Rural–Urban Regions: A Spatial Approach to Define Urban–Rural Relationships in Europe

  • Ingo Zasada
  • Wolfgang Loibl
  • Regine Berges
  • Klaus Steinnocher
  • Mario Köstl
  • Annette Piorr
  • Armin Werner


The previous chapter introduced the range of issues associated with the peri-urban, the subject of this book. The peri-urban as a specific morphological type was defined and the different dimensions of its dynamics were explored. This peri-urban zone is intimately associated with the transition from a dense urban structure to that of a rural character and since it also involves movements into, out of and across it from both these extremes, it is difficult to consider it properly without understanding the broader regional context and dynamics across the urban–rural gradient. Therefore, this chapter will focus on the broader context of urban–rural relationships. Based on recent scientific debates concerning the concept of functional regions and urban–rural relationships, both current and previous definitions and their political implementations are introduced before presenting a new typology to represent Rural–urban Regions (RUR) spatially. Covering the territory of European Union (EU), this typology classifies regions into different types, considering city size, degree of regional mono- and poly-centricity, as well as their urban, peri-urban or rural predominance. The development of the typology includes a further delineation of regions into urban, peri-urban and rural sub-regions, all based on land use patterns and population distribution and density. The typology was subsequently used throughout the PLUREL project and each of the case studies presented in Part Two refers to one of these types, although not all are represented there, since the case studies were unavoidably selected before the typology was developed.


European Union Urban Core Core City NUTS3 Region Small Urban Centre 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Stefanie Lange and Petri Kahila from Nordregio, Stockholm for their kind reviewing work helping to improve this chapter.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ingo Zasada
    • 1
  • Wolfgang Loibl
    • 2
  • Regine Berges
    • 1
  • Klaus Steinnocher
    • 2
  • Mario Köstl
    • 2
  • Annette Piorr
    • 1
  • Armin Werner
    • 1
  1. 1.Leibniz Centre for Agricultural Landscape Research (ZALF)MünchebergGermany
  2. 2.AIT Austrian Institute of Technology GmbHViennaAustria

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