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The location selection of ground-based promotion in city using human mobility data

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Abstract

With the rapid development of urbane-centered economy, today swelling cities is being filled with massive socioeconomic activities, and urban area have gone through strong but heterogeneous sprawl. Marketer has to put more resources for the ground-based promotion of their products in such metropolis. Therefore, it is very significance to help marketer choose a number of more-suited districts in urban area for promotion. This paper investigates location selection problem of ground-based promotion in city with urban computing’s perspective. In a provincial capital of China, massive call logs from millions of people have been used to abstract human mobility patterns and urban area have been segmented to hundreds of districts so that we model this problem with two essential cases and give corresponding solutions via greedy algorithms. Our theoretical analysis and experimental evolution show the algorithm is effectiveness. This application framework can be used in ground-based promotion of many application scenarios such as o2o applications that fusing of multisource mobility data.

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References

  1. Albert R, Jeong H, Barab’asi A-L (1999) Diameter of the world wide web. Nature 401:130–131

    Article  Google Scholar 

  2. 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 Syst 12(1):141–151

    Article  Google Scholar 

  3. Caughlin TT, Ruktanonchai N, Acevedo MA et al (2013) Place-based attributes predict community membership in a mobile phone communication network. PLoS One 8(2), e56057

    Article  Google Scholar 

  4. Chaffey D (2000) Achieving internet marketing success. Mark Rev 1(1):35–59

    Article  Google Scholar 

  5. Deville P, Linard C, Martin S et al (2014) Dynamic population mapping using mobile phone data. Proc Natl Acad Sci 111(45):15888–15893

    Article  Google Scholar 

  6. Ge Y, Xiong H, Tuzhilin A, Xiao K, Gruteser M, Pazzani M (2010) An energy- efficient mobile recommender system, Proc KDD’10, 899–908

  7. Gonzalez MC, Hidalgo CA, Barabasi A-L (2008) Understanding individual human mobility patterns. Nature 453(7196):779–782

    Article  Google Scholar 

  8. Gu W, Lv Z, Hao M (2015) Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools Appl, 1–16

  9. Jiang S, Ferreira Jr J, Gonzalez MC (2012) Discovering urban spatial-temporal structure from human activity patterns. Proc ACM SIGKDD Int Workshop Urban Comput. ACM, 95–102

  10. Jiang S, Ferreira J, González MC (2012) Clustering daily patterns of human activities in the city. Data Min Knowl Disc 25(3):478–510

    Article  MathSciNet  MATH  Google Scholar 

  11. Jiang D, Ying X, Han Y et al (2016) Collaborative multi-hop routing in cognitive wireless networks. Wirel Pers Commun 86(2):901–923

    Article  Google Scholar 

  12. Li X, Lv Z, Hijazi I et al (2016) Assessment of urban fabric for smart cities. IEEE ACCESS

  13. Li X, Lv Z, Wang W et al (2016) WebVRGIS based traffic analysis and visualization system. Adv Eng Softw 93:1–8

    Article  Google Scholar 

  14. Li X, Lv Z, Zheng Z et al (2015) Assessment of lively street network based on geographic information system and space syntax. Multimedia Tools Appl, 1–19

  15. Lv Z, Chen G, Zhong C et al (2012) A framework for multi-dimensional WebGIS based interactive online virtual community. Adv Sci Lett 7(1):215–219

    Article  Google Scholar 

  16. Lv Z, Li X, Zhang B et al (2016) Managing big city information based on WebVRGIS. IEEE ACCESS

  17. Ma H, Yang H, Lyu M R et al (2008) Mining social networks using heat diffusion processes for marketing candidates selection[C]. Proc 17th ACM Conf Information Knowledge Manage. ACM, 233–242

  18. Mahajan V, Muller E, Bass F (1999) New product diffusion models in marketing: a review and directions for research. J Mark 54(1):1–26

    Article  Google Scholar 

  19. Ratti C, Sobolevsky S, Calabrese F et al (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS One 5(12), e14248

    Article  Google Scholar 

  20. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In Proc ACM SIGKDD Conf., 61–70

  21. Roth C, Kang SM, Batty M et al (2011) Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS One 6(1), e15923

    Article  Google Scholar 

  22. Song X, Chi Y, Hino K, Tseng BL (2007) Information flow modeling based on diffusion rate for prediction and ranking. In Proc. of the ACM WWW Conf, 191–200

  23. Song X, Tseng BL, Lin CY, Sun MT (2006) Personalized recommendation driven by information flow. In Proc ACM SIGIR Conf, 509–516

  24. Su T, Lv Z, Gao S et al (2014) 3d seabed: 3d modeling and visualization platform for the seabed. Multimedia Expo Workshops (ICMEW), 2014 I.E. Int Conf IEEE, 1–6

  25. Understanding local live zones of urban area using cellphone call detail record: a case study of Wuhan, in press, 2016

  26. Wang W, Lv Z, Li X et al (2015) Virtual reality based GIS analysis platform. Neural Information Proc. Springer International Publishing, 638–645

  27. Wang Y, Yuan NJ, Lian D et al (2015) Regularity and conformity: location prediction using heterogeneous mobility data. Proc 21th ACM SIGKDD Int Conf Knowledge Discovery Data Mining. ACM, 1275–1284

  28. Yuan NJ, Zheng Y, Xie X et al (2015) Discovering urban functional zones using latent activity trajectories. Knowledge Data Eng IEEE Trans 27(3):712–725

    Article  Google Scholar 

  29. Yuan J, Zheng Y, Xie X, Sun G (2011) Driving with knowledge from the physical world, Proc KDD’11, 316–324

  30. Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and POIs. Proc 18th ACM SIGKDD Int Conf Knowledge Discovery Data Mining. ACM, 186–194

  31. Zhang X, Han Y, Hao DS et al (2015) ARPPS: augmented reality pipeline prospect system. Neural Information Proc. Springer International Publishing, 647–656

  32. Zhao S, Wu H, Tu L et al (2014) Segmentation of urban areas using vector-based model. Ubiquitous Intelligence Computing, 2014 I.E. 11th Intl Conf IEEE 11th Intl Conf Autonomic Trusted Computing, IEEE 14th Intl Conf Scalable Comput Commun Associated Workshops (UTC-ATC-ScalCom). IEEE, 412–416

  33. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol (TIST) 6(3):29

    Google Scholar 

  34. Zhong C, Arisona SM, Huang X et al (2013) Identifying spatial structure of urban functional centers using travel survey data: a case study of Singapore. Proc First ACM SIGSPATIAL Int Workshop Computational Models Place. 28–33

  35. Zhu WY, Peng WC, Chen LJ et al (2015) Modeling user mobility for location promotion in location-based social networks. Proc 21th ACM SIGKDD Int Conf Knowledge Discovery Data Mining. ACM, 1573–1582

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Authors and Affiliations

  1. School of EIC, Huazhong University of Science and Technology, Wuhan, China

    Shu Chen & Benxiong Huang

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  1. Shu Chen
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  2. Benxiong Huang
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Correspondence to Shu Chen.

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Chen, S., Huang, B. The location selection of ground-based promotion in city using human mobility data. Multimed Tools Appl 75, 17745–17760 (2016). https://doi.org/10.1007/s11042-016-3488-x

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  • DOI: https://doi.org/10.1007/s11042-016-3488-x

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