skip to main content
research-article
Open Access

Network Analysis of Internal Migration in Austria

Published:11 July 2021Publication History
Skip Abstract Section

Abstract

Human migration, and urbanization as its direct consequence, are among the crucial topics in regional and national governance. People’s migration and mobility flows make a network structure, with large cities acting as hubs and smaller settlements as spokes. The essential method by which these phenomena can be analyzed comprehensively is network analysis. With this study, we first contribute to capacity building regarding the analysis of internal (national) migration data by providing a set of network indicators, models, and visualizations tested and argued for in terms of applicability and interpretability for analyzing migration. Second, we contribute to the understanding of the shape and scale of the phenomenon of internal migration, particularly toward urbanization and mobility flows between human settlements (i.e., cities, towns, and villages). Third, we demonstrate the utility of our approach on the example of internal migration flows in Austria on the settlement level and provide a longitudinal analysis for the period from 2002 to 2018. To the best of our knowledge, this is the first time that the key traits of a network of internal migration are identified for a European country, which, when accompanied by additional country analyses, has the potential to reveal the migration patterns in the region and beyond.

References

  1. N. Reslow. 2019. EU External migration policy: Taking stock and looking forward. Global Affairs 5, 3 (2019), 273–278. DOI:https://doi.org/10.1080/23340460.2019.1604071Google ScholarGoogle Scholar
  2. United Nations. 2019. SDG Indicators. Retrieved October 8, 2020 from https://unstats.un.org/sdgs/indicators/indicators-list/.Google ScholarGoogle Scholar
  3. N. Piper. 2017. Migration and the SDGs. Global Social Policy 17 (2017), 231–238.DOI:DOI:https://doi.org/10.1177/1468018117703443Google ScholarGoogle Scholar
  4. United Nations General Assembly. 2016. Resolution Adopted by the General Assembly on 19 September 2016. Retrieved October 8, 2020 from https://www.un.org/en/ga/search/view_doc.asp?symbol=A/RES/71/1.Google ScholarGoogle Scholar
  5. E Ordaz. 2019. The SDGs indicators: A challenging task for the international statistical. Global Policy 10, S1 (2019), 141–143. DOI:DOI:https://doi.org/10.1111/1758-5899.12631Google ScholarGoogle Scholar
  6. F. Laczko. 2016. Improving Data on International Migration and Development: Towards a Global Action Plan. Retrieved October 8, 2020 from https://gmdac.iom.int/sites/default/files/papers/Improving%20Data%20on%20International%20Migration%20and%20Development-%20Towards%20a%20Global%20Action%20Plan%3F.pdf.Google ScholarGoogle Scholar
  7. H. De Haas. 2014. Human Migration: Myths, Hysteria and Facts. Retrieved October 8, 2020 from DOI:https://heindehaas.files.wordpress.com/2016/02/de-haas-2013-wolfson-human-migration-myths-hysteria-and-facts.pdf.Google ScholarGoogle Scholar
  8. UNDP. 2009. Human Development Report 2009—Overcoming Barriers: Human Mobility and Development. Retrieved October 8, 2020 from DOI:http://hdr.undp.org/sites/default/files/reports/269/hdr_2009_en_complete.pdf.Google ScholarGoogle Scholar
  9. A. Otoiu, E. Titan, and R. Dumitrescu. 2014. Internal and international migration: Is a dichotomous approach justified?Procedia Social and Behavioral Sciences 109 (2014), 1011–1015. DOI:DOI:https://doi.org/10.1016/j.sbspro.2013.12.581Google ScholarGoogle Scholar
  10. M. Bell and S. Muhidin. 2009. Cross-National Comparisons of Internal Migration. Retrieved October 8, 2020 from DOI:http://hdr.undp.org/sites/default/files/hdrp_2009_30.pdf.Google ScholarGoogle Scholar
  11. Global Migration Data Analysis Centre. 2020. Types of Migration. Retrieved October 8, 2020 from DOI:https://migrationdataportal.org/?i=stock_abs_&t=2019.Google ScholarGoogle Scholar
