MOBILE PHONE DATA IN THE SANDBOX Pilar Rey del Castillo Mobile phone data in the Sandbox Special case only since October 2014 Limited information provided in the dataset Still very interesting to analyse ID: 365139
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STARTING EXPLORING MOBILE PHONE DATA IN THE SANDBOX
Pilar Rey del CastilloSlide2
Mobile phone data in the SandboxSpecial case: only since October 2014Limited information provided in the datasetStill very interesting to analyseSensors of human and social behaviour (location...)
Example of requirements of exploratory step comparing with other type of data in the SandboxAim describe initial steps in attempting to produce meaningful results for statistical purposes
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Location or positioning dataConcept in mobile phones & statistics contextUser assigned to a number of neighbouring antennas for load balancing reasonsTypesActive
Passive: Call Detail Records (CDRs)...3
Passive location
occasional samples of the approximate locations of the phone's userSlide4
Mobile phones datasets (1)D4D Challenge: Orange's “Data for development” in Ivory CoastAnonymised Call Detail Records (CDRs) of outgoing phone calls & sms exchanges Orange’s customers in Ivory Coast Between December 1, 2011 and April 28, 2012 (150 days,
5 months) Sandbox IT infrastructure: perfect 4Slide5
Mobile phones datasets (2)Total antenna-to-antenna traffic on an hourly basis ( 5 million customers)Individual trajectories for 50.000 customers for two week time windows
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Literature exploiting locationSupplementary information at the micro level (ground truth) Lausanne Data Collection Campaign (Nokia 2009-2011)Reality Mining Project (MIT 2004-2005)Ad hoc experiments, conducting surveys… :
Isaacman et al. (2011), De Oliveira et al. (2011)…Just CDRs: Assumptions on the users' behaviour… Orange Data Challenges (Ivory Coast, Senegal)Järv
et al. (Estonia, 2012)Kung et al. (Portugal, IC, Saudi Arabia, Boston, Milan, 2014)…
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Ivory Coast dataPositioning data our aim: human home -> work commuting figuresWay to proceed: obtain results under certain assumptions and compare First assumptions Orange's customers represent population (96% subscriptions per 100 inhabitants, 2013)
Behaviour of 50000 customers sample is representative of mobility behaviour (to be assessed later)7Slide8
2nd step: model to draw meaningful informationProblem of oscillations: antennas aggregation by section = county x urbanization
157 sections Problem of giving a meaning to user's location: daily & weekly patterns of use as discriminative features
Isaacman et al. (2011): home weekends + weekdays between 7 pm & 7 am
work weekdays between 1 pm & 5 pm Kung et al. (2014): home
weekdays between 8 pm & 8 am work weekdays between 8 am &
8 pmApart from other sophisticated filtering…
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Commuting in Ivory CoastSample of 50000 customers 51% cluster 1 28% cluster 2
21% cluster 3Almost 50% of the sample home -> work located Estimate cross-tabulation commuting between Ivory Coast sections
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11Main commutes (%) home-> work between
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Final remarksCDRs useful tool to learn and test new methods (although no reliable figures produced)Just a portion of possible ways to exploit CDRs promising source (need more research)Another possible research strand: develop an "OfficialStatistics" app for smartphones gathering
ground truth12Slide13
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Referencesde Oliveira, R.,Karatzoglou, A., Cerezo, P. C., de Vicuña, A. A. L. and Oliver, N. (2011), “Towards a psychographic user model from mobile phone usage”, in Desney S. Tan; SaleemaAmershi; Bo Begole; Wendy A. Kellogg &
ManasTungare, ed., 'CHI Extended Abstracts' , ACMIsaacman, S., Becker, R., Cáceres, R., Kobourov, S., Martonosi
, M., Rowland, J. and Varshavsky, A. (2011), “Identifying Important Places in People’s Lives from Cellular Network Data”, Lecture Notes in Computer Science Vol. 6696, pp. 133-151.Järv,O., Ahas, R.,
Saluveer, E., Derudder, B.,and 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.0049171Kung, K.S., Greco, K., Sobolevsky, S., and
Ratti, C. (2014), “Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data”, PLoS ONE 9(6): e96180. doi:10.1371/journal.pone.0096180
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