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Exploring the spatial distribution of geo-tagged Twitter feeds via street-centrality measures

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Abstract

The socio-spatial complexity of urban spaces is increasing with the broad reach of mobile devices and digital communication mediums. This research explores the quantifiable spatial distribution of urban activities through the lens of social media. Accordingly, two key objectives are addressed in this paper. First, testing the reliability of social media as a tool for understanding urban public activities; and second, exploring the effects of physical accessibility on the interactions of people with urban spaces. Furthermore, this study explores new methodological possibilities for reading urban space through social media and measurable accessibility. Three layers of data are used to address these objectives: a collection of geo-tagged public Twitter feeds, a geo-tagged name-generator survey, and the metric Euclidian centrality measures of the urban spatial network (closeness and betweenness). The findings show that the geo-tagged Twitter data can be a reliable tool for understanding the socio-spatial structure of urban public spaces. Results also suggest a variety of socio-spatial patterns arising from relating Twitter data to centrality measures with more emphasis on locality.

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Notes

  1. It is also referred to as Volunteered Geographic Information (VGI).

  2. NodeXL is an add-in application for Microsoft Excel that performs network analysis, for more information see Hansen et al. (2010).

  3. The hexagonal grid allows for a more natural comparison in cities without a regular grid, especially when physical proximity is a key component (See Shelton et al. 2015).

  4. Variance Inflation Factor (VIF) is defined by 1/(1 − R2), where R is the correlation coefficient between target IVs. A VIF value larger than ten is considered to have high multicollinearity, between five and ten is considered medium, and close to one is considered to be low multicollinearity (Chatterjee and Hadi 2015).

References

  • Arribas-Bel, D. 2014. Accidental, open and everywhere: Emerging data sources for the understanding of cities. Applied Geography 49: 45–53.

    Article  Google Scholar 

  • Batty, M. 2012. Building a science of cities. Cities 29: S9–S16.

    Article  Google Scholar 

  • Batty, M. 2013. The new science of cities. Cambridge: MIT Press.

    Google Scholar 

  • Bollier, D., and C.M. Firestone. 2010. The promise and peril of big data. Washington, DC: Aspen Institute, Communications and Society Program.

    Google Scholar 

  • Brenner, N., and C. Schmid. 2014. The ‘Urban Age’ in question. International Journal of Urban and Regional Research 38: 731–755.

    Article  Google Scholar 

  • Cairncross, F. 2001. The death of distance: How the communications revolution is changing our lives. Boston: Harvard Business School Press.

    Google Scholar 

  • Carmona, M. 2010. Public places, urban spaces: The dimensions of urban design. London: Routledge.

    Google Scholar 

  • Carpio, G.G. 2016. Racial projections: Cyberspace, public space, and the digital divide. Information, Communication & Society 21: 1–17.

    Google Scholar 

  • Castells, M. 1996. The information age: Economy, society and culture Volume 1: The rise of the network society. Oxford: Wiley Blackwell.

    Google Scholar 

  • Chatterjee, S., and A.S. Hadi. 2015. Regression analysis by example. New York: Wiley.

    Google Scholar 

  • Cho, E., S.A. Myers, and J. Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. 1082–1090, in ACM.

  • Crampton, J.W., M. Graham, A. Poorthuis, T. Shelton, M. Stephens, M.W. Wilson, and M. Zook. 2013. Beyond the geotag: Situating ‘big data’ and leveraging the potential of the geoweb. Cartography and Geographic Information Science 40: 130–139.

    Article  Google Scholar 

  • Crucitti, P., V. Latora, and S. Porta. 2006. Centrality measures in spatial networks of urban streets. Physical Review E 73: 036125.

    Article  Google Scholar 

  • Freeman, L.C. 1977. A set of measures of centrality based on betweenness. Sociometry 40: 35–41.

    Article  Google Scholar 

  • Freeman, L.C. 1978. Centrality in social networks conceptual clarification. Social networks 1: 215–239.

    Article  Google Scholar 

  • Gao, S., Y. Wang, Y. Gao, and Y. Liu. 2013. Understanding urban traffic-flow characteristics: A rethinking of betweenness centrality. Environment and Planning B: Planning and Design 40: 135–153.

