Applied Spatial Analysis and Policy

, Volume 8, Issue 3, pp 231–247 | Cite as

Cyber Cities: Social Media as a Tool for Understanding Cities

  • Daniel Arribas-Bel
  • Karima Kourtit
  • Peter Nijkamp
  • John Steenbruggen


‘Big’ urban data are increasingly becoming accessible for scientific research and policy use. They may enhance the intelligence that is needed for understanding and mapping out social connectivity phenomena (in the sense of Jane Jacobs) in modern smart cities. The present paper aims to highlight and demonstrate the rich potential of information based on digital technology in modern cities. As a case study example of the power of social media data as a support tool in smart cities, we consider Twitter data in the municipality of Amsterdam. We use machine learning techniques to identify temporal patterns that we then relate back to their spatial dimension, effectively connecting the digital with physical aspect of cities. We also show that analysis of geo-referenced tweets can shed significant light on physical aspects of the city and on the spatial distribution of urban functions.


Social buzz Digital technology Twitter ICT New Urban World’ Smart city Internet geography 



The authors wish to thank Euro Beinat and his team at the University of Salzburg for his great help in the data collection for this paper. Comments and suggestions from anonymous referees substantially improved the paper and we are very grateful to them.


  1. Abbott, A. (1997). Of time and space: the contemporary relevance of the Chicago School. Social Forces, 75, 1149–1182.CrossRefGoogle Scholar
  2. Abdoullaev, A. (2011). A Smart World: A Development Model for Intelligent Cities, Keynote Address 11th IEEE International Conference on Computer and Information Technology (CIT-2011) (
  3. Arribas-Bel, D., Kourtit, K., & Nijkamp, P. (2013). Benchmarking of world cities through self-organizing maps. Cities, 31, 248–257.CrossRefGoogle Scholar
  4. Batty, M. (2012). Building a science of cities. Cities, 29, S9–S16.CrossRefGoogle Scholar
  5. Beinat, E., Bannink, I., Oldani, G., Sagl, G., & Steenbruggen, J. (2011), A Review of Emerging Technologies for Crisis Management: Social Media, Internet of Things and Big Data. University of Salzburg - Center of Geoinformatics and Dutch Ministry of Infrastructure and Environment,
  6. Blacksher, E., & Lovasi, G. (2011). Place-focused physical activity research, human agency, and social justice in public health: taking agency seriously in studies of the built environment. Health & Place. doi: 10.1016/j.healthplace.2011.018.019.Google Scholar
  7. Boschma, R. (2005). Proximity and innovation: a critical assessment. Regional Studies, 39, 61–74.CrossRefGoogle Scholar
  8. Bottero, W., & Crossley, N. (2011). Worlds, fields and networks: Becker, Bourdieu and the structures of social structures. Cultural Sociology, 5(1), 99–119.CrossRefGoogle Scholar
  9. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York, Westport, CT and London: Greenwich Press.Google Scholar
  10. Boyd, D., & Crawford, K. (2012). Critical questions for big data. Information Communication & Society, 15(15), 662–679.CrossRefGoogle Scholar
  11. Caragliu, A., Del Bo, C., & Nijkamp, P. (2011). Smart cities in Europe. Journal of Urban Technology, 18(2), 65–82.CrossRefGoogle Scholar
  12. Cohen-Blankshtain, G., & Nijkamp, P. (2013). The importance of ICT for cities: e-governance and cyber perceptions. In J. Klaesson, B. Johansson, & C. Karlsson (Eds.), Metropolitan regions (pp. 295–308). Berlin: Springer.Google Scholar
  13. Del Bimbo, A., Ferracani, A., Pezzatini, D., D’Amato, F., & Sereni, M. (2014, April). LiveCities: Revealing the pulse of cities by location-based social networks venues and users analysis, In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion (pp. 163–166). International World Wide Web Conferences Steering Committee.Google Scholar
  14. Eagle, N., Pentland, A., & Lazer, D. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106, 15274–15278.CrossRefGoogle Scholar
  15. Ferrari, L., Rosi, A., Mamei, M., & Zambonelli, F. (2011, November). Extracting urban patterns from location-based social networks. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (pp. 9–16). ACM.Google Scholar
