Aasa, A. (2019). OD-matrices of daily regular movements in Estonia [Data set]. University of Tartu, Mobility Lab.


aspect content
Objective To develop methodology for everyday mobility database which contains the OD-matrices of movements between territorial communities
Data used OD-matrices are based on mobile positioning data. Mobile positioning data contains locations of call activities (Call Detail Records (CDR)) in network cells (location, time and random unique user ID).
Novelty of the approach The mobile positioning data has high accuracy in time and space, the data allows to detect short-term differences (monthly) and seasonal fluctuations, dataset include regular movements between the place of residence and the workplace
Challenges The main limitation of passive mobile positioning data is access to data, because mobile network operators are hesitant to provide their data and relatively long value chain of implementing mobile positioning data, which requires expertise from several research fields.
Policy implications The resulting database would support mobility-related policymaking.

Background & objectives

In this case study, the Partner of the Mobility Lab of University of Tartu is the Ministry of Economic Affairs and Communications of the Republic of Estonia. The objective of the Ministry is to develop high quality database of mobility and traffic data covering whole Estonia. This database would be an important data input for the Ministry in the spatial planning decision-making process to answer questions related to transport and mobility. Mobility Lab of University of Tartu is developing and implementing a methodology for everyday mobility database, which contains the OD-matrices of movements between territorial communities based on mobile positioning data. The developed database should be compatible with other mobility datasets developed by the Ministry.
Mobile positioning provides more accurate spatio-temporal information than the conventional methods, and data can be collected almost in real-time. Passive mobile positioning has two main strengths – longitudinality and extensive sample. More than 5 billion people in the world have a mobile phone and the number of its users is increasing GSMA Intelligence 2018. Mobile network operators continuously collect Call Detail Records (CDR) and datasets can cover several years. For example, the Mobility Lab of the University of Tartu has already a continuous 12-year time series of CDR data from Estonia.
The usage of mobile positioning data in mobility studies has been increasing over the years. Mobile positioning data provides spatio-temporal knowledge from multitude aspects – locations, flows, spaces – whereas both from a single individual and aggregated perspectives. Mobile positioning data reveals locations that an individual visits and important activity locations such as anchor points (places of residence and work/school) Ahas et al., 2010. This enables to examine both individual spatio-temporal mobility between activity locations Ahas et al., 2007 and individual’s daily and long-term activity spaces Järv et al., 2014. The scale up of individuals’ activity locations and movements reveals the dynamics of human presence and mobility of the whole population as well as within spatial structures. Aggregated individual mobility uncovers daily work-related commuting flows Ahas et al., 2010. Aggregated activity locations and flows unveils the urban spatio-temporal structures, land use and settlement hierarchies of a societies whereas even sheds light on how these structures change in time Ahas et al., 2015, Louail et al., 2015, Pei et al., 2014, Silm and Ahas 2010. This helps to identify and monitor hinterlands of cities, functional urban regions and growth corridors Novak et al., 2013. The mobile positioning data have been widely used in transportation and mobility studies (e.g. Calabrese et al. 2010; Isaacman et al. 2011; Yuan et al. 2012) as well as in geographical research (e.g. Blumenstock and Fratamico 2013; Silm and Ahas 2014). In Estonia, mobile positioning data has been used to map the commuting areas of municipalities by ordering the Ministry of the Interior (Ahas et. al 2010).
During the current project, Mobility Lab is developing an everyday mobility database. Study period extends from January 2016 to March 2018. Dataset will be prepared for every separate month. Based on earlier experience and preliminary analysis, the spatial granularity of the database is planned to remain on level of territorial communities. As the mobile network is very dense in Tallinn the spatial accuracy can probably be higher. Completed datasets will be in machine readable format (*.csv) and available directly from Mobility Lab web service.