Janika Raun

Big Data seminar at Statistics Finland

19.11.2015

Defining usual environment with mobile positioning data

Outline

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Who we are?

What kind of data and methodologies we use?

Defining usual environment with mobile positioning data.

Questions?

Mobility Lab, University of Tartu

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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Mobility Lab, University of Tartu

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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Data and methodologies

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Mobile positioning data

Tracking the location coordinates of mobile phones.

Active positioning

Passive positioning

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Active mobile positioning

Tracking phones with special queries.

Specifically composed sample

Fixed time intervals

Questionnaires

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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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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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Spatial accuracy of the data

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Example of database

E.g 24h of data

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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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Other data collection methods

Android-based YouSense smartphone application

Data about location and phone use

Questionnaires

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Defining usual environment with mobile positioning data

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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)

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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).

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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)

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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)

Activity space

19 Source: Schönfelder & Axhausen 2003

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)

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

Mobile positioning data

Active positioning

Passive positioning

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Anchor point determining model (Ahas et al. 2010).

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Home location

Work location

Domestic trips outside the usual environment in Estonia

24 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).

Distance from place of residence

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GPS data + interviews

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Regularly visited (min 4 times per year) places in 2014

Interviews Android-based YouSense smartphone application

GPS and phone use data from year 2014

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REGULAR PLACES

A B

A B

A: 27 locations (20 of them in Tartu)

B: 31 locations (12 of them in Tartu)

A B

GPS POINTS from 2014

B

Distribution of GPS points in 5x5km grid

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A

Seasonal changes in activity patterns

July, 2014 September, 2014

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B B

Kernel densities of one month GPS data

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

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B

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

B

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

B

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?

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.

Thank you for your attention!

Janika Raun

janika.raun@ut.ee

http://mobilitylab.ut.ee/eng/

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