  12. R. Skeldon. 2018. MRS No. 53—International Migration, Internal Migration, Mobility and Urbanization: Towards More Integrated Approaches. IOM Migration Research Series. United Nations, New York, NY.Google ScholarGoogle Scholar
  13. M. Van Ostaijen. 2016. Between migration and mobility discourses: The performative potential within “intra-European movement.”Critical Policy Studies 11 (2016), 166–190. DOI:DOI:https://doi.org/10.1080/19460171.2015.1102751Google ScholarGoogle Scholar
  14. European Commission. 2019. Urban Agenda for the EU: Multi-Level Governance in Action. Retrieved October 8, 2020 from DOI:https://ec.europa.eu/futurium/en/urban-agenda.Google ScholarGoogle Scholar
  15. C. Özden, C. R. Parsons, M. Schiff, and T. L. Walmsley. 2011. Where on Earth Is Everybody? Policy Research Working Paper No. 5709.World Bank, DC.Google ScholarGoogle Scholar
  16. D. Pitoski, T. J. Lampoltshammer, and P. Parycek. 2021. Human migration as a complex network: Appropriate abstraction, and the feasibility of Network Science tools. In Data Science—Analytics and Applications, P. Haber, T. Lampoltshammer, M. Mayr, and K. Plankensteiner (Eds.). Springer, 113–120. DOI:DOI:https://doi.org/10.1007/978-3-658-32182-6_17Google ScholarGoogle Scholar
  17. European Commission. 2018. Building a European Data Economy Policy. Retrieved October 8, 2020 from https://ec.europa.eu/digital-single-market/en/policies/75979/3489.Google ScholarGoogle Scholar
  18. Statistics Austria. 2020. Statistics Austria Open Data Catalogue. Retrieved October 8, 2020 from https://data.statistik.gv.at/web/meta.jsp?dataset=OGDEXT_BINNENWAND_1.Google ScholarGoogle Scholar
  19. R. Molontay and M. Nagy. 2019. Two decades of network science—As seen through the co-authorship network of network scientists. arXiv:1908.08478 [cs.SI]. DOI:DOI:https://doi.org/10.1145/3341161.3343685Google ScholarGoogle Scholar
  20. A. Barabasi. 1999. Emergence of scaling in random networks. Science 286, 5439 (1999), 509–512. DOI:DOI:https://doi.org/10.1126/science.286.5439.509Google ScholarGoogle Scholar
  21. D. J. Watts and S. H. Strogatz. 1998. Collective dynamics of “small-world” networks. Nature 393, 6684 (1998), 440–442. DOI:DOI:https://doi.org/10.1038/30918Google ScholarGoogle Scholar
  22. M. Newman, A. Barabasi, and D Watts. 2006. The Structure and Dynamics of Networks. Princeton University Press, NJ.Google ScholarGoogle Scholar
  23. G. Fagiolo and M. Mastrorillo. 2013. International migration network: Topology and modeling. Physical Review E 88, 1 (2013), 012812. DOI:DOI:https://doi.org/10.1103/physreve.88.012812Google ScholarGoogle ScholarCross RefCross Ref
  24. K. F. Davis, P. D’Odorico, F. Laio, and L. Ridolfi. 2013. Global spatio-temporal patterns in human migration: A complex network perspective. PLoS One 8 (2013), e53723. DOI:DOI:https://doi.org/10.1371/journal.pone.0053723Google ScholarGoogle ScholarCross RefCross Ref
  25. E. Tranos, M. Gheasi, and P. Nijkamp. 2015. International migration: A global complex network. Environment and Planning B: Urban Analytics and City Science 42 (2015), 4–22. DOI:DOI:https://doi.org/10.1068/b39042Google ScholarGoogle Scholar
  26. The World Bank. 2017. Migration and Remittances Data. Retrieved October 8, 2020 from https://www.worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-remittances-data.Google ScholarGoogle Scholar
  27. M. Peres, H. Xu, and G. Wu. 2016. Community evolution in international migration top1 networks. PLoS One 11 (2016), e0148615. DOI:DOI:https://doi.org/10.1371/journal.pone.0148615Google ScholarGoogle Scholar
  28. I. Porat and L. Penguigui. World migration degree. In Proceedings of the ERSA Conference. Article ersa14p60.Google ScholarGoogle Scholar
  29. V. Danchev and M. A. Porter. 2018. Neither global nor local: Heterogeneous connectivity in spatial network structures of world migration. Social Networks 53 (2018), 4–19. DOI:DOI:https://doi.org/10.1016/j.socnet.2017.06.003Google ScholarGoogle Scholar