  16. Fleming, M. A., & Petty, R. E. (2000). Identity and persuasion: An elaboration likelihood approach. In D. J. Terry & M. A. Hogg (Eds.), Attitudes, behavior, and social context: The role of norms and group membership (pp. 171–199). Mahwah: Lawrence Erlbaum.Google Scholar
  17. Frank, M. R., Mitchell, L., Dodds, P. S., & Danforth, C. M. (2013). Happiness and the patterns of life: a study of geolocated tweets. Scientific Reports, 3, 2625, 1–9.Google Scholar
  18. Glaeser, E. (2012). Triumph of the city. New York: Penguin Press.Google Scholar
  19. Grosveld, H. (2002). The Leading Cities of the World and their Competitive Advantages. The Perception of ‘Citymakers’, Ph.D. Dissertation. Amsterdam: University of Amsterdam.Google Scholar
  20. Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside: University of California.Google Scholar
  21. Hipp, J. R., & Perrin, A. J. (2009). The simultaneous effect of social distance and physical distance on the formation of neighborhood ties. City & Community, 8(1), 5–25.CrossRefGoogle Scholar
  22. Hollands, R. G. (2008). Will the real smart city please stand up? City, 12(3), 303–320.CrossRefGoogle Scholar
  23. Ioannides, Y., & Zabel, J. E. (2008). Interactions, neighborhood selection and housing demand. Journal of Urban Economics, 63(1), 229–252.CrossRefGoogle Scholar
  24. Jacobs, J. (1969). The death and life of great American cities. New York: Vintage Books.Google Scholar
  25. Kling, F., & Pozdnoukhov, A. (2012, November). When a city tells a story: urban topic analysis. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (pp. 482–485). ACM.Google Scholar
  26. Kohonen, T. (2001). Self-organizing maps (3rd ed.). Berlin: Springer.CrossRefGoogle Scholar
  27. Komninos, N. (2002). Intelligent cities: Innovation, knowledge systems and digital spaces. London and New York: Routledge.Google Scholar
  28. Kourtit, K. (2014a). Competitiveness in urban systems - studies on the ‘Urban Century’. Amsterdam: VU University.Google Scholar
  29. Kourtit, K. (2014b). The ‘New Urban World’ - economic-geographical studies on the performance of urban systems. Poznan: Adam Mickiewicz University.Google Scholar
  30. Kourtit, K. (2014c). Planet of cities, by Shlomo Angel. 2012. Cambridge, MA: Lincoln Institute of Land Policy. Journal of Regional Science, 54, 161–162.CrossRefGoogle Scholar
  31. Kourtit, K., & Nijkamp, P. (2013). In praise of megacities in a global world. Regional Science Policy and Practice, 5, 167–182.CrossRefGoogle Scholar
  32. Kourtit, K., Nijkamp, P., Lowik, S., van Vught, F., & Vulto, P. (2011). From islands of innovation to creative hotspots. Regional Science Policy and Practice, 3(3), 145–161.CrossRefGoogle Scholar
  33. Kourtit, K., Deakin, M., Caragliu, A., De Bo, C., Nijkamp, P., Lombardi, P., & Giordano, S. (2013). An advanced triple helix network framework for smart cities performance. In M. Deakin (Ed.), Smart cities (pp. 196–216). London: Routledge.Google Scholar
  34. Krysan, M., & Bader, M. (2009). Racial blind spots: black-white-Latino differences in community knowledge. Social Problems, 56(4), 677–701.CrossRefGoogle Scholar
  35. Leamer, E. E., & Storper, M. (2001). The economic geography of the internet age. Journal of International Business Studies, 32(4), 641–666.CrossRefGoogle Scholar
  36. Lewis, P. M. (2008). Promoting Social Cohesion, The Role of Community Media, Media and Information Society Division, Directorate General of Human Rights and Legal Affairs, Council of Europe:
  37. Lovelace, R., Malleson, N., Harland, K., & Birkin, M. (2014). Geotagged tweets to inform a spatial interaction model: a case study of museums, arXiv preprint arXiv:1403.5118.Google Scholar
  38. Malecki, E. J. (2002). The economic geography of the internet’s infrastructure. Economic Geography, 78, 399–424.CrossRefGoogle Scholar
  39. Massey, D. S. (1981). Social class and ethnic segregation: a reconsideration of methods and conclusions. American Sociological Review, 46, 641–650.CrossRefGoogle Scholar
  40. Mitchell, L., Frank, M. R., Harris, K. D., Dodds, P. S., & Danforth, C. M. (2013). The geography of happiness: connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PloS one, 8(5), e64417.CrossRefGoogle Scholar
