Exploring the heterogeneity of human urban movements using geo-tagged tweets

References

• Azmandian, M., et al., 2013. Following human mobility using tweets. In: L. Cao, et al., eds. Agents and data mining interaction. 2012. Berlin: Springer, 139–149.
   Google Scholar
• Barabási, A.L., 2005. The origin of bursts and heavy tails in human dynamics. Nature, 435, 207–211. doi:10.1038/nature03459
   PubMed Web of Science ®Google Scholar
• Batty, M., 2008. The size, scale, and shape of cities. Science, 319, 769. doi:10.1126/science.1151419
   PubMed Web of Science ®Google Scholar
• Bettencourt, L., et al., 2007. Growth, innovation, scaling and the pace of life in cities. PNAS, 104 (17), 7301. doi:10.1073/pnas.0610172104
   PubMed Web of Science ®Google Scholar
• Bettencourt, L., 2013. The origins of scalling in cities. Science, 340, 1438. doi:10.1126/science.1235823
   PubMed Web of Science ®Google Scholar
• Blumenfeld-Lieberthal, E. and Portugali, J., 2010. Network cities: a complexity-network approach to urban dynamics and development. In: B. Jiang and X. Yao, eds. Geospatial analysis of urban structure and dynamics. Berlin: Springer, 77–90.
   Google Scholar
• Brockmann, D., Hufnage, L., and Geisel, T., 2006. The scaling laws of human travel. Nature, 439, 462–465. doi:10.1038/nature04292
   PubMed Web of Science ®Google Scholar
• Carvalho, R. and Penn, A., 2004. Scaling and universality in the micro-structure of urban space. Physica A, 332, 539. doi:10.1016/j.physa.2003.10.024
   Web of Science ®Google Scholar
• Clauset, A., Shalizi, C.R., and Newman, M.E.J., 2009. Power-law distributions in empirical data. SIAM Review, 51, 661–703. doi:10.1137/070710111
   Web of Science ®Google Scholar
• Dalton, R.C., 2003. The secret is to follow your nose: route path selection and angularity. Environment and Behavior, 35 (1), 107–131. doi:10.1177/0013916502238867
   Web of Science ®Google Scholar
• Freeman, L.C., 1979. Centrality in social networks: conceptual clarification. Social Networks, 1, 215–239. doi:10.1016/0378-8733(78)90021-7
   Web of Science ®Google Scholar
• Gao, S., et al. 2014. Detecting origin-destination mobility flows from geotagged tweets in greater Los Angeles area. In: Proceedings of the eighth international conference on geographic information science. Vienna, Austria, 1–4.
   Google Scholar
• Goldstone, R.L. and Gureckis, T.M., 2009. Collective behavior. Topics in Cognitive Science, 1 (3), 412–438. doi:10.1111/tops.2009.1.issue-3
   PubMed Web of Science ®Google Scholar
• Gonzalez, M., Hidalgo, C.A., and Barabási, A.-L., 2008. Understanding individual human mobility patterns. Nature, 453, 779–782. doi:10.1038/nature06958
   PubMed Web of Science ®Google Scholar
• Han, S.Y., Tsou, M.-H., and Clarke, K.C., 2017. Revisiting the death of geography in the era of big data: the friction of distance in cyberspace and real space. International Journal of Digital Earth, 11, 1–19
   Web of Science ®Google Scholar
• Hawelka, B., et al., 2014. Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41, 260–271. doi:10.1080/15230406.2014.890072
   PubMed Web of Science ®Google Scholar
• Hillier, B. and Iida, S., 2005. Network and psychological effects in urban movement. In: A.G. Cohn and D.M. Mark, eds. Proceedings of the International Conference on Spatial Information Theory, COSIT 2005, 14-18 September. Ellicottville, NY, LNCS 3693, 475–490.
   Google Scholar
• Hillier, B., 2012. Studying cities to learn about minds: some possible implications of space syntax for spatial cognition. Environment and Planning B: Planning and Design, 39 (1), 12–32. doi:10.1068/b34047t
   Web of Science ®Google Scholar
• Jacobs, J., 1961. The death and life of Great American cities. New York: Random House.
