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Humans are always prone to movements, but today, there are new perspectives and methods to analyse aspects of spatial and social mobility with different ways of communications. The course aims to understand different methods and approaches for analysing spatial and social patterns of movements. We have used the mobility data during the course, which belongs to the Mobility Lab of the University of Tartu. The course covered several topics: geography of the information society, mobile data collection methods, information society, usage of GeoSocial media to study cities, smart cities, problems and challenges of mobile data, including privacy issues.
The illustration represents the spatial and social mobility of me from 2015 to 2020 in Tbilisi, Georgia.
Personal experience with Mobility
Spatial mobility is movement between different places characterized by different geographical scales. Frequently, spatial mobility is prompted by social aspects. The ladder of social life is called social mobility. "Social mobility, movement of individuals, families, or groups through a system of social hierarchy or stratification" (Encyclopaedia Britannica 2020, Social mobility).
Calling activities by users in 2008-2014, Estonia. The graph shows the changes in the number of calls in time, for two different users.
Analysing mobility data is one of the effective and powerful mechanisms to identify spatial patterns and analyse the behaviour of movements/activities. Passive mobile positioning data is one of the types of mobility data. The following graphs represent the results of analysing Call Detail Records (CDR) data in Estonia.
For analysis, there were used two separate files with different information - calling activities and spatial information of mobile antennas. The period of a dataset of calling activities is 2008-2014 and contains information about two users, identified as "user A" and "user B" and their calling activities by date and time, also the identification of antenna. The analysis process is comprehensive, including several steps: data processing, data analysing and data visualisation using various programmes: R/R Studio Desktop, GIS (ArcMap, QGIS).
Calling activities by weekday and hour during 2008-2014, Estonia. The graph shows the changes in the total number of calls during the week and between time intervals, for two different users. The numbers are aggregated for the entire period.
Calling activities by weekday and hour in 2008-2014, Estonia. The graph shows the changes in the total number of calls during the week and between time intervals, for two different users. The numbers are represented for each year.
Average calling activities made per day in 2008-2014, Estonia. The graph shows the average number of calls made by users.
Average calling activities by months in 2008-2014, Estonia. The graph shows the average number of calls made by users.
GPS stop points per sq. km. The map shows the high and low concentration of stop points.
GPS data analysis is one of the ways to assess mobility patterns. Though it is a challenging and complicated process as the time and accuracy of data should be considered while analysing. The following maps represent the results of GPS data analysis in Estonia, Tartu. The data belongs to the Mobility Lab of the University of Tartu. Provided data consists of two datasets - raw data of GPS tracks and stop points. For analysis, mainly the GPS stop dataset was used, which contains information about the start and end times of the stop, duration of time at a stop, radius connected to the accuracy of the point location, and geographical data - coordinates.
When working on the data, the main considerable variables were time, duration of stop and radius. For analysis, several tools were implemented.
GPS stop points by duration. The map shows the duration of time at each point.
Hot Spot Analysis of GPS stop data. The map shows active areas by the duration of time.
Location types of GPS stop points.
Home and Work/Study locations. The map shows buffer zones of home and work/study locations based on radius values of stop points.
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