  41. Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., & Vespignani, A. (2013). The twitter of Babel: mapping world languages through microblogging platforms. PloS one, 8(4), e61981.CrossRefGoogle Scholar
  42. Modica, M., Reggiani, A., & Nijkamp, P. (2013). Are Gibrat and Zipf monozygotic or heterozygotic twins? A comparative analysis of means and variances in complex urban systems, Paper European Regional Science Association Conference, Palermo, August, 2013.Google Scholar
  43. Mouw, T., & Entwisle, B. (2006). Residential segregation and interracial friendship in schools. American Journal of Sociology, 112(2), 394–441.CrossRefGoogle Scholar
  44. Neal, Z. (2013). The connected city: How networks are shaping the modern metropolis. New York: Routledge.Google Scholar
  45. Nijkamp, P. (2013). The universal law of gravitation and the death of distance. Romanian Journal of Regional Science, 7(3), 1–10.Google Scholar
  46. Nijkamp, P., & Kourtit, K. (2011). Urban europe scoping document, joint programming initiative, urban europe. Amsterdam: VU University.Google Scholar
  47. Nijkamp, P., & Kourtit, K. (2012). The ‘New Urban Europe’: global challenges and local responses in the urban century. European Planning Studies, 21(3), 291–315.CrossRefGoogle Scholar
  48. Ozdikis, O., Oguztuzun, H., & Karagoz, P. (2013, November). Evidential location estimation for events detected in Twitter, In Proceedings of the 7th Workshop on Geographic Information Retrieval (pp. 9–16). ACM.Google Scholar
  49. Pattison, P., & Robins, G. L. (2004). Building models for social space: neighbourhood based models for social networks and affiliation structures. Mathematiques des Science Humaines, 168, 11–29.Google Scholar
  50. Pentland, A. (2009). Reality mining of mobile communications. In S. Dutta & I. Mia (Eds.), Mobility in a networked world (pp. 75–80). Paris: World Economic Forum/INSEAD.Google Scholar
  51. Raento, M., Oulasvirta, A., & Eagle, N. (2009). Smartphones: an emerging tool for social scientists. Sociological Methods and Research, 37, 426–454.CrossRefGoogle Scholar
  52. Ratcliffe, P., & Newman, I. (Eds.). (2011). Promoting social cohesion: Implications for policy and evaluation. Bristol: Policy Press.Google Scholar
  53. Sadler, S. (2005). Archigram: Architecture without architecture. Cambridge: MIT Press.Google Scholar
  54. Steenbruggen, J., Tranos, E., & Nijkamp, P. (2014). Data from mobile phone operators: a tool for smarter cities?. Telecommunications Policy, 39, 335–346.Google Scholar
  55. Storper, M., & Venables, A. J. (2004). Buzz: face-to-face contact and the urban economy. Journal of Economic Geography, 4, 351–370.CrossRefGoogle Scholar
  56. Tobler, W. (1970). A computer movie simulating urban growth in the Detroit Region. Economic Geography, 46(2), 234–240.CrossRefGoogle Scholar
  57. Tranos, E. (2013). The geography of the internet: Cities, regions and internet infrastructure in Europe. Cheltenham: Edward Elgar.CrossRefGoogle Scholar
  58. Tranos, E., & Nijkamp, P. (2013). The death of distance revisited: cyberplace, physical and relational proximities. Journal of Regional Science, 53, 855–873.CrossRefGoogle Scholar
  59. Verdery, A. M., Entwisle, B., Faust, K., & Rindfuss, R. R. (2012). Social and spatial networks: kinship distance and dwelling unit proximity in rural Thailand. Social Networks, 34, 112–127.CrossRefGoogle Scholar
  60. Warren Liao, T. (2005). Clustering of time series data—a survey. Pattern Recognition, 38(11), 1857–1874.CrossRefGoogle Scholar
  61. Zipf, G. (1949). Human behaviour and the principle of least effort. Reading: Addison-Wesley.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Daniel Arribas-Bel
    • 1
  • Karima Kourtit
    • 2
    • 3
  • Peter Nijkamp
    • 3
    • 4
  • John Steenbruggen
    • 4
  1. 1.School of Geography, Earth and Environmental SciencesUniversity of BirminghamBirminghamUK
  2. 2.Department of Urban Planning and Environment, School of Architecture and Built EnvironmentKTH Royal Institute of TechnologyStockholmSweden
  3. 3.A. Mickiewicz UniversityPoznanPoland
  4. 4.Department of Spatial EconomicsVU University AmsterdamAmsterdamThe Netherlands

Personalised recommendations