The Spatial Analysis of Weibo Check-in Data—— The Case Study of Wuhan

  • Mingye Bao
  • Nanhai Yang
  • Liang Zhou
  • Yizhen Lao
  • Yun Zhang
  • Yangge Tian
Part of the Communications in Computer and Information Science book series (CCIS, volume 399)

Abstract

With the popularization and development of mobile phones, more and more people share their spatial locations on social network, to leave their footprints. However, Studies in the patterns of the check-in data and its relation to the existing space are not enough. Using the method of the spatial analysis of the data direction distribution and hierarchical analysis, we found that the check-in data has the close contact with the real space. It is of great value for us to deeply explore spatial characteristics and extend the usage of check-in data.

Keywords

check-in weibo patterns 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li, L., Goodchild, M.F., Xu, B.: Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartography and Geographic Information Science 40(2), 61–77 (2013)CrossRefGoogle Scholar
  2. 2.
    Lee, R., Sumiya, K.: Measuring\Geographical Regularities of Crowd Behaviors for Twitter-Based Geo- Social Event Detection. In: Proceedings of the 2nd ACMSIGSPATIAL International Workshop on Location Based Social Networks (LBSN 2010), pp. 1–10. ACM, New York (2010)CrossRefGoogle Scholar
  3. 3.
    Hollenstein, L., Purves, R.: Exploring Place Through User-Generated Content: Using Flickr to Describe City Cores. Journal of Spatial Information Science 1(1), 21–48 (2010)Google Scholar
  4. 4.
    Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: Exploring Millions of Footprints in Location Sharing Services. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM), Barcelona, pp. 81–88. AAAI Press, Palo Alto (2011)Google Scholar
  5. 5.
    Li, L., Goodchild, M.F.: Spatio-Temporal Footprints in Social Networks. In: Alhajj, R.S., Rokne, J.G. (eds.) Encyclopedia of Social Networks and Mining. Springer (2013)Google Scholar
  6. 6.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors. In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, pp. 851–860. ACM, New York (2010)CrossRefGoogle Scholar
  7. 7.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)CrossRefMATHGoogle Scholar
  8. 8.
    Soule, L.C., Shell, L.W., Kleen, B.A.: Exploring Internet Addiction: Demographic Characteristics and Stereotypes of Heavy Internet Users. Journal of Computer Information Systems 44(1), 64–73 (2003)Google Scholar
  9. 9.
    Taylor, W.J., Zhu, G.X., Dekkers, J., Marshall, S.: Socio-Economic Factors Affecting Home Internet Usage Patterns in Central Queensland. Informing Science 6, 233–246 (2003)Google Scholar
  10. 10.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: Fourth International AAAI Conference on Weblogs and Social Media, Washington, DC, May 23-26 (2010)Google Scholar
  11. 11.
    Wold, H.: Estimation of Principal Components and Related Models by Iterative Least Squares. In: Krishnaiaah, P.R. (ed.) Multivariate Analysis, pp. 391–420. Academic Press, New York (1966)Google Scholar
  12. 12.
    Wold, S., Sjöström, M., Eriksson, L.: PLS-Regression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130 (2001)CrossRefGoogle Scholar
  13. 13.
    Zandbergen, P.A.: Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning. Transactions in GIS 13(s1), 5–25 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mingye Bao
    • 1
  • Nanhai Yang
    • 1
  • Liang Zhou
    • 1
  • Yizhen Lao
    • 1
  • Yun Zhang
    • 1
  • Yangge Tian
    • 1
  1. 1.International school of softwareWuhan UniversityWuhanChina

Personalised recommendations