Strategic Spatial Governance: Deriving Social–Ecological Frameworks for Managing Landscapes and Regions

  • David J Brunckhorst
  • Ian Reeve
  • Phil Morley
  • Karl Bock
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Since the 1980s, watersheds or catchments have been the primary regions used for natural resource management in many countries. Catchments however, often do not represent the range of biophysical and social characteristics of importance for effective resource governance. The requirements for spatial definition of resource governance egions have received little analysis in science or policy. Research on ‘place’ attachment and community participation in resource management provides a grounding to re-examine such regional arrangements. This chapter describes three characteristics considered to be of priority importance in identifying regional boundaries for resource governance. Firstly, the boundaries of resource governance regions should enclose areas of high interest and importance to local residents. Secondly, the biophysical characteristics of a resource governance region should be as homogenous as possible. Thirdly, the nature and reach of environmental externalities of resource use should determine the size and nesting of resource management regions. An example of the application of these concepts to derive a hierarchy of nested regions for the State of New South Wales, Australia is provided. The results have been used by the New South Wales Government and the Electoral Commission to reconsider local government, natural resource management regions, and representative democracy within the State. Wider applications might include review of municipal boundaries and regional governance frameworks in other nations and across the European Union.


Natural Resource Management Community Area Place Attachment Fuzzy Cognitive Mapping Resource Governance 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • David J Brunckhorst
    • 1
  • Ian Reeve
    • 1
  • Phil Morley
    • 1
  • Karl Bock
    • 1
  1. 1.Institute for Rural Futures and UNESCO Centre for Bioregional Resource ManagementUniversity of New EnglandNSWAustralia

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