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Using Passive Mobile Positioning Data for Generating Statistics:

Estonian Experiences

Seminar. Statistics Finland02.06.2014 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 ofMobile Positioning Data for Tourism

StatisticsEurostat 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 inhuman 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“ DGINS1. Acknowledge that Big Data represent new opportunities and challenges for OfficialStatistics,

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 PositioningSubscriber Activity Time Cell

3725264020 SMS 07.04.2014 12:15:00 43879244121965188 Call 07.04.2014 12:15:01 43879206201963365 SMS 07.04.2014 12:15:01 44866244121965188 Data 07.04.2014 12:15:04 43879244121965188 Call 07.04.2014 12:15:04 43879244211964246 Data 07.04.2014 12:15:05 43877244121965188 Call 07.04.2014 12:15:07 4387924405239944 SMS 07.04.2014 12:15:08 48512244211548784 Call 07.04.2014 12:15:11 48987244121964444 Call 07.04.2014 12:15:14 45559244051604891 Data 07.04.2014 12:15:15 4560124201725641 SMS 07.04.2014 12:15:15 45463244051965315 Data 07.04.2014 12:15:17 48987244211963912 Call 07.04.2014 12:15:20 43570244051605773 Data 07.04.2014 12:15:20 35550244211914278 Data 07.04.2014 12:15:23 4898724421417297 Call 07.04.2014 12:15:26 4898724421838967 Data 07.04.2014 12:15:28 43951244051965316 SMS 07.04.2014 12:15:29 43909

Antenna ID

Subscriber ID

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ONE MONTH OF DATA150M records / month

Transportation studies

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Studying individual mobility: Movement of University professor in Estonia

2007-2013

Second home439 days

Work 1000

Movement of professor in world 2007-2013

694 days

34 states

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Generating transportation datafrom Call Detail Records

Passive mobile positiong data

Transportation zones

Movement vectors

Anchor pointsmodel

Characterised movements

Reference dataPenetration

model

Corrected movements

OD-matricies andtemporal & social

coeficents

Modelling traffic flows

30.11.2009 26 Erki Saluveer

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OD-Matrices -> transportation model

Publications in transportationstudies

Järv, O., Ahas, R. and Witlox, F. 2014. Understanding monthly variability in humanactivity 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 Operatorsfor transportation studies, In Timmermans H. & Rasouli S. (eds.) MobileTechnologies 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 AnalysisUsing 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 PositioningData to Model Locations Meaningful to Users of Mobile Phones , Journal of UrbanTechnology, 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 positioningdata. 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 withlanguage only (ref.

Estonian)

-0.189** -8.707*** -0.362***

Difference with othercharacteristics (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 StudiesSilm, 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 forBalance 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 preparedExcel 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 RegularVisitors using Mobile Positioning Data: The case of Estonia, Journal of Urban Technology21(2): Kuusik, A., Tiru, M., Varblane, U., Ahas, R. 2011. Process innovation in destinationmarketing: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 tourismmanagement: 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 assessingspace-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 mobilepositioning data for tourism surveys: An Estonian case study. Tourism Management29(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 tounderstand fast changes