Urban geographical patterns of the relationship between mobile communication, social networks and economic development – the case of Hungary
In the post-industrial age, the transformation of urban networks and urban regions was fundamentally influenced by the rapid spread of infocommunication technologies (ICT) and the Internet. People share information in their daily lives with the help of various ICT devices and ultimately generate georeferenced data that could obtain important information about people’s use of space, spatial movement and social connections. The main aim of the study is to explore the urban geographical and spatial impacts of ICT and social media networks in Hungarian cities. We focus on drawing territorial and settlement hierarchical patterns and clusters based on the mobile communication and online social network relationship data of Hungarian cities. The paper highlights the relationship between the intensity of mobile communication and the density and expansion of intercity social relations and the settlements’ level of economic development, respectively. The methodology is based on mobile phone call detail record (CDR) analysis and intercity network analysis of social media activities. Our findings suggest that different communication networks follow divergent spatial patterns in Hungary. The traditional East–West dichotomy of the Hungarian spatial divide is still reflected in mobile communication, but intercity clusters based on social media activities are usually aligned to the borders of administrative structures. In several cases, we were able to identify strong intercity links between settlements with a similar level of economic development of the mesolevel spatial structure that traverses over different counties and regional borders. Results on social and demographic issues suggest that ‘generation Z’ could play a key role in dampening the social and economic tensions created by the digital divide in the long run. Using a multidimensional explanatory model, we could demonstrate the growing interconnectedness between digital networks and economic development.
Ahas, R., Aasa, A., Mark, Ü., Pae, T. and Kull, T. 2006. Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tourism Management 28. (3): 898-910. https://doi.org/10.1016/j.tourman.2006.05.010
Alias, N.A. 2013. ICT Development for Social and Rural Connectedness. New York, Springer. https://doi.org/10.1007/978-1-4614-6901-8
Audirac, I. 2005. Information technology and urban form: Challenges to smart growth. International Regional Science Review 28. 119-145. https://doi.org/10.1177/0160017604273624
Bailey, M., Cao, R., Kuchler, T., Stroebel, J. and Wong, A. 2018. Social connectedness: Measurement, determinants, and effects. Journal of Economic Perspectives 32. (3): 259-280. https://doi.org/10.1257/jep.32.3.259
Becker, R.A., Caceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A. and Volinsky, C. 2011. A tale of one city: Using cellular network data for urban planning. Pervasive Computing 10. (4): 18-26. https://doi.org/10.1109/MPRV.2011.44
Blondel, V.D., Guillaume, J.-L., Lambiotte, R. and Lefebvre, E. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Blondel, V.D., Krings, G. and Thomas, I. 2010. Regions and borders of mobile telephony in Belgium and around Brussels. Brussel Studies 42. Available at https://perso.uclouvain.be/gautier.krings/docs/EN_129_BruS42EN.pdf https://doi.org/10.4000/brussels.806
Blondel, V.D., Decuyper, A. and Krings, G. 2015. A survey of results on mobile phone datasets analysis. EPJ Data Science 4. 10. https://doi.org/10.1140/epjds/s13688-015-0046-0
Blumenstock, J., Cadamuro, G. and On, R. 2015. Predicting poverty and wealth from mobile phone metadata. Science 350. (6264): 1073-1076. https://doi.org/10.1126/science.aac4420
Cairncross, F. 2001. The Death of Distance: How the Communications Revolution is Changing our Lives. Boston, Harvard Business Press.
Calabrese, F., Dahlem, D., Gerber, A., Paul, D., Xiaoji, C., Rowland, J., Rath, C. and Ratti, C. 2011a. The connected states of America: Quantifying social radii of influence. In Privacy, Security, Risk and Trust. IEEE Third International Conference on Socia Computing, 9-11. October, 2011. Boston. SocialCom'11. Conference Proceedings, 223-230. https://doi.org/10.1109/PASSAT/SocialCom.2011.247
Calabrese, F., Smoreda, Z., Blondel, V.D. and Ratti, C. 2011b. Interplay between telecommunications and face-to- face interactions: A study using mobile phone data. PLoS ONE 6. (7): e20814. https://doi.org/10.1371/journal.pone.0020814
Calabrese, F., Ferrari, L. and Blondel, V.D. 2014. Urban sensing using Mobile phone network data: A survey of research. ACM Computing Surveys 47. (2): 1-20. https://doi.org/10.1145/2655691
Ciuccarelli, P., Lupi, G. and Simeone, L. 2014. Visualizing the Data City: Social Media as a Source of Knowledge for Urban Planning and Management. Springer Science- Business Media.
