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Utilizing digital traces of mobile phones for understanding social dynamics in urban areas

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Abstract

Understanding land use in urban areas, from the perspective of social function, is beneficial for a variety of fields, including urban and highway planning, advertising, and business. However, big cities with complex social dynamics and rapid development complicate the task of understanding these social functions. In this paper, we analyze and interpret human social function in urban areas as reflected in cellular communication usage patterns. We base our analysis on digital traces left by mobile phone users, and from this raw data, we derive a varied collection of features that illuminate the social behavior of each land use. We divide space and time into basic spatiotemporal units and classify them according to their land use. We categorize land uses with a leveled hierarchy of semantic categories that include different levels of detail resolution. We apply the above methodology to a dataset consisting of 62 days of cellular data recorded in nine cities in the Tel Aviv district. The methodology proved beneficial with an accuracy rate ranging from 84 to 91%, dependent on land use label resolution. In addition, analyzing the results sheds light on some of the limitations of relying solely on cellular communication as a data resource. We discuss some of these problems and offer applicable solutions.

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References

  1. Alberti M, Marzluff JM, Shulenberger E, Bradley G, Ryan C, Zumbrunnen C (2003) Integrating humans into ecology: opportunities and challenges for studying urban ecosystems. AIBS Bull 53(12):1169–1179

    Google Scholar 

  2. Altmann A, Toloşi L, Sander O, Lengauer T (2010) Permutation importance: a corrected feature importance measure. Bioinformatics 26(10):1340–1347.3

    Article  Google Scholar 

  3. Arribas-Bel D, Tranos E (2018) Characterizing the spatial structure(s) of cities “on the fly”: the space-time calendar. Geogr Anal 50(2):162–181

    Article  Google Scholar 

  4. Ben Zion E, Lerner B (2017) Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns. European Symposium on Artificial Networks, Computational Intelligence and Machine Learning (ESANN2017), Bruges

    Google Scholar 

  5. Ben Zion E, Lerner B (2018) Identifying and predicting social lifestyles in people’s trajectories by neural networks. EPJ Data Sci 7(45):1–27

    Google Scholar 

  6. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  7. Calabrese F, Diao M, Di Lorenzo G, Ferreira J Jr, Ratti C (2013) Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp Res C 26:301–313

    Article  Google Scholar 

  8. Calabrese F, Ferrari L, Blondel VD (2015) Urban sensing using mobile phone network data: a survey of research. ACM Comput Surv 47(2):25

    Article  Google Scholar 

  9. Candia J, González MC, Wang P, Schoenharl T, Madey G, Barabási AL (2008) Uncovering individual and collective human dynamics from mobile phone records. J Phys A Math Theor 41(22):224015

    Article  MathSciNet  Google Scholar 

  10. Diao M, Zhu Y, Ferreira J Jr, Ratti C (2016) Inferring individual daily activities from mobile phone traces: a Boston example. Environ Plann B Plann Des 43(5):920–940

    Article  Google Scholar 

  11. Díaz-Uriarte R, De Andres SA (2006) Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7(1):3

    Article  Google Scholar 

  12. Gao S, Janowicz K, Couclelis H (2017) Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans GIS 21(3):446–467

    Article  Google Scholar 

  13. Heiden U, Heldens W, Roessner S, Segl K, Esch T, Mueller A (2012) Urban structure type characterization using hyperspectral remote sensing and height information. Landsc Urban Plan 105(4):361–375

    Article  Google Scholar 

  14. Ho TK (1995) Random decision forest. In: Proceedings of the 3rd International Conference on Document Analysis and recognition. IEEE, Montreal, pp 278–282

    Google Scholar 

  15. Hu T, Yang J, Li X, Gong P (2016) Mapping urban land use by using landsat images and open social data. Remote Sens 8(2):151

    Article  Google Scholar 

  16. Isaacman S, Becker R, C’aceres R, Kobourov S (2011) Identifying important places in people’s lives from cellular network data. In: International Conference on Pervasive Computing. Springer, pp 133–151

  17. Khoroshevsky F, Lerner B (2017) Human mobility-pattern discovery and next-place prediction from GPS data. In: IAPR workshop on multimodal pattern recognition of social signals in human-computer-interaction. Springer, Berlin, pp 24–35

    Chapter  Google Scholar 

  18. Liu Y, Liu X, Gao S, Gong L, Kang C, Zhi Y, Chi L, Shi L (2015) Social sensing: a new approach to understanding our socioeconomic environments. Ann Assoc Am Geogr 105(3):512–530

    Article  Google Scholar 

  19. Liu X, Kang C, Gong L, Liu Y (2016) Incorporating spatial interaction patterns in classifying and understanding urban land use. Int J Geogr Inf Sci 30(2):334–350

