contains photos uploaded in the UK, it resembles the shape of the UK. The points in the map are local maxima identified with a maximum filter and thresholding, and correspond to the location of main UK cities. The names indicated in black indicate cities that do not appear in the list of the 20 most populous UK cities. (b) Aggregate transition probability between pairs of main UK cities. The line widths are proportional to p(xd,xo)+p(xo,xd) as defined in equation (4.2) and represent the probability of observing a transition between any two pairs of cities, aggregated over all the users in the dataset.
Mentions: Having analysed the trajectory of a single user, we now focus on deriving aggregate results for all the users in the dataset. This will allow us to infer general patterns that describe the probability of finding any Flickr user in a given geographical area, and the probability of transition between pairs of areas. Figure 2a displays the function derived in equation (4.1) that describes the log-likelihood of finding a Flickr user in a given geographical location. The silhouette of Great Britain and Northern Ireland are clearly visible, along with areas of high probability corresponding to main UK cities. To obtain a set of points corresponding to maximum values of the function p(x), we employ a maximum filter. This is a commonly used tool in image processing that operates on a two-dimensional function by applying a sliding rectangular window of dimensions (dx,dy), selecting the local maximum within each window, and setting to zero all the other values. Since some areas do not contain notable local maxima (for example, regions located in open sea), we also thresholded the local maxima retaining only the ones with probability greater than a level ϕ.Figure 2.

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