   Google Scholar
• Jayasinghe, A., Sano, K., and Nishiuchi, H., 2015. Explaining traffic flow patterns using centrality measures. International Journal for Traffic and Transport Engineering, 5 (2), 134–149. doi:10.7708/2217-544X
   Google Scholar
• Jia, T., et al., 2012. An empirical study on human mobility and its agent-based modeling. Journal of Statistical Mechanics: Theory and Experiment, 2012 (11), P11024. doi:10.1088/1742-5468/2012/11/P11024
   Google Scholar
• Jiang, B., 2013. Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution. The Professional Geographer, 65 (3), 482–494. doi:10.1080/00330124.2012.700499
   Web of Science ®Google Scholar
• Jiang, B., 2017. A topological representation for taking cities as a coherent whole. Geographical Analysis, 50 (3), 298–313. doi:10.1111/gean.12145
   Web of Science ®Google Scholar
• Jiang, B. and Yin, J., 2014. Ht-index for quantifying the fractal or scaling structure of geographic features. Annals of the Association of American Geographers, 104 (3), 530–541. doi:10.1080/00045608.2013.834239
   Web of Science ®Google Scholar
• Jiang, B., Yin, J., and Zhao, S., 2009. Characterizing human mobility patterns in a large street network. Physical Review E, 80, 021136. doi:10.1103/PhysRevE.80.021136
   Web of Science ®Google Scholar
• Jiang, B., Zhao, S., and Yin, J., 2008. Self-organized natural roads for predicting traffic flow: A sensitivity study. Journal of Statistical Mechanics: Theory and Experiment, P07008.
   Google Scholar
• Jurdak, R., et al., 2015. Understanding human mobility from Twitter. PloS One, 10 (7), e0131469. doi:10.1371/journal.pone.0131469
   PubMed Web of Science ®Google Scholar
• Ma, D., et al., 2018. Why topology matters in predicting human activities? Environment and Planning B: Planning and Design, 46 (7), 1297–1313.
   Google Scholar
• Mayer-Schonberger, V. and Cukier, K., 2013. Big data: a revolution that will transform how we live, work, and think. New York: Eamon Dolan/Houghton Mifflin Harcourt.
   Google Scholar
• Newman, M.E.J., 2005. Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46 (5), 323–351. doi:10.1080/00107510500052444
   Web of Science ®Google Scholar
• Noh, J.D. and Rieger, H., 2004. Random walks on complex networks. Physical Review Letters, 92 (11), 118701. doi:10.1103/PhysRevLett.92.118701
   PubMed Web of Science ®Google Scholar
• Oki, T., 2018. Possibility of using tweets to detect crowd congestion: a case study using tweets just before/after the Great East Japan Earthquake. In: Proceedings of 15th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2018). Rochester, NY, 584–596.
   Google Scholar
• Omer, I. and Jiang, B., 2015. Can cognitive inferences be made from aggregate urban flow data? Computers, Environment and Urban Systems, 54, 219–229. doi:10.1016/j.compenvurbsys.2015.08.005
   Web of Science ®Google Scholar
• Osaragi, T., Kishimoto, M., and Oki, T., 2018. Assessment of street network accessibility in Tokyo metropolitan area after a large earthquake. Journal of Disaster Research, 13 (2), 262–271. doi:10.20965/jdr.2018.p0262
   Web of Science ®Google Scholar
• Page, L. and Brin, S., 1998. The anatomy of a large-scale hypertextual Web search engine. In: Proceedings of the Seventh International Conference on World Wide Web. Brisbane, Australia, 107–117.
   Google Scholar
• Rhee, I., et al., 2011. On the levy-walk nature of human mobility. IEEE/ACM Transactions on Networking, 19 (3), 630–643. doi:10.1109/TNET.2011.2120618
   Web of Science ®Google Scholar
• Serra, M. and Hillier, B., 2018. Angular and metric distance in road network analysis: a nationwide correlation study. Computers, Environment and Urban Systems. doi:10.1016/j.compenvurbsys.2018.11.003
   Web of Science ®Google Scholar
• Shen, Y., et al., 2019. Physical co-presence intensity: measuring dynamic face-to-face interaction potential in public space using social media check-in records. PloS One, 14 (2), e0212004. doi:10.1371/journal.pone.0212004
   PubMed Web of Science ®Google Scholar
• Song, C., et al., 2010. Modelling the scaling properties of human mobility. Nature Physics, 6, 818–823. doi:10.1038/nphys1760
   Web of Science ®Google Scholar
• Stephen, L. and Traunmueller, M., 2017. Off the shortest path: betweenness on street network level to study pedestrian movement. In: Proceedings of 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 971–976. doi:10.1177/1753193417718417
   Google Scholar
• Stroock, D.W., 2005. An introduction to Markov processes. Heidelberg, Germany: Springer-Verlag.
   Google Scholar
• Wu, L., et al., 2014. Intra-urban human mobility and activity transition: evidence from social media check-in data. PloS One, 9 (5), e97010. doi:10.1371/journal.pone.0097010
   PubMed Web of Science ®Google Scholar