Mobile positioning data

It has become an everyday habit for many people to carry a mobile phone with them.
It has become an everyday habit for many people to carry a mobile phone with them.

Mobile positioning data (MPD) is a form of big data which results from the high data volumes of mobile positioning – tracking the location of mobile phones.[1]

Mobile positioning data can be used for generating population and tourism statistics, for measuring human mobility, creating data-driven solutions in urban planning, establishing a response plan to disasters etc.[2]

There are many ways to track the location of a mobile device in a network but this article covers mobile positioning data from network-based technologies:

  • Active mobile positioning is based on mobile network operators where the location of the mobile phone is determined with a special query.[3] Mobile phones are positioned based on network signals from the network antennae, and usually using the signal triangulation method.[4] Collecting this data generally requires special permissions (consent from people being positioned),[2] meaning that the number of people who are being positioned is usually small.[5]
  • Passive mobile positioning uses metadata from mobile phone use, such as incoming or outgoing calls or text messages (call detail records) or mobile internet usage (data detail records),[2] that are automatically stored by every mobile network operator.[6] The accuracy of passive mobile positioning is limited to the coverage area of network cells, which can range from a few hundred metres to multiple kilometres.[4]

Compared to passive mobile positioning, active mobile positioning yields more accurate location data and provides a greater frequency in the data points created. Although less accurate, passive mobile positioning data has many benefits: it can be collected more easily compared to active mobile positioning data (requires no individual agreements), the number of people positioned can be much bigger and it can be gathered for longer periods of time.[6]

  1. ^ Ahas, Rein; Aasa, Anto; Silm, Siiri; Tiru, Margus (2007), "Mobile Positioning Data in Tourism Studies and Monitoring: Case Study in Tartu, Estonia", Information and Communication Technologies in Tourism 2007, Vienna: Springer Vienna, pp. 119–128, doi:10.1007/978-3-211-69566-1_12, ISBN 978-3-211-69564-7, retrieved 2021-08-24
  2. ^ a b c Ahas, Rein; Aasa, Anto; Roose, Antti; Mark, Ülar; Silm, Siiri (June 2008). "Evaluating passive mobile positioning data for tourism surveys: An Estonian case study". Tourism Management. 29 (3): 469–486. doi:10.1016/j.tourman.2007.05.014. ISSN 0261-5177.
  3. ^ Ahas, Rein; Aasa, Anto; Silm, Siiri; Tiru, Margus (February 2010). "Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data". Transportation Research Part C: Emerging Technologies. 18 (1): 45–54. doi:10.1016/j.trc.2009.04.011.
  4. ^ a b Ahas, Rein; Mark, Ülar (August 2005). "Location based services—new challenges for planning and public administration?". Futures. 37 (6): 547–561. doi:10.1016/j.futures.2004.10.012. ISSN 0016-3287.
  5. ^ Raun, Janika; Shoval, Noam; Tiru, Margus (2020-03-09). "Gateways for intra-national tourism flows: measured using two types of tracking technologies". International Journal of Tourism Cities. 6 (2): 261–278. doi:10.1108/ijtc-08-2019-0123. ISSN 2056-5607. S2CID 216388897.
  6. ^ a b Silm, Siiri; Järv, Olle; Masso, Anu (2020), "Tracing human mobilities through mobile phones", Handbook of Research Methods and Applications for Mobilities, Edward Elgar Publishing, pp. 182–192, doi:10.4337/9781788115469.00025, ISBN 978-1-78811-546-9, S2CID 225356172, retrieved 2021-08-31