Skip to main content

Advertisement

SpringerLink
Log in

The spatial implications of the functional proximity deriving from air passenger flows between European metropolitan urban regions

  • Published:
GeoJournal Aims and scope Submit manuscript

Abstract

Until recently the traditional spatial configuration of the European geography was based upon the core-periphery model. The ‘pentagon’, broadly defined as lying between London, Paris, Milan, Munich and Hamburg, was seen as the core area characterised by having the highest concentration of economic development in the European Union (EU), with the remainder of the European territory viewed as peripheral, albeit to varying degrees. In a number of cases such peripheral areas equated with clear regional disparities. The elaboration of the European Spatial Development Perspective (ESDP) (CEC, European spatial development perspective, towards balanced and sustainable development in the territory of the European Union, 1999) challenged this core-periphery model. European spatial planning policies, aimed at encouraging social and economic, and with ever increasing importance, territorial cohesion, seek amongst other aspects to encourage the development of a balanced and polycentric urban system. This paper adopts a network analysis approach to the analysis of air passenger flows between some 28 principal European metropolitan urban regions. The evaluation of these flows contributes to an enhanced comprehension of the spatial dynamics of the European metropolitan territory which goes beyond that deriving from the more standard analyses of the individual components of the urban system. Several indicators are used, deriving from gravitational modelling techniques, to analyse the complexity of the air passenger flows. A multidimensional scaling (MDS) technique is introduced in order to interpret and visualise the resulting spatial configuration and positioning of the different metropolitan centres within the conceptual European ‘space of air passenger flows’, thereby contrasting with the more traditional map-based geographical image of Europe, based upon Cartesian coordinates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Belgium, France, Germany, Italy, Luxembourg, The Netherlands, the United Kingdom, Denmark, Ireland, Greece, Spain, Portugal, Austria, Finland, Sweden, the Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Slovakia, Bulgaria and Rumania (EU27) and Norway and Switzerland.

  2. Paris and London.

  3. Munich, Frankfurt, Madrid, Bruxelles, Milano, Roma, Hamburg, Kobenhavn, Zurich, Amsterdam, Berlin, Stockholm, Stuttgart, Barcelona, Düsseldorf, Wien and Köln.

  4. Helsinki, Oslo, Athens, Greater Manchester, Dublin, Goteborg, Torino and Geneve.

  5. Lyon, Antwerp, Lisboa, Rotterdam, Malmo, Marseille, Lille, Nice, Napoli, Bern, Praha, Glasgow, Bremen, Toulouse, Warsawa, Budapest, Aarhus, Edinburgh, Bergen, Birmingham, Bilbao, Valencia, Luxembourg, Bologna and Palma de Mallorca.

  6. Bratislava, Turku, Cork, Bordeaux, Le Havre, Genova, Bucuresti, Tallinn, Sofia, Southampton, Sevilla, Porto, Krakow, Vilnius, Ljublijana, Riga, Katowice, Gdansk-Gdynia-Sopo, Poznan, Wroclaw, Lodz, Valletta, Szczecin and Timosoara.

  7. EU15+2 = Belgium, France, Germany, Italy, Luxembourg, The Netherlands, United Kingdom, Denmark, Ireland, Greece, Spain, Portugal, Austria, Finland, Sweden; and Norway and Switzerland.

  8. Paris, London, Munich, Frankfurt, Madrid, Brussels, Milan, Rome, Hamburg, Copenhagen, Zurich, Amsterdam, Berlin, Stockholm, Stuttgart, Barcelona, Düsseldorf, Vienna, Cologne/Bonn, Helsinki, Oslo, Athens, Greater Manchester, Dublin, Gothenburg and Geneva, as well as Lisbon and Luxembourg, given their capital city status within the EU15 grouping.

  9. http://epp.eurostat.ec.europa.eu

  10. Other data sources such as the ICAO were considered but were rejected on the basis of not being complete for the sample of 28 cities and appearing to be restricted to returns from a limited number of airlines operating from the airports in question.

  11. Berlin (Tegel, Tempelhof and Schonefeld); Paris (Charles de Gaulle and Orly); Milan (Linate and Malpensa); Rome (Fiumicino and Campino) and London (Luton, Gatwick, City, Heathrow and Stansted).

  12. <<Transport <<Air transport <<Air transport measurement <<Detailed air passenger transport by reporting country and routes <<Air passenger transport between the main airports of reporting country and their main partner airports.

  13. Although the matrix contains (n × n) cells, the maximum number of possible combinations is ((n × n) − n), on the basis of the diagonal being zero. No passengers depart from and arrive at the same airport. Even in the case of the London airports, no data was found relating to passenger flows of this nature.

  14. <<Transport <<Air transport <<Air transport measurement <<Overview of the air passenger transport by country and airports <<Air passenger transport between main airports in each reporting country and partner reporting countries.

