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Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis

Author

Listed:
  • Carlo Ratti
  • Dennis Frenchman
  • Riccardo Maria Pulselli

    (Department of Chemical and Biosystem Sciences, University of Siena, Italy)

  • Sarah Williams

    (Spatial Information Design Laboratory, 1172 Amsterdam Avenue, 400 Avery Hall, Columbia University, New York, NY 10027, USA)

Abstract

The technology for determining the geographic location of cell phones and other handheld devices is becoming increasingly available. It is opening the way to a wide range of applications, collectively referred to as location-based services (LBS), that are primarily aimed at individual users. However, if deployed to retrieve aggregated data in cities, LBS could become a powerful tool for urban analysis. In this paper we aim to review and introduce the potential of this technology to the urban planning community. In addition, we present the ‘Mobile Landscapes' project: an application in the metropolitan area of Milan, Italy, based on the geographical mapping of cell phone usage at different times of the day. The results enable a graphic representation of the intensity of urban activities and their evolution through space and time. Finally, a number of future applications are discussed and their potential for urban studies and planning is assessed.

Suggested Citation

  • Carlo Ratti & Dennis Frenchman & Riccardo Maria Pulselli & Sarah Williams, 2006. "Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis," Environment and Planning B, , vol. 33(5), pages 727-748, October.
    • Handle: RePEc:sae:envirb:v:33:y:2006:i:5:p:727-748
    DOI: 10.1068/b32047
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    Cited by:

    1. Kang Wu & Jingxian Tang & Ying Long, 2019. "Delineating the Regional Economic Geography of China by the Approach of Community Detection," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    2. 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.
    3. Wenlai Wang & Tao Pei & Jie Chen & Ci Song & Xi Wang & Hua Shu & Ting Ma & Yunyan Du, 2019. "Population Distributions of Age Groups and Their Influencing Factors Based on Mobile Phone Location Data: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
    4. Williams, Sarah & White, Adam & Waiganjo, Peter & Orwa, Daniel & Klopp, Jacqueline, 2015. "The digital matatu project: Using cell phones to create an open source data for Nairobi's semi-formal bus system," Journal of Transport Geography, Elsevier, vol. 49(C), pages 39-51.
    5. Fabio Manfredini & Paola Pucci & Paolo Tagliolato, 2014. "Toward a Systemic Use of Manifold Cell Phone Network Data for Urban Analysis and Planning," Journal of Urban Technology, Taylor & Francis Journals, vol. 21(2), pages 39-59, April.
    6. 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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