  30. F. Aleskerov, N. Meshcheryakova, A. Rezyapova, and S. Shvydun. 2017. Network analysis of international migration. arXiv:1806.06705 [physics.soc-ph].Google ScholarGoogle Scholar
  31. R. Cerqueti, G. P. Clemente, and R. Grassi. 2018. A network-based measure of the socio-economic roots of the migration flows. Social Indicators Research 146 (2018), 187–204. DOI:DOI:https://doi.org/10.1007/s11205-018-1883-6Google ScholarGoogle Scholar
  32. M. Windzio. 2018. The network of global migration 1990–2013. Social Networks 53 (2018), 20–29. DOI:DOI:https://doi.org/10.1016/j.socnet.2017.08.006Google ScholarGoogle Scholar
  33. V. Balàž and K. Karasovà. 2017. Geographical patterns in the intra-european migration before and after eastern enlargement: The connectivity approach. Journal of Economics 65 (2017), 3–30.Google ScholarGoogle Scholar
  34. Z. Xu. 2017. The structure and dynamics of population migration among economic areas in the United States from 1990 to 2011. Papers in Regional Science 97 (2017), 785–800. DOI:DOI:https://doi.org/10.1111/pirs.12282Google ScholarGoogle Scholar
  35. X. Liu, R. Hollister, and C. Andris. 2018. Wealthy hubs and poor chains: Constellations in the U.S. urban migration system. In Agent-Based Models and Complexity Science in the Age of Geospatial Big Data. Advances in Geographic Information Science Book Series. Springer, 73–86.Google ScholarGoogle Scholar
  36. T. Goldade, B. Charyyev, and M. H. Gunes. 2017. Network analysis of migration patterns in the United States. Complex Networks and Their Applications 6 (2017), 770–783. DOI:DOI:https://doi.org/10.1007/978-3-319-72150-7_62Google ScholarGoogle Scholar
  37. B. Charyyev and M. H. Gunes. 2019. Complex network of United States migration. Computational Social Networks 6 (2019), 1. DOI:DOI:https://doi.org/10.1186/s40649-019-0061-6Google ScholarGoogle Scholar
  38. F. Li, Z. Feng, P. Li, and Z. You. 2017. Measuring directional urban spatial interaction in China: A migration perspective. PLoS One 12 (2017), e0171107. DOI:DOI:https://doi.org/10.1371/journal.pone.0171107Google ScholarGoogle Scholar
  39. Y. Sun and K. Pan. 2014. Prediction of the intercity migration of Chinese graduates. 2014. Journal of Statistical Mechanics: Theory and Experiment 2014 (2014), P12022. DOI:DOI:https://doi.org/10.1088/1742-5468/2014/12/p12022Google ScholarGoogle Scholar
  40. G. Maier and M. Vyborny. 2005. Internal Migration Between US States—A Social Network Analysis. Working Paper. University of Vienna.Google ScholarGoogle Scholar
  41. R. A. Manduca. 2014. Domestic Migration Networks in the United States. Massachusetts Institute of Technology, Cambridge, MA.Google ScholarGoogle Scholar
  42. V. L. Sciabolazza. 2018. Moving people: Network analysis of international migration flows. In Networks of International Trade and Investment, A. Amighini, S. Gorgoni, and M. Smith (Eds.). Vernon Press, Wilmington, DE, 249–296.Google ScholarGoogle Scholar
  43. D. Askar and T. House. 2010. Complex Patterns of Multiscale Human Mobility in United Kingdom. Working Paper. University of Warwick.Google ScholarGoogle Scholar
  44. C. Caudillo-Cos and R. Tapia-McClung. 2014. Patterns of internal migration of Mexican highly qualified population through network analysis. In Computational Science and Its Applications—ICCSA 2014. Lecture Notes in Computer Science, Vol. 8582. Springer, 169–184. DOI:DOI:https://doi.org/10.1007/978-3-319-09147-1_13Google ScholarGoogle Scholar
  45. M. Szell, R. Sinatra, G. Petri, S. Thurner, and V. Latora. 2012. Understanding mobility in a social petri dish. Scientific Reports 2 (2012), 457. DOI:DOI:https://doi.org/10.1038/srep00457Google ScholarGoogle Scholar
  46. D. Guo, X. Zhu, H. Jin, P. Gao, and C. Andris. 2012. Discovering spatial patterns in origin destination mobility data. Transactions in GIS 16, 3 (2012), 411–429. DOI:DOI:https://doi.org/10.1111/j.1467-9671.2012.01344.xGoogle ScholarGoogle ScholarCross RefCross Ref
