Skip to main content
Springer Nature Link
Log in

Proximity-Based Aggregation Method for LBS Human Mobility Data

  • Conference paper
  • First Online:
Spatial Data and Intelligence (SpatialDI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12567))

Included in the following conference series:

  • 715 Accesses

Abstract

Human mobility is an inevitable element of urban development. The rise of big data has accelerated the popularity of various location-based service (LBS) data, and mobility data has gradually become the mainstream data. When mobility data is applied to the research into human mobility, geographic location will be integrated into a complex network structure composed of massive human mobility data to form a geographic network space, which will integrate human mobility and urban development in the era of big data. Mobility data features large quantity, complexity and redundancy, and its aggregation is valuable. In this paper, based on LBS human mobility data, a geographic proximity-based aggregation method for mobility data is proposed, aiming at aggregating mobility data of geographic proximity according to human mobility values to achieve mobility data aggregation in geographic sense without changing the situation of data, and generate a series of urban aggregation areas formed by closely approximate internal mobility data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Deng, Y., Si, Y.: The spatial pattern and influence factors of urban expansion: a case study of Beijing. Geogr. Res. 34(12), 2247–2256 (2015)

    Google Scholar 

  2. Han, H., Yang, C., Song, J.: The spatial-temporal characteristic of land use change in Beijing and its driving mechanism. Econ. Geogr. 35(05), 148–154+197 (2015)

    Google Scholar 

  3. Yuan, Y., Xu, X., Xue, D.: Spatial distribution, evolution and driving force of non-registered population of Guangzhou metropolitan area in 1990–2000. Econ. Geogr. (02), 250–255 (2007)

    Google Scholar 

  4. Li, J., Ning, Y.: Population spatial change and urban spatial restructuring in Shanghai since the 1990s. Urban Plann. Forum (02). 20–24 (2007)

    Google Scholar 

  5. Zhao, M., Wang, D.: The spatial sprawl and driving mechanism of the floating population in Beijing metropolitan areas. Scientia Geographica Sinica 39(11), 1729–1738 (2019)

    Google Scholar 

  6. Zhao, M., Liu, S., Qi, W.: Comparison on foreign and domestic migrant communities: characteristics and effects. City Plann. Rev. 39(12), 19–27 (2015)

    Google Scholar 

  7. Alba, R.D., Logan, J.R., Stults, B.J., et al.: Immigrant groups in the suburbs: a reexamination of suburbanization and spatial assimilation. Am. Sociol. Rev. 64(3), 446–460 (1999)

    Article  Google Scholar 

  8. Allen, J.P., Turner, E.: Ethnic residential concentrations in United States metropolitan areas. Geogr. Rev. 95(2), 267–285 (2005)

    Article  Google Scholar 

  9. Logan, J.R., Zhang, W., Alba, R.D.: Immigrant enclaves and ethnic communities in New York and Los Angeles. Am. Sociol. Rev. 67(2), 299–322 (2002)

    Article  Google Scholar 

  10. Bao, S., Zhang, B.: The spatial distribution and growing trends of migrant population in Beijing. J. Capital Normal Univ. (Nat. Sci. Ed.) 33(02), 74–78 (2012)

    Google Scholar 

  11. Feng, J., Zhou, Y.: The growth and distribution of population in Beijing metropolitan area (1982–2000). Acta Geogr. Sin. 06, 903–916 (2003)

    Google Scholar 

  12. Geng, H., Shen, D.: Spatial distribution and mechanism of migrant population in shanghai. City Plann. Rev. 12, 21–31 (2009)

    Google Scholar 

  13. Yi, C., Gao, B., Huang, Y.: Spatial distribution of Beijing's Hukou-residence separated population and its determinants. Chin. J. Popul. Sci. (01), 33–43+126–127 (2014)

    Google Scholar 

  14. Jie, Z., Xiao, L., Bo, X.: Spatial distribution and variation of floating population in megacities --case study of Beijing, Shanghai, Guangzhou and Wuhan. Urban Plann. Forum (06), 56–62 (2015)

    Google Scholar 

  15. Chen, T., Liu, S.: Research summary of the floating population in China. J. Anhui Agric. Sci. 37(30), 14940–14942+14948 (2009)

    Google Scholar 

  16. Gu, C., Cai, J., Zhang, W., et al.: A study on the patterns of migration in Chinese large and medium cities. Acta Geographica Sinica (03), 14–22 (1999)

    Google Scholar 

  17. Ding, J., Liu, Z., Cheng, D., et al.: Areal differentiation of inter-provincial migration in China and characteristics of the flow field. Acta Geogr. Sin. 60(01), 106–114 (2005)

    Google Scholar 

  18. Liu, Y.: The spatio-temporal distribution of floating population and its development tendency in China. China Popul. Resour. Environ. 01, 139–144 (2008)

    Google Scholar 

  19. Lu, Q., Wang, G., Yang, C., et al.: Some explanations to the relationships between the geographical distribution change of migrants and economic development change in regions of China, 1990 and 2000. Geogr. Res. (05), 765–774+949 (2006)

    Google Scholar 

  20. Lu, Q., Wu, P., Lu, L., et al.: The relation between the characteristics of the migrants and the economic development in Beijing and the regional differentiation of their distribution. Acta Geogr. Sin. (05), 851–862 (2005)

    Google Scholar 

  21. Yu, T.: Spatial-temporal features and influential factors of the China urban floating population growth. Chin. J. Popul. Sci. (04), 47–58+111–112 (2012)

    Google Scholar 

  22. Liu, S., Deng, Y., Hu, Z.: Research on classification methods and spatial patterns of the regional types of China’s floating population. Acta Geogr. Sin. 65(10), 1187–1197 (2010)