    Article  Google Scholar 

  • Hampton, K.N., L.F. Sessions, E.J. Her, and L. Rainie. 2009. Social isolation and new technology. Pew Internet American Life Project 4: 89.

    Google Scholar 

  • Hansen, D., B. Shneiderman, and M.A. Smith. 2010. Analyzing social media networks with NodeXL: Insights from a connected world. Burlington: Morgan Kaufmann.

    Google Scholar 

  • Hawkins, D.M. 1980. Identification of outliers. New York: Springer.

    Book  Google Scholar 

  • Hillier, B. 1996. Space is the machine: A configurational theory of architecture. Cambridge: Press Syndicate.

    Google Scholar 

  • Hillier, B. 1999. Centrality as a process: Accounting for attraction inequalities in deformed grids. Urban Design International 4: 107–127.

    Article  Google Scholar 

  • Hillier, B. 2009. Spatial sustainability in cities organic patterns and sustainable forms. Stockholm: Royal Institute of Technology (KTH).

    Google Scholar 

  • Hillier, B., and J. Hanson. 1984. The social logic of space. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Hillier, B., S. Iida. 2005. Network and psychological effects in urban movement. International Conference on Spatial Information Theory, 2005. 475–490, in Springer.

  • Hillier, B., and V. Netto. 2002. Society seen through the prism of space: Outline of a theory of society and space. Urban Design International 7: 181–203.

    Article  Google Scholar 

  • Hillier, B., A. Penn, J. Hanson, T. Grajewski, and J. Xu. 1993. Natural movement: Or, configuration and attraction in urban pedestrian movement. Environment and Planning B: planning and design 20: 29–66.

    Article  Google Scholar 

  • Hollenstein, L., and R. Purves. 2010. Exploring place through user-generated content: Using Flickr tags to describe city cores. Journal of Spatial Information Science 2010: 21–48.

    Google Scholar 

  • Irwin, M.D., and H.L. Hughes. 1992. Centrality and the structure of urban interaction: Measures, concepts, and applications. Social Forces 71: 17–51.

    Article  Google Scholar 

  • Jacobs, J. 1961. The death and life of great American Cities. New York: Vintage Books, Random hous.

    Google Scholar 

  • Jiang, B. 2009. Ranking spaces for predicting human movement in an urban environment. International Journal of Geographical Information Science 23: 823–837.

    Article  Google Scholar 

  • Jiang, B., and C. Claramunt. 2004. A structural approach to the model generalization of an urban street network. GeoInformatica 8: 157–171.

    Article  Google Scholar 

  • Kim, H.J., B.K. Chae, and S.B. Park. 2018. Exploring public space through social media: An exploratory case study on the High Line New York City. Urban Design International 23: 69–85.

    Article  Google Scholar 

  • Koschinsky, J., E. Talen, M. Alfonzo, and S. Lee. 2017. How walkable is Walker’s paradise? Environment and Planning B: Urban Analytics and City Science 44: 343–363.

    Google Scholar 

  • Kotzias, D., T. Lappas, D. Gunopulos. 2014. Addressing the Sparsity of Location Information on Twitter. EDBT/ICDT Workshops. 339–346.

  • Kotzias, D., T. Lappas, and D. Gunopulos. 2015. Home is where your friends are: Utilizing the social graph to locate twitter users in a city. Information Systems. 57: 77–87.

    Article  Google Scholar 

  • Liao, T., and L. Humphreys. 2015. Layar-ed places: Using mobile augmented reality to tactically reengage, reproduce, and reappropriate public space. New Media & Society 17: 1418–1435.

    Article  Google Scholar 

  • Lim, M. 2014. Seeing spatially: People, networks and movements in digital and urban spaces. International Development Planning Review 36: 51–72.

    Article  Google Scholar 

  • Longley, P.A., and M. Adnan. 2016. Geo-temporal Twitter demographics. International Journal of Geographical Information Science 30: 369–389.

    Article  Google Scholar 

  • Malecki, E.J. 2014. Connecting the fragments: Looking at the connected city in 2050. Applied Geography 49: 12–17.

