Abstract
In telecommunications, the billing data of each telephone, denoted call detail records (CDRs), are a large and rich database with information that can be geo-located. By analyzing the events logged in each antenna, a set of time series can be constructed measuring the number of voice and data events in each time of the day. One question that can be addressed using these data involves estimating the movement or flow of people in the city, which can be used for prediction and monitoring in transportation or urban planning. In this work, geostatistical estimation techniques such as kriging and inverse distance weighting (IDW) are used to numerically estimate the flow of people. In order to improve the accuracy of the model, secondary information is included in the estimation. This information represents the locally varying anisotropy (LVA) field associated with the major streets and roads in the city. By using this technique, the flow estimation can be obtained with a better quantitative and qualitative interpretation. In terms of storage and computing power, the volume of raw information is extremely large; for that reason big data technologies are mandatory to query the database. Additionally, if high-resolution grids are used in the estimation, high-performance computing techniques are necessary to speed up the numerical computations using LVA codes. Case studies are shown, using voice/data records from anonymized clients of Telefónica Movistar in Santiago, capital of Chile.
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References
Amazon Inc (2008) Amazon elastic compute cloud. URL: http://aws.amazon.com
Boisvert J, Deutsch CV (2011) Programs for kriging and sequential Gaussian simulation with locally varying anisotropy using non-Euclidean distances. Comput Geosci 37(4):495–510
Boisvert JB, Manchuk JG, Deutsch CV (2009) Kriging in the presence of locally varying anisotropy using non-Euclidean distances. Math Geosci 41(5):585–601
Calabrese F, Colonna M, Lovisolo P, Parata D, Ratti C (2011) Real-time urban monitoring using cell phones: a case study in Rome. IEEE Trans Intell Transp Sys 12:141–151
Candia J, González MC, Wang P, Schoenharl T, Greg M, Barabási A-L (2008) Uncovering individual and collective human dynamics from mobile phone records. J Phys A: Math Theor 41:224015
Chandra R, Dagum L, Kohr D, Maydan D, McDonald J, Menon R (2001) Parallel programming in openMP. Morgan Kaufmann Publishers, San Francisco
Chilès JP, Delfiner P (2012) Geostatistics: modeling spatial uncertainty, 2nd edn. Wiley, New York, p 734, Wiley series in probability and statistics
Colak S, Alexander LP, Alvim BG, Mehndiretta SR, González MC (2015) Analyzing cell phone location data for urban travel: current methods, limitations and opportunities. Transportation Research Board 94th Annual Meeting, Issue, 15–5279
Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Gates A (2011) Programming pig, 1st edn. O’Reilly Media, Sebastopol
Gonzalez MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453:779–782
Görnerup O (2012) Scalable mining of common routes in mobile communication network traffic data. In: Proceedings of the 10th international conference on Pervasive Computing, Pervasive’12, pp 99–106
Haklay M, Weber P (2008) OpenStreetMap: user-generated street maps. Pervasive Comput IEEE 7:12–18
Horanont TA (2012) Study on urban mobility and dynamic population estimation by using aggregate mobile phone sources. CSIS discussion paper no. 115. Technical report, Center for Spatial Information Science, The University of Tokyo, 2012
Horanont T, Shibasaki R (2008) An implementation of mobile sensing for large-scale urban monitoring. Proceedings of Urbansense’08 2008
ITC (2015) The world in 2015 - ICT facts and figures. International Telecommunications Union, ICT Data and Statistics Division, Geneva
Leontiadis I, Lima A, Kwak H, Stanojevic R, Wetherall D, Papagiannaki K (2014) From cells to streets: estimating mobile paths with cellular-side data. In: Proceedings of the 10th ACM international on conference on emerging Networking Experiments and Technologies (CoNEXT ‘14). ACM, New York, pp 121–132
Lima A, Stanojevic R, Papagiannaki D, Rodriguez P, González MC (2016) Understanding individual routing behaviour. J R Soc Interface 13:20160021
Li Y, Deng H, Li J, Peng C, Lu S (2016) Instability in distributed mobility management: revisiting configuration management in 3G/4G mobile networks. ACM SIGMETRICS 2015, Antibes Juan-les-pines
Maksuda L, Boisvert JB (2015) Inference of locally varying anisotropy fields from diverse data sources. Comput Geosci 82:170–182
Mao H, Shuai X, Ahn YY, Bollen J (2013) Mobile communications reveal the regional economy in cote D’Ivoire. Cambridge, MA. NetMob 2013: data for development challenge
Microsoft Corporation (2010) Microsoft azure. URL: https://azure.microsoft.com
Microsoft Corporation (2013) Azure HDInsight. URL: https://azure.microsoft.com/en-us/services/hdinsight/
Oliver N, Matic A, Frias-Martinez E (2015) Mobile network data for public health: opportunities and challenges. Front Public Health 3:189
Peredo O, Ortiz JM, Herrero JR (2015) Acceleration of the Geostatistical Software Library (GSLIB) by code optimization and hybrid parallel programming. Comput Geosci 85:210–233, PA (December 2015)
Saha B, Shah H, Seth S, Vijayaraghavan G, Murthy A, Curino C (2015) Apache Tez: a unifying framework for modeling and building data processing applications. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD ‘15). ACM, New York, pp 1357–1369
Saravanan M, Pravinth S, and Holla P (2011) Route detection and mobility based clustering. In: Proceedings of IEEE 5th international conference on Internet Multimedia Systems Architecture and Application (IMSAA), pp 1–7
Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021
Soto V, Frias-Martinez V, Virseda J, Frias-Martinez E (2011) Prediction of socioeconomic levels using cell phone records. Proceedings of User Modeling, Adaption and Personalization: 19th International Conference, UMAP 2011, Girona, Spain, July 11–15, 2011. Ed. Springer, Berlin, pp 377–388
Traag VA, Browet A, Calabrese F, Morlot F (2011) Social event detection in massive mobile phone data using probabilistic location inference. Privacy, Security, Risk and Trust (PASSAT) and 2011 I.E. Third International Conference on Social Computing (SocialCom), 2011, pp 625–628
Toole JL, Colak S, Sturt B, Alexander LP, Evsukoff A, González MC (2015) The path most traveled: travel demand estimation using big data resources. Transp Res Part C: Emerg Technol 58(Part B):162–177
White T (2009) Hadoop: the definitive guide, 1st edn. O’Reilly Media, Sebastopol
Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I (2010) Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX conference on Hot topics in cloud computing (HotCloud’10). USENIX Association, Berkeley, pp 10–10
Acknowledgments
The authors would like to acknowledge the project CORFO 13CEE2-21592 (2013-21592-1-INNOVA_PRODUCCION2013-21592-1), Telefónica Investigación y Desarrollo SPA and Telefónica Chile SA for the support and data provided in this study.
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Peredo, O.F., García, J.A., Stuven, R., Ortiz, J.M. (2017). Urban Dynamic Estimation Using Mobile Phone Logs and Locally Varying Anisotropy. In: Gómez-Hernández, J., Rodrigo-Ilarri, J., Rodrigo-Clavero, M., Cassiraga, E., Vargas-Guzmán, J. (eds) Geostatistics Valencia 2016. Quantitative Geology and Geostatistics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-46819-8_66
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