  15. Possibilities for estimating indirect flows, and as a consequence taking traditional European ‘hubs’ into consideration, lie within Markov Chain and complex gravity modelling methodologies. Coincidentally the authors are currently developing work in this area, with a view to applying it to air passenger flows within the European space.

  16. Rail would undoubtedly be the realistic mode of travel for connecting between these cities.

  17. Self-containment refers to the proportion of the workers who reside and work in the same municipality (RWL) with respect to the resident employed population who might work within or outside the municipality (REP). Self-sufficiency is seen as the proportion between the same RWL and total localised workplaces (LWP).

  18. Xcg = (ΣMi × Xi)/(ΣMi), for i = 1 to N; and Ycg = (ΣMi × Yi)/(ΣMi), for i = 1 to N; where Xcg and Ycg are the x and y coordinates of the Centre of Gravity; Xi and Yi are the x and y coordinates of the airports; Mi is the mass of the airport (in this case M = 1); and N is the number of airports.

  19. LONGITUDE 7.86725° East and LATITUDE 49.86725° North.

  20. Great Circle Distance Formula (with radians) = 6,378.8 * arcos[sin(lat1) * sin(lat2) + cos(lat1) * cos(lat2) * cos(lon2 − lon1)].

  21. A more thorough reading of the spatial positioning would have been achieved taking into consideration multi-modality, i.e. air, rail and road passenger flows. Indeed this would have compensated in part for the absence of air passenger flows in the cases requiring the input of the ‘virtual’ passenger flows. It is the authors’ intention to carry out future research examining multi-modality at the European level.

References

  • Alkaabi, K. A., & Debbage, K. (2007). Air passenger demand and skilled labor markets by US metropolitan area. Journal of Air Transport Management, 13(3), 121–130.

    Article  Google Scholar 

  • Brueckner, J. K. (2003). Airline traffic and urban economic development. Urban Studies, 40(8), 1455–1469.

    Article  Google Scholar 

  • Brunet, R. (1989). Les villes européennes. Report for the DATAR. Paris: Groupement d’Intérêt Public.

  • Button, K., & Taylor, S. (2000). International air transportation and economic development. Journal of Air Transport Management, 6(4), 209–222.

    Article  Google Scholar 

  • Castells, M. (1989). The informational city: Information technology, economic restructuring, and the urban-regional process. Oxford: Blackwell.

    Google Scholar 

  • Castells, M. (1993). European cities, the informational society and the global economy. Tijdschrift voor Economische en Sociale Geografie, 84, 247–257.

    Article  Google Scholar 

  • Castells, M. (1996). The information age: Economy society and culture. Vol I: The rise of the network society. Oxford: Blackwell.

    Google Scholar 

  • Cattan, N. (1995). Attractivity and internationalisation of major European cities: The example of air traffic. Urban Studies, 32(2), 303–312.

    Article  Google Scholar 

  • CEC. (1999). European spatial development perspective, towards balanced and sustainable development in the territory of the European Union. Luxembourg: Office for Official Publications of the European Communities.

  • CEC. (2000). Lisbon European Council 23 and 24 March 2000, Presidency Conclusions. Retrieved Jul 30, 2007, from http://www.europarl.europa.eu/summits/lis1_en.htm.

  • CEC. (2001). European Council Göteborg, Conclusions of the Presidency, 15 and 16 June 2001. Retrieved July 30, 2007, from http://www.europarl.europa.eu/summits/pdf/got1_en.pdf.

  • CEC. (2004). Interim Territorial Cohesion Report. Luxembourg: Office for Official Publications of the European Communities.

  • CEC. (2007). Territorial Agenda of the European Union, Towards a more competitive and sustainable Europe of diverse regions. Retrieved July 11, 2007 from http://www.bmvbs.de/Anlage/original_1005295/Territorial-Agenda-of-the-European-Union-Agreed-on-25-May-2007-barrier-free.pdf.

  • Coombes, M., Green, A. E., & Openshaw, S. (1986). An efficient algorithm to generate official statistical reporting areas: The case of the 1984 travel-to-work-areas revision in Britain. Journal of the Operational Research Society, 37, 943–953.

    Article  Google Scholar 

  • Coombes, M., & Openshaw, S. (1982). The use and definition of travel-to-work areas in Great Britain: Some comments. Regional Studies, 16, 141–149.

    Article  Google Scholar 

  • Council of Europe European Conference of Ministers responsible for Regional Planning (CEMAT). (1983). European charter for regional/spatial planning, adopted on May 20th 1983 in Torremolinos, Spain.

  • Debbage, K. (1999). Air transportation and urban-economic restructuring: Competitive advantage in the US Carolinas. Journal of Air Transport Management, 5(4), 211–221.

    Article  Google Scholar 

  • Debbage, K., & Delk, D. (2001). The geography of air passenger volume and local employment patterns by US metropolitan core area: 1973–1996. Journal of Air Transport Management, 7(3), 159–167.

