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Measuring the size of a crowd using Instagram

Author

Listed:
  • Federico Botta

    (Data Science Lab, Behavioural Science, 65915Warwick Business School, UK)

  • Helen Susannah Moat
  • Tobias Preis

Abstract

Measuring the size of a crowd in a specific location can be of crucial importance for crowd management, in particular in emergency situations. Here, using two football stadiums as case studies, we present evidence that data generated through interactions with the social media platform Instagram can be used to generate estimates of the size of a crowd. We present a detailed analysis of the impact of varying the time period and spatial area considered for the collection of Instagram data. Crucially, we demonstrate how to address issues that arise from changes in the usage of a social media platform such as Instagram. Our findings show how social media datasets carrying location-based information may help provide near to real-time measurements of the size of a crowd.

Suggested Citation

  • Federico Botta & Helen Susannah Moat & Tobias Preis, 2020. "Measuring the size of a crowd using Instagram," Environment and Planning B, , vol. 47(9), pages 1690-1703, November.
  • Handle: RePEc:sae:envirb:v:47:y:2020:i:9:p:1690-1703
    DOI: 10.1177/2399808319841615
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    References listed on IDEAS

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    1. Jim Giles, 2012. "Computational social science: Making the links," Nature, Nature, vol. 488(7412), pages 448-450, August.
    2. Dirk Helbing & Illés Farkas & Tamás Vicsek, 2000. "Simulating dynamical features of escape panic," Nature, Nature, vol. 407(6803), pages 487-490, September.
    3. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
    4. Merve Alanyali & Tobias Preis & Helen Susannah Moat, 2016. "Tracking Protests Using Geotagged Flickr Photographs," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-8, March.
    5. Adrian Letchford & Tobias Preis & Helen Susannah Moat, 2016. "Quantifying the Search Behaviour of Different Demographics Using Google Correlate," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-11, February.
    6. Talayeh Aledavood & Eduardo López & Sam G B Roberts & Felix Reed-Tsochas & Esteban Moro & Robin I M Dunbar & Jari Saramäki, 2015. "Daily Rhythms in Mobile Telephone Communication," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.
    7. Federico Botta & Charo I del Genio, 2017. "Analysis of the communities of an urban mobile phone network," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-14, March.
    8. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    9. Marcos Cruz & Domingo Gómez & Luis M Cruz-Orive, 2015. "Efficient and Unbiased Estimation of Population Size," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
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    Cited by:

    1. Andrew Crooks & Linda See, 2022. "Leveraging Street Level Imagery for Urban Planning," Environment and Planning B, , vol. 49(3), pages 773-776, March.

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