Using Passive Mobile Positioning Data for Generating Statistics: Estonian Experiences, Rein Ahas
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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
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