The document discusses the utilization of mobile positioning data as a source for generating statistics, particularly focusing on its potential for tourism and transportation studies in Estonia. It outlines the feasibility of big data in replacing or enhancing traditional statistics while addressing challenges related to privacy and data accuracy. Significant findings include insights on ethnic segregation and human mobility patterns derived from mobile phone usage data.

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Using Passive Mobile Positioning
Data for Generating Statistics:
Estonian Experiences
Seminar. Statistics Finland
02.06.2014 Helsinki
Prof. Rein Ahas (University of Tartu)
http://mobilitylab.ut.ee/eng/
Objectives:
• BIG data as source for statistics?
• Use of Mobile Phone data for
statistical purposes

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Feasibility Study on the Use of
Mobile Positioning Data for Tourism
Statistics
Eurostat contract no. 30501.2012.001-2012.452
BIG DATA

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Do we need new data?
Can BIG data replace existing statistics?
Can we trust secondary BIG data?
Privacy…
ICT revolution - fastest change in
human behaviour

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ICT is changing society
(Sheller & Urry 2006):
• More communication = more travel
• More information = more spatial mobility
It is not possible to understand and
govern contemporary society
without digital information layers
- Quantitative
- Qualitative

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The Global Database of Events,
Language, and Tone (GDELT)
Georgetown University, Washington DC
http://gdeltproject.
org/
Do we think like:
„data managers“ – is there need to replace
traditional data with new BIG sources?
„end-users“ - what kind of data is needed
for managing this „new“ society?

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F1 success in1990:
budget, car, driver…
F1 success in 2014:
budget, sensors, …

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Paradigm shift:
• Intelligent transportation systems
• Smart City
• Monitoring systems
Scheveningen Memorandum „Big
Data and Official Statistics“ DGINS
1. Acknowledge that Big Data represent new
opportunities and challenges for Official
Statistics,
and therefore encourage the European Statistical
System and its partners to effectively examine
the potential of Big Data sources in that regard.
• EUROSTAT Task Force ‘Big Data and Official
Statistics’
Director Generals of the National Statistical Institutes (DGINS)

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Mobile phone data

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I Active Positioning
Locating phone with special Query:
„find“ „ask“ „record“…
Requires approval from the phone owner
Smartphone based questionnaires
• Tracking locations
• Recording sensor data
• Movement
• Phone use
• Noise
• …
• Asking questions in
phone

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II Passive mobile positioning
Memory files of Mobile Network Operator (MNO)
Call Detail Record (CDR), Data Detail Record
(DDR)…
Passive Positioning
Subscriber Activity Time Cell
3725264020 SMS 07.04.2014 12:15:00 43879
244121965188 Call 07.04.2014 12:15:01 43879
206201963365 SMS 07.04.2014 12:15:01 44866
244121965188 Data 07.04.2014 12:15:04 43879
244121965188 Call 07.04.2014 12:15:04 43879
244211964246 Data 07.04.2014 12:15:05 43877
244121965188 Call 07.04.2014 12:15:07 43879
24405239944 SMS 07.04.2014 12:15:08 48512
244211548784 Call 07.04.2014 12:15:11 48987
244121964444 Call 07.04.2014 12:15:14 45559
244051604891 Data 07.04.2014 12:15:15 45601
24201725641 SMS 07.04.2014 12:15:15 45463
244051965315 Data 07.04.2014 12:15:17 48987
244211963912 Call 07.04.2014 12:15:20 43570
244051605773 Data 07.04.2014 12:15:20 35550
244211914278 Data 07.04.2014 12:15:23 48987
24421417297 Call 07.04.2014 12:15:26 48987
24421838967 Data 07.04.2014 12:15:28 43951
244051965316 SMS 07.04.2014 12:15:29 43909
Antenna ID
Subscriber ID

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ONE MONTH OF DATA
150M records / month
Transportation studies

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Studying individual mobility:
Movement of University professor in Estonia
2007-2013
 Second home
439 days
 Work 1000
Movement of professor in world 2007-2013
694 days
34 states

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Generating transportation data
from Call Detail Records
Passive mobile
positiong data
Transportation
zones
Movement
vectors
Anchor points
model
Characterised
movements
Reference data
Penetration
model
Corrected
movements
OD-matricies and
temporal & social
coeficents
Modelling traffic
flows
30.11.2009 26 Erki Saluveer

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OD-Matrices -> transportation model
Publications in transportation
studies
Järv, O., Ahas, R. and Witlox, F. 2014. Understanding monthly variability in human
activity spaces: a twelve-month study using mobile phone call detail records.
Transportation Research Part C: Emerging Technologies 38 (1): 122–135.
Saluveer E, Ahas, R. 2014. Using Call Detail Records of Mobile Network Operators
for transportation studies, In Timmermans H. & Rasouli S. (eds.) Mobile
Technologies for Activity-Travel Data Collection & Analysis, IGI Global.
Jarv. O., Ahas, Saluveer, E., Derudder, B., Witlox, F. 2012. Mobile Phones in a
Traffic Flow: A Geographical Perspective to Evening Rush Hour Traffic Analysis
Using Call Detail Records, PLoS ONE 7(11),
http://dx.plos.org/10.1371/journal.pone.0049171
Ahas, R., Silm, S., Järv, O., Saluveer E., Tiru, M. 2010. Using Mobile Positioning
Data to Model Locations Meaningful to Users of Mobile Phones , Journal of Urban
Technology, 17(1): 3-27.
Ahas, R. Aasa, A., Silm, S., Tiru, M. 2010. Daily rhythms of suburban commuters’
movements in the Tallinn metropolitan area: case study with mobile positioning
data. Transportation Research C, 18: 45–54.

