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Modelling human mobility patterns using photographic data shared online.

Daniele Barchiesi1, Tobias Preis2, Steven Bishop1

  • 1Department of Mathematics , University College London, Gower Street , London WC1E 6BT, UK.

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|September 12, 2015
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Summary
This summary is machine-generated.

Human mobility patterns can be modeled using online data. A machine learning algorithm analyzing geo-tagged Flickr images accurately predicts human movement and location probabilities within the UK.

Keywords:
Flickrcomplex systemscomputational social sciencedata sciencehuman mobilitysocial media

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Area of Science:

  • Computational Social Science
  • Geographic Information Science
  • Machine Learning

Background:

  • Human mobility is fundamental to urban planning and transportation system design.
  • Understanding large-scale human movement patterns is crucial for societal development.
  • Previous research has explored human mobility using Lévy flight theory.

Purpose of the Study:

  • To develop a machine learning algorithm for inferring human mobility patterns.
  • To quantify the probability of individuals being in specific geographical locations.
  • To model the probability of movement between different locations.

Main Methods:

  • Collected geo-tagged image data from 16,000 individuals on Flickr within the UK.
  • Applied a machine learning algorithm inspired by Lévy flight theory.
  • Inferred spatial and movement probabilities from the collected data.

Main Results:

  • The algorithm successfully inferred human mobility patterns from online data.
  • Model predictions showed general agreement with official UK travel statistics.
  • Online data sources demonstrate potential for quantifying large-scale human mobility.

Conclusions:

  • Geo-tagged online data, like Flickr images, can effectively model human mobility.
  • Machine learning approaches can accurately predict movement and location probabilities.
  • This methodology offers a novel way to study large-scale human movement patterns.