Csáji, B.C., Browet, A., Traag, V.A., Delvenne, J.C., Huens, E., van Dooren, P., Smoreda, Z. and Blondel, V.D. 2012. Exploring the mobility of mobile phone users. Physica A: Statistical Mechanics and its Applications 392. (6): 1459-1473. https://doi.org/10.1016/j.physa.2012.11.040
Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E. and Tatem, A.J. 2014. Dynamic population mapping using mobile phone data. PNAS Proceedings of the National Academy of Sciences of the United States of America. 111. (45): 15888-15893. https://doi.org/10.1073/pnas.1408439111
Dusek, T., Lukács, R. and Rácz, I. 2014. Development differences among the regions of Hungary. Procedia Economics and Finance 9. 264-277. https://doi.org/10.1016/S2212-5671(14)00028-8
Eagle, N., de Montjoye, Y. and Bettencourt, L.M. 2009. Community computing: Comparisons between rural and urban societies using mobile phone data. In Procedings of the 12th Computational Science and Engineering International Conference, 29-31. August, 2009 Vancouver, CDN, CSE '09, 144-150. https://doi.org/10.1109/CSE.2009.91
Eagle, N., Macy, M. and Claxton, R 2010. Network diversity and economic development. Science 328. (5981): 1029-1031. https://doi.org/10.1126/science.1186605
EC 2014. Feasibility study on the use of mobile positioning data for tourism statistics consolidated report. Eurostat Contract No 30501.2012.001-2012.452. Available at https://ec.europa.eu/eurostat/documents/747990/6225717/MP-Consolidated-report.pdf
Egedy, T., Kovács, Z. and Szabó, B. 2018. Changing geography of the creative economy in Hungary at the beginning of the 21st century. Hungarian Geographical Bulletin 67. (3): 275-291. https://doi.org/10.15201/hungeobull.67.3.5
Firmino, R.J., Aurigi, A. and Camargo, A.R. 2006. Urban and technological developments why is it so hard to integrate ICTs into the planning agenda? Vienna, CORP 2006. Geomultimedia 06. 143-152.
Frias-Martinez, V., Soguero-Ruiz, C., Frias-Martinez, E. and Josephidou, M. 2013. Forecasting socioeconomic trends with cell phone records. Paper to the Proceedings of the 3rd ACM Symposium on Computing for Development. Article No. 15. New York, ACM. https://doi.org/10.1145/2442882.2442902
Gillespie, A. and Williams, H. 1988. Telecommunications and the reconstruction of regional comparative advantage. Environment and Planning A, 20. 1311-1321. https://doi.org/10.1068/a201311
Gonzalez, M., Hidalgo, C. and Barabási, A.-L. 2008. Understanding individual human mobility patterns. Nature 453. (7196): 779-782. https://doi.org/10.1038/nature06958
Graham, S. 1998. The end of geography or the explosion of place? Conceptualizing space, place and information technology. Progress in Human Geography 22. 165-185. https://doi.org/10.1191/030913298671334137
Graham, S. and Marvin, S. 2002. Telecommunications and the City: Electronic Spaces, Urban Places. London, Routledge.
Granovetter, M. 1985. Economic action and social structure: The problem of embeddedness. American Journal of Sociology 91. (3): 481-510. https://doi.org/10.1086/228311
Gregersen, F.A. and Lunke, E.B. 2018. The Application of Cellular Network Data for Travel Behavior Research. Oslo, Institute of Transport Economics, Norwegian Center for Transport Research.
Győri, R. and Mikle, G. 2017. A fejlettség területi különbségeinek változása Magyarországon, 1910-2011 (Transformation of regional development disparities in Hungary, 1910-2011). Tér és Társadalom 31. (3): 143-165. https://doi.org/10.17649/TET.31.3.2866
He, Y., Yu, F.R., Zhao, N., Yin, H., Yao, H. and Qiu, R.C. 2016. Big Data Analytics in Mobile Cellular Networks. IEEE Access 4. 1985-1996. https://doi.org/10.1109/ACCESS.2016.2540520
Helsley, R.W. and Zenou, Y. 2014 Social networks and interactions in cities. Journal of Economic Theory 150. 426-466. https://doi.org/10.1016/j.jet.2013.09.009
Hernandez, M., Hong, L., Frias-Martinez, V., Whitby, A. and Frias-Martinez, E. 2017. Estimating Poverty Using Cell Phone Data. Evidence from Guatemala. Policy Research Working Paper 7969, World Bank Group, Macroeconomics and Fiscal Management Global Practice Group. Available at https://openknowledge.worldbank.org/handle/10986/26136 https://doi.org/10.1596/1813-9450-7969
Iammarino, S. and McCann, P. 2013. Multinationals and Economic Geography: Location and Technology, Innovation. Cheltenham, Edward Elgar Publishing. https://doi.org/10.4337/9781781954799
Jackson, O.M. 2008. Social and Economic Networks. Princeton-Oxford, Princeton University Press.
Jakobi, Á. 2013. Space and virtuality: New characteristics of inequalities in the information society and economy. Review of Applied Socio-Economic Research 5. 4-14.
Jakobi, Á. 2017. Proximity-driven motives in the evolution of an online social network. In The Rise of Big Spatial Data. Lecture notes in geoinformation and cartography. Eds.: Ivan, I., Singleton, A., Horák, J. and Inspector, T., Springer Verlag. https://doi.org/10.1007/978-3-319-45123-7_15
Järv, O., Ahas, R. and Witlox, F. 2014. Understanding monthly variability in human activity spaces: A twelve‐month study using mobile phone call detail records. Transportation Research Part C: Emerging Technologies 38. 122-135. https://doi.org/10.1016/j.trc.2013.11.003
Kadushin, C. 2012. Understanding Social Networks - Theories, Concepts and Findings. New York, Oxford University Press.
Lambiotte, R., Blondel, V., de Kerchove, C., Huens, E., Prieur, C., Smoreda, Z. and Dororen, P.V. 2008. Geographical dispersal of mobile communication networks. Physica A: Statistical Mechanics and its Applications 387. 5317-5325. https://doi.org/10.1016/j.physa.2008.05.014
Lengyel, B., Varga, A., Ságvári, B., Jakobi, Á. and Kertész, J. 2015. Geographies of an online social network. PLoS ONE 10. (9): e0137248. https://doi.org/10.1371/journal.pone.0137248
Long-Scott, A. 1995. Access denied? - Outlook 8. (1): 1-13.
Mao, H., Shuai, X., Ahn, Y.Y. and Bollen, J. 2013. Mobile communications reveal the regional economy in Côte d'Ivoire. Mobile phone data for development - analysis of mobile phone datasets for the development of Ivory Coast. Orange D4D challenge. 4. 17-34.
McPherson, M., Smith-Lovin, L. and Cook, J.M. 2001. Birds of a feather: homophily in social networks. Annual Review Sociology 27. 415-444. https://doi.org/10.1146/annurev.soc.27.1.415
More, J. and Lingam, C. 2013. Current trends in reality mining. IRJES 22. (2): 35-39.
Nemes Nagy, J. and Tagai, G. 2011. Regional inequalities and the determination of spatial structure. Regional Statistics 1. Special Issue, 15-28.