  47. B. Hawelka, I. Sitko, E. Beinat, S. Sobolevsky, P. Kazakopoulos, and C. Ratti. 2013. Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science 41, 3 (2013), 260–271. DOI:DOI:https://doi.org/10.1080/15230406.2014.890072Google ScholarGoogle Scholar
  48. F. B. Piel, A. J. Tatem, Z. Huang, S. Gupta, T. N. Williams, and D. J. Weatherall. 2014. Global migration and the changing distribution of sickle haemoglobin: A quantitative study of temporal trends between 1960 and 2000. Lancet Global Health 2, 2 (2014), e80—e89. DOI:DOI:https://doi.org/10.1016/S2214-109X(13)70150-5Google ScholarGoogle Scholar
  49. Y. Gong and M. Small. 2018. Epidemic spreading on metapopulation networks including migration and demographics. Chaos: An Interdisciplinary Journal of Nonlinear Science 28, 8 (2018), 083102. DOI:DOI:https://doi.org/10.1063/1.5021167Google ScholarGoogle Scholar
  50. G. Fagiolo and G. Santoni. 2014. Human-mobility networks, country income, and labor productivity. Network Science3, 3 (2014), 377–407. DOI:DOI:https://doi.org/10.2139/ssrn.2416574Google ScholarGoogle Scholar
  51. A. Garas, A. Lapatinas, and K. Poulios. 2016. The relation between migration and FDI in the OECD from a complex network perspective. Advances in Complex Systems 19, 6-7 (2016), 1650009. DOI:DOI:https://doi.org/10.1142/S0219525916500090Google ScholarGoogle Scholar
  52. P. Zhao, X. Liu, W. Shi, T. Jia, W. Li, and M. Chen. 2020. An empirical study on the intra-urban goods movement patterns using logistics big data. International Journal of Geographical Information Science 34, 6 (2020), 1089–1116. DOI:DOI:https://doi.org/10.1080/13658816.2018.1520236Google ScholarGoogle Scholar
  53. F. Lillo and J. A. Molina Garay. 2019. The global remittance network: An inflow and outflow analysis. The Journal of Mathematical Sociology 43, 2 (2019), 59–75. DOI:DOI:https://doi.org/10.1080/0022250X.2018.1496917Google ScholarGoogle Scholar
  54. S. Lozano and E. Gutierrez. 2018. A complex network analysis of global tourism flows. International Journal of Tourism Research 20, 1 (2018), 588–604.DOI:DOI:https://doi.org/10.1002/jtr.2208Google ScholarGoogle Scholar
  55. D. Provenzano. 2020. The migration–tourism nexus in the EU28. Tourism Economics 26, 8 (2020), 1374–1393. DOI:DOI:https://doi.org/10.1177/1354816620909994Google ScholarGoogle Scholar
  56. Bundesanstalt Statistik Österreich. 2014. Standard-Dokumentation Metainformationen (Definitionen, Erläuterungen, Methoden, Qualität) zur Wanderungsstatistik. Retrieved October 8, 2020 from http://www.statistik.at/web_de/wcmsprod/groups/gd/documents/stddok/029352.pdf#pagemode=bookmarks.Google ScholarGoogle Scholar
  57. The World Bank. 2020. World Bank Open Data. Retrieved May 16, 2021 from https://data.worldbank.org/indicator/SP.POP.TOTL.Google ScholarGoogle Scholar
  58. A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani. 2004. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America 101 (2004), 3747–3752DOI:DOI:https://doi.org/10.1073/pnas.0400087101Google ScholarGoogle ScholarCross RefCross Ref
  59. E. G. Ravenstein. 1889. The laws of migration. Journal of the Royal Statistical Society 52 (1889), 241–305. DOI:DOI:https://doi.org/10.2307/2979181Google ScholarGoogle Scholar
  60. D. Pitoski, T. J. Lampoltshammer, and P. Parycek. 2020. Supplementary Material for the Manuscript “Network Analysis of Internal Migration in Austria.” figshare. Available at https://figshare.com. DOI:DOI:https://doi.org/10.6084/m9.figshare.12387122Google ScholarGoogle Scholar