    Google Scholar 

  23. Shen, J.: Changing patterns and determinants of interprovincial migration in China 1985–2000. Popul. Space Place 18(3), 384–402 (2012)

    Article  Google Scholar 

  24. Wang, Y., Li, H., Yu, Z., et al.: Approaches to census mapping: Chinese solution in 2010 rounded census. Chin. Geogra. Sci. 22(3), 356–366 (2012)

    Article  Google Scholar 

  25. Cao, G., Liu, T.: Rising role of inland regions in China’s urbanization in the 21st century: the new trend and its explanation. Acta Geogr. Sin. 66(12), 1631–1643 (2011)

    Google Scholar 

  26. Phan, D., Xiao, L., Yeh, R., et al.: Flow map layout. In: IEEE Computer Society, Los Alamitos, pp. 219–224 (2005)

    Google Scholar 

  27. Tobler, W.R.: Experiments in migration mapping by computer. Am. Cartogr. 14(2), 155–163 (1987)

    Article  Google Scholar 

  28. Guo, D., Zhu, X., Jin, H., et al.: Discovering spatial patterns in origin-destination mobility data. Trans. GIS 16(3), 411–429 (2012)

    Article  Google Scholar 

  29. Tobler, W.R.: A model of geographical movement. Geogr. Anal. 13(1), 1–20 (1981)

    Article  Google Scholar 

  30. Ullman, E.L., Dacey, M.F.: The minimum requirements approach to the urban economic base. Pap. Reg. Sci. 6(1), 175–194 (1960)

    Article  Google Scholar 

  31. Gould, P.R.: On the geographical interpretation of eigenvalues. Trans. Inst. Br. Geogr. 42, 53–86 (1967)

    Article  Google Scholar 

  32. Corcoran, J., Chhetri, P., Stimson, R.: Using circular statistics to explore the geography of the journey to work*. Pap. Reg. Sci. 88(1), 119–132 (2009)

    Article  Google Scholar 

  33. Groff, E., McEwen, T.: Exploring the spatial configuration of places related to homicide events. Institute for Law and Justice, Alexandria, VA (2006)

    Google Scholar 

  34. Andrienko, N., Andrienko, G.: Spatial generalization and aggregation of massive movement data. IEEE Trans. Visual Comput. Graphics 17(2), 205–219 (2011)

    Article  Google Scholar 

  35. Guo, D.: Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans. Visual Comput. Graphics 15(6), 1041–1048 (2009)

    Article  Google Scholar 

  36. Holten, D., Van Wijk, J.J.: Force-directed edge bundling for graph visualization. Comput. Graph. Forumz 28(3), 983–990 (2009)

    Article  Google Scholar 

  37. Verbeek, K., Buchin, K., Speckmann, B.: Flow map layout via spiral trees. IEEE Trans. Visual Comput. Graphics IEEE Trans. Visual Comput. Graphics 17(12), 2536–2544 (2011)

    Article  Google Scholar 

  38. Cui, W., Zhou, H., Qu, H., et al.: Geometry-based edge clustering for graph visualization. IEEE Trans. Visual Comput. Graphics 14(6), 1277–1284 (2008)

    Article  Google Scholar 

  39. Guo, D.: Visual analytics of spatial interaction patterns for pandemic decision support. Int. J. Geogr. Inf. Sci. 21(8), 859–877 (2007)

    Article  Google Scholar 

  40. Guo, D.S., Chen, J., MacEachren, A.M., et al.: A visualization system for space-time and multivariate patterns (VIS-STAMP). IEEE Trans. Visual Comput. Graphics 12(6), 1461–1474 (2006)

    Article  Google Scholar 

  41. Wood, J., Dykes, J., Slingsby, A.: Visualisation of origins, destinations and flows with OD maps. Cartogr. J. 47(2), 117–129 (2010)

    Article  Google Scholar 

  42. Yan, J., Thill, J.C.: Visual data mining in spatial interaction analysis with self-organizing maps. Environ. Plan. B-Plan. Des. 36(3), 466–486 (2009)

    Article  Google Scholar 

  43. Bie, W., Yi, Z., Xiao, W., et al.: Urban community structure mining based on Weibo Check-in data. Geomatics World 26(04), 68–73 (2019)

    Google Scholar 

  44. Qin, X., Zhen, F., Xiong, L.: Methods in urban temporal and spatial behavior research in the Big Data Era. Prog. Geogr. 32(09), 1352–1361 (2013)

    Google Scholar 

  45. Dan, J.: Thoughts and several suggestions on LBS development in China. Geomatics World 10(06), 22–24 (2012)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Open Topic of Hunan Key Laboratory of Land Resources Evaluation and Utilization, grant number SYS-MT-201901. We greatly appreciate the constructive comments and suggestions from reviewers.

Author information

Authors and Affiliations

  1. China University of Geosciences (Beijing), Beijing, 100083, China

    Yao Long & Tao Yuan

  2. Hunan Key Laboratory of Land Resources Evaluation and Utilization, Changsha, 410118, Hunan, China

    Lin Zhang

Authors
  1. Yao Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Yuan .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. Shenzhen University, Shenzhen, China

    Yang Yue

  4. Chinese Academy of Sciences, Beijing, China

    Zhiming Ding

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Long, Y., Zhang, L., Yuan, T. (2021). Proximity-Based Aggregation Method for LBS Human Mobility Data. In: Meng, X., Xie, X., Yue, Y., Ding, Z. (eds) Spatial Data and Intelligence. SpatialDI 2020. Lecture Notes in Computer Science(), vol 12567. Springer, Cham. https://doi.org/10.1007/978-3-030-69873-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-69873-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69872-0

  • Online ISBN: 978-3-030-69873-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics