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

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Part of the book series: Communications in Computer and Information Science ((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.

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References

  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)

    Article  Google Scholar 

  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)

    Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  7. Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)

    Book  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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. Wold, S., Sjöström, M., Eriksson, L.: PLS-Regression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130 (2001)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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© 2013 Springer-Verlag Berlin Heidelberg

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Bao, M., Yang, N., Zhou, L., Lao, Y., Zhang, Y., Tian, Y. (2013). The Spatial Analysis of Weibo Check-in Data—— The Case Study of Wuhan. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2013. Communications in Computer and Information Science, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41908-9_49

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  • DOI: https://doi.org/10.1007/978-3-642-41908-9_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41907-2

  • Online ISBN: 978-3-642-41908-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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