  61. A. Clauset, C. R. Shalizi, and M. E. J. Newman. 2009. Power-law distributions in empirical data. SIAM Review 51, 4 (2009), 661–703. DOI:DOI:https://doi.org/10.1137/070710111Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. S. Brin and L. Page. 1998. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30 (1998), 107–117. DOI:DOI:https://doi.org/10.1016/S0169-7552(98)00110-XGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  63. J. M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM 46 (1999), 604–632. DOI:DOI:https://doi.org/10.1145/324133.324140Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. T. Deguchi, K. Takahashi, H. Takayasu, and M. Takayasu. 2014. Hubs and authorities in the world trade network using a weighted HITS algorithm. PLoS One 9 (2014), e100338. DOI:DOI:https://doi.org/10.1371/journal.pone.0100338Google ScholarGoogle ScholarCross RefCross Ref
  65. J. J. Bartholdi, P. Jarumaneeroj, and A. Ramudhin. 2016. A new connectivity index for container ports. Maritime Economics & Logistics 18 (2016), 231–249. DOI:DOI:https://doi.org/10.1057/mel.2016.5Google ScholarGoogle Scholar
  66. T. Squartini, F. Picciolo, F. Ruzzenenti, D. Garlaschelli. 2013. Reciprocity of weighted networks. Scientific Reports 3 (2013), Article 2729. DOI:DOI:https://doi.org/10.1038/srep02729Google ScholarGoogle Scholar
  67. M. E. J. Newman. 2003. Mixing patterns in networks. Physical Review E 67, 2 (2003). DOI:DOI:https://doi.org/10.1103/PhysRevE.67.026126Google ScholarGoogle ScholarCross RefCross Ref
  68. G. Csárdi and T. Nepusz. 2006. The iGraph Software Package for Complex Network Research. Retrieved October 8, 2020 from https://cran.r-project.org/web/packages/igraph/igraph.pdf.Google ScholarGoogle Scholar
  69. M. E. J. Newman and M. Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69 (2004), 026113. DOI:DOI:https://doi.org/10.1103/physreve.69.026113Google ScholarGoogle ScholarCross RefCross Ref
  70. M. Rosvall, D. Axelsson, and C. T. Bergstrom. 2009. The map equation. European Physical Journal Special Topics2009, 178 (2009), 13–23. DOI:DOI:https://doi.org/10.1140/epjst/e2010-01179-1Google ScholarGoogle Scholar
  71. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. 2008. Fast unfolding of communities in large networks.Journal of Statistical Mechanics: Theory and Experiment 2008 (2008), P10008. DOI:DOI:https://doi.org/10.1088/1742-5468/2008/10/p10008Google ScholarGoogle Scholar
  72. Z. Yang, R. Algesheimer, and C. J. Tessone. 2016. A comparative analysis of community detection algorithms on artificial networks. Scientific Reports 6 (2016), 30750. DOI:DOI:https://doi.org/10.1038/srep30750Google ScholarGoogle ScholarCross RefCross Ref
  73. A. Clauset, M. E. J. Newman, and C. Moore. 2004. Finding community structure in very large networks. Physical Review E 70 (2004), 066111. DOI:DOI:https://doi.org/10.1103/physreve.70.066111Google ScholarGoogle ScholarCross RefCross Ref
  74. G. K. Zipf. 1946. The hypothesis: On the intercity movement of persons. American Sociological Review 11 (1946), 677–686, DOI:DOI:https://doi.org/10.2307/2087063Google ScholarGoogle ScholarCross RefCross Ref
  75. P. Kaluza, A. Kolzsch, M. T. Gastner, and B. Blasius. 2010. The complex network of global cargo ship movements. Journal of the Royal Society Interface 7 (2010), 1093–1103. DOI:DOI:https://doi.org/10.1098/rsif.2009.0495Google ScholarGoogle ScholarCross RefCross Ref
  76. F. Simini, M. C. González, A. Maritan, and A.-L. Barabási. 2012. A universal model for mobility and migration patterns. Nature 484 (2012), 96–100. DOI:DOI:https://doi.org/10.1038/nature10856Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Network Analysis of Internal Migration in Austria

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image Digital Government: Research and Practice
        Digital Government: Research and Practice  Volume 2, Issue 3
        Regular Papers
        July 2021
        102 pages
        EISSN:2639-0175
        DOI:10.1145/3474845
        Issue’s Table of Contents

        Copyright © 2021 Owner/Author

        This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 11 July 2021
        • Online AM: 7 May 2021
        • Accepted: 1 January 2021
        • Revised: 1 October 2020
        • Received: 1 May 2020
        Published in dgov Volume 2, Issue 3

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format