Yuqin Jiang
ABSTRACT
City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies' communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city network in the big social data context, this research builds a network at the county level using digital footprints from Twitter users. Retrieving geotags from Twitter users, we identify the connection strength of each pair of counties based on the amounts of shared users who leave digital footprints on both counties. Using the shared user amount as the weighted link and each county as the node, we build a county-to-county user flow network. Various network structures have been detected at the state level. In addition, by creating a direct flow chain, we can identify influential counties and its hinterland. This network demonstrates how human mobility operate across various spatial settings and distances. Results of this study can be used in transportation planning, regional planning and metropolitan management.
- Green, N. 2007. Functional polycentricity: a formal definition in terms of social network analysis. Urban Studies, 44(11), 2077--2103.Google Scholar
Cross Ref
- Taylor, P.J., 2001. Specification of the world city network. Geographical analysis, 33(2), pp. 181--194.Google Scholar
- Taylor, P.J., Catalano, G. and Walker, D.R., 2002. Exploratory analysis of the world city network. Urban Studies, 39(13), pp. 2377--2394.Google ScholarCross Ref
- O'Connor, K. 2003. Global air travel: toward concentration or dispersal?. Journal of Transport Geography, 11(2), 83--92.Google ScholarCross Ref
- Neal, Z., 2014. AIRNET: A programme for generating intercity networks. Urban Studies, 51(1), pp. 136--152.Google ScholarCross Ref
- Liu, X., Derudder, B., & Wu, K. 2016. Measuring polycentric urban development in China: An intercity transportation network perspective. Regional Studies, 50(8), 1302--1315.Google ScholarCross Ref
- Grabowicz, P.A., Ramasco, J.J., Gonçalves, B. and Eguíluz, V.M., 2014. Entangling mobility and interactions in social media. PloS one, 9(3), p.e92196.Google ScholarCross Ref
- Xu, C., Wong, D.W. and Yang, C., 2013. Evaluating the "geographical awareness" of individuals: an exploratory analysis of twitter data. Cartography and Geographic Information Science, 40(2), pp. 103--115.Google ScholarCross Ref
- Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P. and Ratti, C., 2014. Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), pp. 260--271.Google ScholarCross Ref
- Lancichinetti, A., Radicchi, F., Ramasco, J.J. and Fortunato, S., 2011. Finding statistically significant communities in networks. PloS one, 6(4), p.e18961.Google ScholarCross Ref
- Taylor, P.J., 2004. World city network: a global urban analysis. Psychology Press. Google ScholarDigital Library
- Liu, X., Derudder, B. and Taylor, P., 2014. Mapping the evolution of hierarchical and regional tendencies in the world city network, 2000--2010. Computers, environment and urban systems, 43, pp. 51--66.Google Scholar
- Vasanen, A., 2012. Functional polycentricity: examining metropolitan spatial structure through the connectivity of urban sub-centres. Urban studies, 49(16), pp. 3627--3644.Google Scholar
- Van der Laan, L., Vogelsang, J., Schalke, R., 1998. Commuting in Multi-Nodal Urban Systems: An Empirical Comparison of Three Alternative Models. Tijdschrift voor economische en sociale geografie, 89(4), pp. 384--400.Google Scholar
- Derudder, B. and Witlox, F., 2005. An appraisal of the use of airline data in assessing the world city network: a research note on data. Urban Studies, 42(13), pp. 2371--2388.Google ScholarCross Ref
- Neal, Z. (2010). Refining the air traffic approach to city networks. Urban Studies, 47(10), 2195--2215.Google ScholarCross Ref
- Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. and Mascolo, C., 2012. A tale of many cities: universal patterns in human urban mobility. PloS one, 7(5), p.e37027.Google ScholarCross Ref
- França, U., Sayama, H., McSwiggen, C., Daneshvar, R. and Bar-Yam, Y., 2016. Visualizing the "Heartbeat" of a City with Tweets. Complexity, 21(6), pp. 280--287.Google ScholarCross Ref
- Lenormand, M., Picornell, M., Cantú-Ros, O.G., Tugores, A., Louail, T., Herranz, R., Barthelemy, M., Frias-Martinez, E. and Ramasco, J.J., 2014. Cross-checking different sources of mobility information. PLoS One, 9(8), p.e105184.Google ScholarCross Ref
- Thusoo, A., Sarma, J.S., Jain, N., Shao, Z., Chakka, P., Anthony, S., Liu, H., Wyckoff, P. and Murthy, R., 2009. Hive: a warehousing solution over a map-reduce framework. Proceedings of the VLDB Endowment, 2(2), pp. 1626--1629. Google ScholarDigital Library
- Bittorf, M.K.A.B.V., Bobrovytsky, T., Erickson, C.C.A.C.J., Hecht, M.G.D., Kuff, M.J.I.J.L., Leblang, D.K.A., Robinson, N.L.I.P.H., Rus, D.R.S., Wanderman, J.R.D.T.S. and Yoder, M.M., 2015. Impala: A modern, open-source SQL engine for Hadoop. In Proceedings of the 7th Biennial Conference on Innovative Data Systems Research.Google Scholar
- Bastian, M., Heymann, S. and Jacomy, M., 2009. Gephi: an open source software for exploring and manipulating networks. Icwsm, 8, pp. 361--362.Google ScholarCross Ref
- Hecht, B.J. and Stephens, M., 2014. A Tale of Cities: Urban Biases in Volunteered Geographic Information. ICWSM, 14, pp. 197--205.Google Scholar
- Malik, M., Lamba, H., Nakos, C., & Pfeffer, J. (2015, March). Population bias in geo-tagged tweets. In ICWSM Workshop on Standards and Practices in Large-Scale Social Media Research (pp. 18--27).Google Scholar
- Jiang Y., Li Z., Ye X. (2018) Understanding Demographic and Socioeconomic Bias of Geo-tagged Twitter Users at the County Level, Cartography and Geographic Information Science, 15230406.2018.1434834Google Scholar
- Liu X., Huang Q., Li Z., Wu M., (2017), The impact of MTUP to explore online trajectories for human mobility studies. Proceedings of the 1st ACM SIGSPATIAL International Workshop on PredictGIS Google ScholarDigital Library
- United States Census Bureau. 2010 Census. U.S. Census Bureau. 2010. Web. 15 September 2018 http://www.census.gov/2010census/data.Google Scholar
- Huang, X., Zhao, Y., Yang, J., Zhang, C., Ma, C., & Ye, X. (2016). TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data. IEEE Transactions on Visualization and Computer Graphics, 22(1), 160--169.Google ScholarDigital Library
- Liu, Q., Wang, Z., & Ye, X (2018) Comparing Mobility Patterns between Residents and Visitors Using Geotagged Social Media Data. Transactions in GIS.Google ScholarCross Ref
- Ye, X., & Liu, X. (Eds.). (2018). Cities as Spatial and Social Networks. Springer.Google Scholar
Index Terms
- Measuring Inter-city Network Using Digital Footprints from Twitter Users
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