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Quantifying crowd size with mobile phone and Twitter data.

Federico Botta1, Helen Susannah Moat2, Tobias Preis2

  • 1Centre for Complexity Science , Warwick Business School, University of Warwick , Coventry CV4 7AL, UK ; Data Science Lab, Behavioural Science , Warwick Business School, University of Warwick , Coventry CV4 7AL, UK.

Royal Society Open Science
|June 12, 2015
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Summary
This summary is machine-generated.

Estimating crowd size is vital for safety. This study shows mobile phone and Twitter activity strongly correlates with real-world crowd numbers in stadiums and airports, offering insights into societal conditions.

Keywords:
complex systemscomputational social sciencedata science

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Area of Science:

  • Societal data analysis
  • Computational social science
  • Public safety technology

Background:

  • Accurate crowd size estimation is critical for preventing disasters and managing evacuations.
  • Traditional methods for crowd counting can be labor-intensive and may not provide real-time data.
  • Understanding population density in public spaces is essential for urban planning and emergency response.

Purpose of the Study:

  • To investigate the relationship between mobile phone activity and Twitter data with actual crowd sizes.
  • To explore the potential of using digital footprints for real-time crowd monitoring.
  • To demonstrate the feasibility of this approach in high-traffic venues like stadiums and airports.

Main Methods:

  • Collected anonymized mobile phone network data and Twitter data from specific geographic areas.
  • Utilized case studies at a football stadium and an airport.
  • Correlated digital activity metrics with known crowd density figures.

Main Results:

  • A strong positive correlation was observed between mobile phone activity/Twitter data and the number of people present.
  • The findings indicate that digital data can serve as a reliable proxy for crowd size.
  • This method proved effective in diverse, high-occupancy environments.

Conclusions:

  • Mobile phone and internet data offer a scalable and potentially real-time method for estimating crowd size.
  • These digital traces provide valuable insights into the current state of societal activity.
  • The approach has significant implications for public safety, emergency management, and understanding population dynamics.