    Article  Google Scholar 

  • Mehaffy, M., S. Porta, Y. Rofe, and N. Salingaros. 2010. Urban nuclei and the geometry of streets: The ‘emergent neighborhoods’ model. Urban Design International 15: 22–46.

    Article  Google Scholar 

  • Morales, A., J. Borondo, J.C. Losada, and R.M. Benito. 2015. Measuring political polarization: Twitter shows the two sides of Venezuela. Chaos: An Interdisciplinary Journal of Nonlinear Science 25: 033114.

    Article  Google Scholar 

  • Moreno Pires, S., L. Magee, and M. Holden. 2017. Learning from community indicators movements: Towards a citizen-powered urban data revolution, 2399654417691512. Environment and Planning C: Politics and Space.

    Google Scholar 

  • Penn, A., and K. Al Sayed. 2017. Spatial information models as the backbone of smart infrastructure. Environment and Planning B: Urban Analytics and City Science 44: 197–203.

    Google Scholar 

  • Porta, S., P. Crucitti, and V. Latora. 2006. The network analysis of urban streets: A primal approach. Environment and Planning B: planning and design 33: 705–725.

    Article  Google Scholar 

  • Porta, S., P. Crucitti, and V. Latora. 2008. Multiple centrality assessment in Parma: A network analysis of paths and open spaces. Urban design International 13: 41–50.

    Article  Google Scholar 

  • Rainie, L., and B. Wellman. 2012. Networked: The new social operating system. Cambridge: MIT Press.

    Google Scholar 

  • Ratti, C., D. Frenchman, R.M. Pulselli, and S. Williams. 2006. Mobile landscapes: Using location data from cell phones for urban analysis. Environment and Planning B: Planning and Design 33: 727–748.

    Article  Google Scholar 

  • Ruths, D., and J. Pfeffer. 2014. Social media for large studies of Behavior. Science 346: 1063–1064.

    Article  Google Scholar 

  • Rutten, R., H. Westlund, and F. Boekema. 2010. The spatial dimension of social capital. European Planning Studies 18: 863–871.

    Article  Google Scholar 

  • Scellato, S., A. Cardillo, V. Latora, and S. Porta. 2006. The backbone of a city. The European Physical Journal B-Condensed Matter and Complex Systems 50: 221–225.

    Article  Google Scholar 

  • Seresinhe, C.I., H.S. Moat, and T. Preis. 2017. Quantifying scenic areas using crowdsourced data, 0265813516687302. Environment and Planning B: Urban Analytics and City Science.

    Google Scholar 

  • Shelton, T., A. Poorthuis, and M. Zook. 2015. Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landscape and Urban Planning 142: 198–211.

    Article  Google Scholar 

  • Stevens, J.P. 2012. Applied multivariate statistics for the social sciences. London: Routledge.

    Google Scholar 

  • Tobler, W.R. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46: 234–240.

    Article  Google Scholar 

  • Wang, H., A. Chin, H. Wang. 2011. Interplay between social selection and social influence on physical proximity in friendship formation. SRS 2011 workshop.

  • Wasserman, S., and K. Faust. 1994. Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ye, Y., A. Yeh, Y. Zhuang, A. Van Nes, and J. Liu. 2017. “Form Syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design. Urban Design International 22: 73–90.

    Article  Google Scholar 

  • Yuan, Y., M. Raubal, and Y. Liu. 2012. Correlating mobile phone usage and travel behavior—A case study of Harbin, China. Computers, Environment and Urban Systems 36: 118–130.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Architecture, Eastern Mediterranean University, Famagusta, Mersin 10, Turkey

    Aminreza Iranmanesh & Resmiye Alpar Atun

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  1. Aminreza Iranmanesh
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  2. Resmiye Alpar Atun
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Correspondence to Aminreza Iranmanesh.

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Iranmanesh, A., Atun, R.A. Exploring the spatial distribution of geo-tagged Twitter feeds via street-centrality measures. Urban Des Int 23, 293–306 (2018). https://doi.org/10.1057/s41289-018-0073-0

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