Norbutas, L. and Corten, R. 2018. Network structure and economic prosperity in municipalities: A largescale test of social capital theory using social media data. Social Networks 52. 120-134. https://doi.org/10.1016/j.socnet.2017.06.002
Novák, J., Ahas, R., Aasa, A. and Silm, S. 2013. Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia. Journal of Maps 9. (1): 10-15. https://doi.org/10.1080/17445647.2012.762331
Onnela, J.-P., Saramäki, J., Hyvonen, J., Szabo, G., Lazer, D., Kaski, K., Kertesz, J. and Barabási, A.L. 2007. Structure and tie strengths in mobile communication networks. PNAS Proceedings of the National Academy of Sciences of the United States of America 104. (18): 7332-7336. https://doi.org/10.1073/pnas.0610245104
Pan, W., Ghoshal, G., Krumme, C., Cebrian, M. and Pentland, A. 2013. Urban characteristics attributable to density-driven tie formation. Nature Communications 4. Article Number 1961. https://doi.org/10.1038/ncomms2961
Pepper, R. and Garrity, J. 2015. ICTs, income inequality, and ensuring inclusive growth. In The Global Information Technology Report 2015. ICTs for Inclusive Growth. Geneva, CH, World Economic Forum, 31-38. https://doi.org/10.2139/ssrn.2588115
Portugali, J., Meyer, H., Stolk, E. and Tan, E. 2012. Complexity Theories of Cities have Come of Age: An Overview with Implications to Urban Planning and Design. Springer Science-Business Media. https://doi.org/10.1007/978-3-642-24544-2
Prensky, M. 2001. Digital natives, digital immigrants. Part 1. On the Horizon 9. (5): 1-6. https://doi.org/10.1108/10748120110424816
Qin, S., Man, J., Wang, X., Li, C., Dong, H. and Ge, X. 2019. Applying Big Data analytics to monitor tourist flow for the scenic area operation management. Discrete Dynamics in Nature and Society 2. 1-11. https://doi.org/10.1155/2019/8239047
Raschke, M., Schläpfer, M., Grauwin, S., Bettencourt, L., Claxton, R., Smoreda, Z., West, G. and Ratti, C. 2014. The scaling of human interactions with city size. Journal of The Royal Society Interface 11. 1742-5662. https://doi.org/10.1098/rsif.2013.0789
Rallet, A. and Rochelandet, F. 2007. ICTs and inequalities: The digital divide. In Internet and Digital Economics. Eds.: Brousseau, E. and Curien, N., Cambridge, Cambridge University Press, 693-717. https://doi.org/10.1017/CBO9780511493201.025
Richmond, K. and Triplett, R.E. 2017. ICT and income inequality: a cross-national perspective. International Review of Applied Economics 32. (2): 195-214. https://doi.org/10.1080/02692171.2017.1338677
Sassen, S. 2001. Impacts of information technologies on urban economic and politics. International Journal of Urban and Regional Research 25. (2): 411-418. https://doi.org/10.1111/1468-2427.00319
Schläpfer, M., Bettencourt, L., Grauwin, S., Raschke, M., Claxton, R., Smoreda, Z., West, G.B. and Ratti, C. 2014. The scaling of human interactions with city size. Journal of the Royal Society Interface 11:20130789 https://doi.org/10.1098/rsif.2013.0789
Smith-Clarke, C., Mashhadi, A. and Capra, L. 2014. Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems. New York, ACM, 511-520. https://doi.org/10.1145/2556288.2557358
Song, C., Qu, Z., Blumm, N. and Barabási, A.‐L. 2010. Limits of predictability in human mobility. Science 327. 1018-1021. https://doi.org/10.1126/science.1177170
Stiakakis, E., Kariotellis, P. and Vlachopoulou, M. 2010. From the digital divide to digital inequality: A secondary research in the European Union. In Next Generation Society. Technological and Legal Issues. e-Democracy 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 26. Eds.: Sideridis, A.B. and Patrikakis, C.Z., Berlin- Heidelberg, Springer, 43-54. https://doi.org/10.1007/978-3-642-11631-5_4
The Global Information Technology Report 2015. ICTs for Inclusive Growth. Geneva, CH. World Economic Forum.
Tóth, G., Wachs, J., Di Clemente, R., Jakobi, Á., Ságvári, B., Kertész, B. and Lengyel, B. 2021. Inequality is rising where social network segregation interacts with urban topology. Nature Communications 12. 1143. https://doi.org/10.1038/s41467-021-21465-0
Tranos, E., Reggiani, A. and Nijkamp, P. 2013. Accessibility of cities in the digital economy. Cities 30. 59-67. https://doi.org/10.1016/j.cities.2012.03.001
Tranos, E. and Nijkamp, P. 2013. The death of distance revisited: Cyber-place, physical and relational proximities. Journal of Regional Science 53. 855. https://doi.org/10.1111/jors.12021
Tranos, E. 2013. The Geography of the Internet: Cities, Regions and Internet Infrastructure in Europe. Cheltenham, Edward Elgar Publishing.
Wang, P., Gonzalez, M., Hidalgo, C. and Barabasi, A.-L. 2009. Understanding the spreading patterns of mobile phone viruses. Science 324. 1071-1076. https://doi.org/10.1126/science.1167053
Wiig, A. 2014. After the Smart City: Global Ambitions and Urban Policymaking in Philadelphia. Philadelphia, Temple University.
World Development Report 2016: Digital Dividends. Vienna, World Bank Group.
Copyright (c) 2021 Tamás Egedy, Bence Ságvári
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.