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Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia

Author

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  • Jakub Novak
  • Rein Ahas
  • Anto Aasa
  • Siiri Silm

Abstract

The paper presents initial steps into the research of commuting patterns and functional regions using mobile phone location data. The main aim is to introduce and discuss the potential of mobile phone location data as an alternative data sources to censuses for mapping commuting flows and subsequent functional regionalization. A set of analytical maps covering various aspects of regular daily movements of population and functional regionalization is provided. Estonia is serving as a pilot laboratory for analyses based on commuting flows derived from mobile phone location data. The maps give to reader a synthetic overview of contemporary settlement system in Estonia and introduce the potential of mobile phone location data for research in this field.

Suggested Citation

  • Jakub Novak & Rein Ahas & Anto Aasa & Siiri Silm, 2013. "Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia," Journal of Maps, Taylor & Francis Journals, vol. 9(1), pages 10-15, March.
    • Handle: RePEc:taf:tjomxx:v:9:y:2013:i:1:p:10-15
    DOI: 10.1080/17445647.2012.762331
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    Cited by:

    1. Steenbruggen, John & Tranos, Emmanouil & Nijkamp, Peter, 2015. "Data from mobile phone operators: A tool for smarter cities?," Telecommunications Policy, Elsevier, vol. 39(3), pages 335-346.
    2. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    3. Yixuan Wang & Shuwen Yang & Xianglong Tang & Zhiqi Ding & Yikun Li, 2024. "Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China," Sustainability, MDPI, vol. 16(20), pages 1-25, October.
    4. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu, 2018. "Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    5. Sandra Hadam, 2021. "Pendler Mobil: Die Verwendung von Mobilfunkdaten zur Unterstützung der amtlichen Pendlerstatistik [Pendler Mobil: The use of mobile network data to support official commuter statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 15(3), pages 197-235, December.
    6. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    7. Stefano Maria Iacus & Carlos Santamaria & Francesco Sermi & Spyridon Spyratos & Dario Tarchi & Michele Vespe, 2022. "Mobility functional areas and COVID-19 spread," Transportation, Springer, vol. 49(6), pages 1999-2025, December.
    8. Beibei Yu & Zhonghui Wang & Haowei Mu & Li Sun & Fengning Hu, 2019. "Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data," Sustainability, MDPI, vol. 11(23), pages 1-18, November.

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