    Article  Google Scholar 

  • Derudder B., & 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), 2371–2388.

    Article  Google Scholar 

  • ESPON (2004). ESPON Project 1.1.1. Potentials for polycentric development in Europe, Final Report, ESPON.

  • EUROSTAT. (1992). Study of employment zones. Regional statistics and accounts. Luxembourg: EUROSTAT.

    Google Scholar 

  • Guimerà, R., Mossa, S., Turtschi, A., & Amaral, L. (2005). The worldwide air transportation network: anomalous centrality, community structure and cities’ global roles. Proceedings of the National Academy of Sciences of the United States of America, 102(22), 7794–7799.

  • Hall, P. (2004). World cities, mega-cities and global mega-city regions. GaWC Annual Lecture. Loughborough University: Globalisation and World Cities Study Group and Network.

  • Hall, P., & Pain, K. (2006). The polycentric metropolis. Learning from mega-city regions in Europe. London: Earthscan.

    Google Scholar 

  • Keeling, D. J. (1995). Transport and the world city paradigm. In P. Knox & P. J. Taylor (Eds.), World cities in a world-system. Cambridge: Cambridge University Press.

    Google Scholar 

  • Kunzmann, K., & Wegener, M. (1991). The pattern of urbanisation in Western Europe 1960–1990, Report for the Directorate General XVI of the Commission of the European Communities as part of the study Urbanisation and the Function of Cities in the European Community, IRPUD, Dortmund.

  • Lee, C. (1973). Models in planning. Oxford: Pergamon Press.

    Google Scholar 

  • Liu, Z-J., Debbage, K., & Blackburn, B. (2007). Locational determinants of major US air passenger markets by metropolitan areas. Paper presented at the 2007 Meeting of the Association of American Geographers, 17–21 April, San Francisco.

  • O’Connell, A. (1999). Book review of ‘modern multidimensional scaling: Theory and practice’, by I. Borg & P. Groenen (1997). New York: Springer-Verlag in Journal of the American Statistical Association, 94(445), 338–339.

  • Roca, J., & Moix, M. (2005). The interaction value: its scope and limits as an instrument for delimiting urban systems. Regional Studies, 39(3), 357–373.

    Article  Google Scholar 

  • Sforzi, F. (1991). La delimitazioni dei sistemi urbani: definizione, concetti e metodi. In C. Bertglia & A. La Bella (Eds.), I sistemi urbani Milan: Franco Angeli.

    Google Scholar 

  • Smart M. W. (1974). Labour market areas: Uses and definitions. Progress in Planning, 2, 238–353.

    Article  Google Scholar 

  • Smith, D. A., & Timberlake, M. (1995a). Conceptualising and mapping the structure of the world system’s city system. Urban Studies, 32(2), 287–302.

    Article  Google Scholar 

  • Smith, D. A., & Timberlake, M. (1995b). Cities in global matrices: Toward mapping the world-system’s city system. In P. Knox & P. J. Taylor (Eds.), World cities in a world-system Cambridge: Cambridge University Press.

    Google Scholar 

  • Smith, D. A., & Timberlake, M. (2001). World city networks and hierarchies, 1977–1997. American Behavioral Scientist, 44(10), 1656–1678.

    Article  Google Scholar 

  • Smith, D. A., & Timberlake, M. (2002). Hierarchies of dominance among World Cities: A network approach. In S. Sassen (Ed.), Global networks, linked cities London: Routledge.

    Google Scholar 

  • Spiekermann, K., & Wegener, M. (1994). The shrinking continent: New time space maps of Europe. Environment and Planning B: Planning and Design, 21, 653–673.

    Article  Google Scholar 

  • Taylor, P. J., & Hoyler, M. (2000). The spatial order of European cities under conditions of contemporary globalization. Tijdschrift voor Economische en Social Geografie, 91(2), 176–189.

    Article  Google Scholar 

  • Timberlake, M., & Ma, X. (2007). Shanghai’s increasing centrality in airline passenger networks. Paper presented at the 2007 Meeting of the Association of American Geographers, 17–21 April, San Francisco.

  • Wilson, A. G. (1971). Entropy in urban and regional modelling. London: Pion.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, 08034, Spain

    Malcolm C. Burns, Josep Roca Cladera & Montserrat Moix Bergadà

Authors
  1. Malcolm C. Burns
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Josep Roca Cladera
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Montserrat Moix Bergadà
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malcolm C. Burns.

Additional information

Special Issue: Airline Networks.

Appendix 1

Appendix 1

Table A Functional distances between the airports of the sample
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Burns, M.C., Roca Cladera, J. & Moix Bergadà, M. The spatial implications of the functional proximity deriving from air passenger flows between European metropolitan urban regions. GeoJournal 71, 37–52 (2008). https://doi.org/10.1007/s10708-008-9144-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10708-008-9144-x

Keywords

Search

Navigation