Big data -seminaari 19.11.2015, Tilastokeskus

1. Janika Raun
Big Data seminar at Statistics Finland
19.11.2015
Defining usual environment with
mobile positioning data

2. Outline
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 Who we are?
 What kind of data and methodologies we use?
 Defining usual environment with mobile
positioning data.
 Questions?

3. Mobility Lab, University ofTartu
 Internationally known for its mobile positioning based
research and development.
 Head of the working group – professor Rein Ahas
 http://mobilitylab.ut.ee/eng/
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4. Mobility Lab, University ofTartu
 Long-term strategic cooperation partner is a spin-off
company Positium LBS.
 Understand where people are (population), where they are
from (origin) and where they go (destination) through
mobile location data.
 http://positium.com/
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5. Data and methodologies
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6. Mobile positioning data
 Tracking the location coordinates of mobile phones.
 Active positioning
 Passive positioning
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7. Active mobile positioning
 Tracking phones with special queries.
 Specifically composed sample
 Fixed time intervals
 Questionnaires
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8. Passive mobile positioning
 Using secondary data from memory files of mobile
operator.
 CDR: Call Detail
Record
 Data management
by Positium LBS
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9. Passive mobile positioning datasets
 Estonians in Estonia (everyday movements and
domestic tourism)
 Inbound – roaming
phones in Estonia
(foreigners in Estonia)
 Outbound – roaming
phones outside of Estonia
(Estonians abroad)
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10. Spatial accuracy of the data
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11. Example of database
E.g 24h of data
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12. Privacy and data protection:
 Working group has regular consultation with lawyers
of Estonian mobile operators and Estonian Data
Protection Inspectorate
 Privacy and data protection of all phone owners is
strictly protected.
 No personal data is available outside of control of
mobile operators server
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13. Other data collection methods
 Android-basedYouSense smartphone application
 Data about location and phone use
 Questionnaires
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14. Defining usual environment with
mobile positioning data
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15. 15
Tourism means the activity of visitors taking a
trip to a main destination outside the usual
environment
for less than a year, for any main purpose,
including business, leisure or other personal
purpose, other than to be employed by a
resident entity in the place visited.
(Eurostat 2014)

16. 16
Individual’s usual environment is defined as
the geographical area
(though not necessarily a contiguous one)
within which an individual conducts his/her
regular life routines.
(Eurostat 2008).

17. 17
The usual environment of an individual
includes the place of usual residence of the
household to which he/she belongs,
his/her own place of work or study and any
other place that he/she visits regularly and
frequently,
even when this place is located far away from
his/her place of usual residence or in another
locality.
(Eurostat 2008)

18. 18
The determination of the usual environment
should be based on the following criteria:
A. frequency of the trip (except for visits to
vacation homes);
B. duration of the trip;
C. the crossing of administrative or national
borders;
D. distance from the place of usual residence
E. the purpose of the visit.
(Eurostat 2014)

19. Activity space
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Source: Schönfelder & Axhausen 2003

20. Activity space can be defined by six
elements:
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1. home location;
2. duration of residence;
3. the number of activity locations in the vicinity of
home;
4. trips within the neighbourhood;
5. mobility to and from frequently visited activity
locations;
6. travel between and around the centres of daily
life
(Schönfelder & Axhausen 2010)

21. Available data collection methods
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 Traditional data sources:
 accommodation statistics
 surveys
 travel diaries
 interviews
 New available datasets:
 mobile positioning data
 GPS data

22. Mobile positioning data
Active positioning
Passive positioning
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23. Anchor point determining model
(Ahas et al. 2010).
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Home location
Work location

24. Domestic trips outside the usual environment in Estonia
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Source: Eurostat Feasibility Study 2014
Using LAU-2 for defining usual environment.
Using LAU-1 for defining usual environment.
Official domestic accommodation statistics (LAU-1).

25. Distance from place of residence
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26. GPS data + interviews
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27. 27
Regularly visited (min 4 times
per year) places in 2014
Interviews
Android-basedYouSense
smartphone application
GPS and phone use data from
year 2014

28. 28
REGULAR PLACES
A B
A B
A: 27 locations (20 of
them inTartu)
B: 31 locations (12 of
them inTartu)

29. A B
GPS POINTS from 2014
B
Distribution of GPS points in 5x5km grid
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A

30. Seasonal changes in activity patterns
July, 2014 September, 2014
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BB
Kernel densities of one month GPS data

31. Delimiting usual environment with administrative
borders (LAU-1) (e.g. movements in March)
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B

32. 32
Delimiting usual environment with administrative
borders (LAU-2)(e.g. all movements inTartu in 2014)
B

33. 33
Delimiting usual environment with the distance from
the place of usual residence (e.g. movements in March)
B

34. Discussion
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 Precise, longitudional data on individual level.
 There is a need for clarifying temporal and spatial
parametres of usual environment:
 frequency;
 duration;
 distance.
 Is there a need for qualitative data?
 Usual environment as a network of connected
places, activities and people?

35. Conclusions
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 Microdata from mobile devices gives new
opportunities for identifying and measuring
usual environment.
 New data for “old concepts”.
 Useful for online monitoring and marketing.
 Valuable input for tourism statistics.

36. Thank you for your attention!
Janika Raun
janika.raun@ut.ee
http://mobilitylab.ut.ee/eng/
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