Mobility Tracking

Mobility Tracking refers to the collection of participants’ mobility data with the help of mobile apps or devises, e.g. through their mobile phones or other daily-use wearable devices. There are two types of Mobility Tracking: Active Tracking and Passive Tracking. Active Tracking requires participants to share their data actively or to authorize some software on their mobile devices to collect and share data. Passive Tracking does not need participants’ consent, and planners collect participants’ data from mobile network operators. By cleaning and analyzing the data collected through Mobility Tracking, people’s travel behaviors, place preference, etc., can be known. The legality of collecting data without the user's consent should be discussed.

Basic Information on the Method
Mode of communication
Online
Group size
31 and more
Geographical scale
Public space, Neighbourhood, City, Region
Skills required
Basic
Resources needed
Medium, High
Level of Involvement
Level of involvement
Consult
Type of knowledge enabled
Divergence (Broad public)
Additional Criteria
Planning phase
Initiatiion, Evaluation & Research, Maintenance
Methodological approach
Diagnostic, Expressive

How to use the method

Depending on the form of Mobility Tracking, the data accessibility and data collection processes vary.

Active Tracking

  • Data Collection
    • Participants can be recruited both on site (within the study area) and online (contact those who have shared data on a tracking application through email).
    • Participants are asked to give consent to share the data collected by the application they are using.
    • If necessary, socio-cultural background information could be asked from the participants.
  • Data Analysis
    • Personal identifying information should be processed to ensure the anonymity.
    • Based on the planning/study purpose, collected data should be filtered according to various factors, e.g. boundaries of study area, separation of daily mobility and occational mobility.[1]
    • GPS positioning accuracy should be estimated.

Passive Tracking

  • Data Collection
    • Data of Passive Tracking is usually collected by mobile network operators.
    • In some countries, operators collect their customers’ data and share/sell it to researchers/planners without notifying the customers, therefore the participants participate passively.
    • In other countries, the accessibilty of the mobile network data may be restricted by the data protection agency.[2] In this case, ad hoc cooperations between planners and mobile network operators may be a solution.
  • Data Analysis
    • Data analysis process is similar to the analysis process of Active Tracking, which should be tailored to the planning purpose/theme.
    • Usually the dataset provided by operators is larger and more complex that demand more advanced skills of big data analysis.

What are the outcomes

  • Data about personal daily experiences and various aspects of the study area.[3]
  • Participants’ preference of locations.
  • Participants’ behavioural trajectory.

Skills required

Skills: basic

  • Basic skills of sharing information through their devices

Resources needed

Resources: medium, high

  • Access to the data from operator or data-collecting software
  • Skills of analyzing massive data

Strengths and weaknesses

Strengths
  • Full-time and detailed information about the revealed bahavior patterns of users of urban space
  • Can be conducted through participants’ mobile phones without asking them to do any extra operation
Weaknesses
  • Privacy issues, dealing with participants’ consent of sharing personal/behavioural information
  • The volume of the collected data can be very large[4]
  • Challenge of the trustworthiness of the collected data due to the accuracy of GPS[5]
  • The data typically includes little additional information about the participants
  • Participants may act not as usual if they aware that they are being observed

 

Use cases

Mobility in Finland during Covid-19 Crisis[6]

Researchers at University of Helsinki used big data provided by Finnish telecommunications companies Telia and Elisa to study urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. This research also shows that mobile phone data is potentially very useful in the field of crisis reponse, housing, population dynamics and mobility.

Beijing[7]

In 2016, an academic research use Baidu (a Chinese Map service provider) POI (point of interest) data and geotagged Weibo (a Chinese Twitter-like microblog platform) data to study Beijing’s fine-grained urban land use mapping. It shown that social sensing data can help to represent temporally and spatially fine-grained population distribution.

Try one of these tools & resources

References
  1. [1] Korpilo, S., Virtanen, T., & Lehvävirta, S. (2017). Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement. Landscape and Urban Planning, 157, 608-617.
  2. [2] Poom, A., Järv, O., Zook, M., & Toivonen, T. (2020). COVID-19 is spatial: Ensuring that mobile Big Data is used for social good. Big Data & Society, 7(2), 2053951720952088.
  3. [3] Silva, T. H., Celes, C. S. F. S., Neto, J., Mota, V., Cunha, F., Ferreira, A., ... & Loureiro, A. (2016). Users in the urban sensing process: Challenges and research opportunities. Pervasive Computing: Next Generation Platforms for Intelligent Data Collection, 45-95.
  4. [4] Aggarwal, C. C. (Ed.). (2013). Managing and mining sensor data. Springer Science & Business Media.
  5. [5] Aggarwal, C. C. (Ed.). (2013). Managing and mining sensor data. Springer Science & Business Media.
  6. [6] Willberg, E., Järv, O., Väisänen, T., & Toivonen, T. (2021). Escaping from cities during the covid-19 crisis: Using mobile phone data to trace mobility in finland. ISPRS International Journal of Geo-Information, 10(2), 103.
  7. [7] Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., & Wang, H. (2017). The combined use of remote sensing and social sensing data in fine-grained urban land use mapping: A case study in Beijing, China. Remote Sensing, 9(9), 865.