    Article  Google Scholar 

  20. Liu X, He J, Yao Y, Zhang J, Liang H, Wang H, Hong Y (2017) Classifying urban land use by integrating remote sensing and social media data. Int J Geogr Inf Sci 31(8):1675–1696

    Article  Google Scholar 

  21. Lu D, Weng Q (2006) Use of impervious surface in urban land-use classification. Remote Sens Environ 102(1):146–160

    Article  Google Scholar 

  22. Patel S, Kientz J, Hayes G, Bhat S, Abowd G (2006) Farther than you may think: an empirical investigation of the proximity of users to their mobile phones. In: International conference on ubiquitous computing. Springer, Orange County, pp 123–140

    Google Scholar 

  23. Pei T, Sobolevsky S, Ratti C, Shaw SL, Li T, Zhou C (2014) A new insight into land use classification based on aggregated mobile phone data. Int J Geogr Inf Sci 28(9):1988–2007

    Article  Google Scholar 

  24. Poushter J (2016) Smartphone ownership and internet usage continues to climb in emerging economies. Pew Research Center 22:1–44

    Google Scholar 

  25. Shen Y, Karimi K (2016) Urban function connectivity: characterisation of functional urban streets with social media check-in data. Cities 55:9–21

    Article  Google Scholar 

  26. Sheng C, Zheng Y, Hsu W, Lee ML, Xie X (2010) Answering top-k similar region queries. In: International conference on database systems for advanced applications. Springer, Berlin, Heidelberg, pp 186–201

    Chapter  Google Scholar 

  27. Siła-Nowicka K, Vandrol J, Oshan T, Long JA, Demšar U, Fotheringham AS (2016) Analysis of human mobility patterns from GPS trajectories and contextual information. Int J Geogr Inf Sci 30(5):881–906

    Article  Google Scholar 

  28. Theobald DM (2014) Development and applications of a comprehensive land use classification and map for the US. PLoS One 9(4):e94628

    Article  MathSciNet  Google Scholar 

  29. Toch E, Lerner B, Ben-Zion E, Ben-Gal I (2018) Analyzing large-scale human mobility data: a survey of machine learning methods and applications. Knowl Inf Syst:1–23

  30. Toole JL, Ulm M, González MC, Bauer D (2012) Inferring land use from mobile phone activity. In: Proceedings of the ACM SIGKDD international workshop on urban computing. ACM, Beijing, pp 1–8

    Google Scholar 

  31. Trasarti R, Olteanu-Raimond AM, Nanni M, Couronné T, Furletti B, Giannotti F, Smoreda Z, Ziemlicki C (2015) Discovering urban and country dynamics from mobile phone data with spatial correlation patterns. Telecommun Policy 39(3):347–362

    Article  Google Scholar 

  32. Tu W, Cao J, Yue Y, Shaw SL, Zhou M, Wang Z, Chang X, Xu Y, Li Q (2017) Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns. Int J Geogr Inf Sci 31(12):2331–2358

    Article  Google Scholar 

  33. Wang H, Calabrese F, Di Lorenzo G, Ratti C (2010) Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: Intelligent transportation systems (ITSC), 2010 13th international IEEE conference. IEEE, Funchal, pp 318–323

    Google Scholar 

  34. Wen D, Huang X, Zhang L, Benediktsson JA (2016) A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation. Geosci Remote Sens 54(1):609–625

    Article  Google Scholar 

  35. Wu C, Zhang L, Zhang L (2016) A scene change detection framework for multi-temporal very high resolution remote sensing images. Signal Process 124:184–197

    Article  Google Scholar 

  36. Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, Beijing, pp 325–334

    Google Scholar 

  37. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Beijing, pp 186–194

    Google Scholar 

  38. Zhao Z, Shaw SL, Xu Y, Lu F, Chen J, Yin L (2016) Understanding the bias of call detail records in human mobility research. Int J Geogr Inf Sci 30(9):1738–1762

    Article  Google Scholar 

  39. Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (TIST) 5(3):38

    Google Scholar 

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Funding

This research was supported by a grant from the Israel Ministry of Science and Technology.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

    Oded Zinman & Boaz Lerner

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  1. Oded Zinman
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  2. Boaz Lerner
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Correspondence to Boaz Lerner.

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Zinman, O., Lerner, B. Utilizing digital traces of mobile phones for understanding social dynamics in urban areas. Pers Ubiquit Comput 24, 535–549 (2020). https://doi.org/10.1007/s00779-019-01318-w

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  • DOI: https://doi.org/10.1007/s00779-019-01318-w

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