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Urban studies
Ethnic segregation studies:
 Russian-speaking people visit a smaller number of
districts than Estonians when travelling in Tallinn, in
Estonia and abroad.
Tallinn Estonia
(excluding
Tallinn)
Foreign
countries
Estonians 16.7 19.3 2.04
Russians 16.6 10.6 1.68
Difference with
language only (ref.
Estonian)
-0.189** -8.707*** -0.362***
Difference with other
characteristics (ref .
Estonian)
0.021 -8.157*** -0.117**

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Temporal segregation in City:
 Ethnic groups are more unevenly distributed in the evenings.
 Probability of interethnic contacts are higher on working hours (10-16).
 Ethnic groups are more unevenly distributed on residential areas than
on working hours.
Segregation in social networks:

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Publications in Urban Studies
Silm, S. & Ahas, R. 2014.The temporal variation of ethnic segregation in a city:
evidence from a mobile phone use dataset, Social Science Research 47: 30-43.
http://dx.doi.org/10.1016/j.ssresearch.2014.03.011
Silm, S. & Ahas, R. 2014. Ethnic differences activity spaces: The study of out-of-home
non-employment activities with mobile phone data, Annals of Association of American
Geographers 104(5): 542-559.
http://dx.doi.org/10.1080/00045608.2014.892362
Novak, J., Ahas, R., Aasa, A., Silm, S. 2013. Application of mobile phone location data
in mapping of commuting patterns and functional regionalization: a pilot study of
Estonia, Journal of Maps 9(1): 10-15.,
http://dx.doi.org/10.1080/17445647.2012.762331
Silm, S., Ahas, R., Nuga, M. 2013. Gender differences in space-time mobility patterns
in a post-communist city: a case study based on mobile positioning in the suburbs of
Tallinn. Environment and Planning B: Planning and Design 40(5) 814 – 828.
Silm,S., Ahas, R., 2010. 'The seasonal variability of population in Estonian
municipalities, Environment and Planning A, 42(10) 2527-2546.
Tourism data

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Balance of Payments – Travel Item
Monthly international travel statistics for
Balance of Payment calculations
Country level
Inbound and outbound (to and from Estonia)
Data since 2009
Inbound Travel
Indicators:
• Number of visits
• Number of days spent
• Number of nights spent
Breakdown:
• Country of origin
• Estonia as transit / destination
• Same-day / overnight visit
• Tourist / long-term visitor (resident)

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Outbound Travel
Indicators:
• Number of trips / visits
• Number of days spent
• Number of nights spent
Breakdown:
• Total abroad / specific country
• Country as transit / destination
• Same-day / overnight visit
• Tourist / long-term visitors (non-residents)

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Data Transfer
Monthly transfer of CSV files with prepared
Excel pivot tables

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Permanent
Transit
Visitor
Permanent
Temporary
Foreigner

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Publications in Tourism Studies:
Nilbe, K., Ahas, R., Silm, S. 2014. Evaluating the Travel Distances of Events and Regular
Visitors using Mobile Positioning Data: The case of Estonia, Journal of Urban Technology
21(2):
Kuusik, A., Tiru, M., Varblane, U., Ahas, R. 2011. Process innovation in destination
marketing:
use of passive mobile positioning (PMP) for segmentation of repeat visitors in case of
Estonia, Baltic Journal of Management 6(3): 378 – 399.
Tiru, M., Kuusik, A., Lamp, M-L., Ahas, R. 2010. LBS in marketing and tourism
management: measuring destination loyalty with mobile positioning data. Journal of
Location Based Services, 4(2): 120-140.
Ahas, R. 2010. Mobile positioning data in geography and planning, Editorial. Journal of
Location Based Services, 4(2): 67-69.
Tiru, M., Saluveer E., Ahas, R., Aasa, A. 2010. Web-based monitoring tool for assessing
space-time mobility of tourists using mobile positioning data: Positium Barometer.
Journal of Urban Technology, 17(1): 71-89.
Ahas, R. Aasa, A., Roose, A., Mark, Ü., Silm, S. 2008. Evaluating passive mobile
positioning data for tourism surveys: An Estonian case study. Tourism Management
29(3): 469–486.
Ahas, R., Aasa, A., Mark, Ü., Pae, T., Kull, T. 2007. Seasonal tourism spaces in Estonia:
case study with mobile positioning data. Tourism Management 28(3): 898–910.
Conclusions

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Conclusions I:
• Timeliness – fast data collection, digital
processing, automatic
• Better spatial and temporal accuracy
• Longitudiness – covering longer time period
and area
• …
Conclusions II
• Access to data complicated, privacy…
• Missing information about users,
purpose of trips, expenditures
• Sampling issues
• …

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Conclusions III
• Replacing existing data with BIG data?
• Improving existing data with BIG data?
• Collecting data about new aspects of social life?
• NEW PRODUCTS and CONSUMER GROUPS,
monitoring, real-time…
Thank you!
rein.ahas@ut.ee
Silm, S. & Ahas, R. 2014.The temporal variation of ethnic segregation in a
city: evidence from a mobile phone use dataset, Social Science Research
47: 30-43. http://dx.doi.org/10.1016/j.ssresearch.2014.03.011
Silm, S. & Ahas, R. 2014. Ethnic differences activity spaces: The study of
out-of-home non-employment activities with mobile phone data, Annals of
Association of American Geographers 104(5): 542-559.
http://dx.doi.org/10.1080/00045608.2014.892362

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„BIG data as mirror“ – society is trying to
understand fast changes