Keystone Sector Identification

  • Maureen Kilkenny
  • Laura Nalbarte
Part of the Advances in Spatial Science book series (ADVSPATIAL)


This chapter presents a new a method for identifying keystone sectors in communities, where sectors are broadly defined to include churches, clubs, associations, and public institutions as well as different types of businesses and industries. In an arch, the keystone is the one with the unique shape at the top of the arch that is critical for the arch’s structural stability. The term keystone species was first coined by ecologists in the late 1960s with respect to the species responsibility for the structure and integrity of an ecosystem. We now coin the term for use in community development analysis. In a community, the keystone sector is one that plays a unique role and without which the community is fundamentally and detrimentally altered.


Community College Social Network Analysis Voluntary Association Keystone Species Perfect Substitute 
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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  1. Berge, C. 1962. The Theory of Graphs and Its Applications. New York, Wiley.Google Scholar
  2. Campbell, J. 1975. “Application of graph theoretic analysis to interindustry relationships: The example of Washington state.” Regional Science and Urban Economics, 5, 91–106.CrossRefGoogle Scholar
  3. Cella, G. 1984. “The input-output measurement of interindustry linkages.” Oxford Bulletin of Economics and Statistics, 46, 73–84.CrossRefGoogle Scholar
  4. Fienberg, S.E., M.M. Meyer, and S. Wasserman. 1981. “Analyzing data from multivariate directed graphs: an application to social networks.” In V. Barnett, ed. Interpreting Multivariate Data. London, John Wiley. Pp. 289–306.Google Scholar
  5. Fienberg, S.E., M.M. Meyer, and S. Wasserman. 1985. “Statistical Analysis of multiple sociometric relations.” Journal of American Statistician Association, 80. 51–67.CrossRefGoogle Scholar
  6. Freeman, L. 1977. “A set of Measures of Centrality Based on Betweenness.” Sociometry, 1, 35–41.CrossRefGoogle Scholar
  7. Galaskiewicz, J. and P.V. Mardsen. 1978. “Interorganizational resource networks: formal patterns of overlap.” Social Science Research, 7, 89–107.CrossRefGoogle Scholar
  8. Goode, F., and S. Hastings. 1988. Northeast Industrial Targeting (NIT) and Economic Development Data Base (EDD) System User’s Manual version 1, Dept. of Agricultural Economics and Rural Sociology, The Pennsylvania State University, University Park, PA 16802; March.Google Scholar
  9. Granovetter, M. 1973. “The Strength of Weak Ties.” American Journal of Sociology, 78, 1360–1380.CrossRefGoogle Scholar
  10. Hanson, S. and J. Huff. 1986. “Classification Issues in the Analysis of Complex Travel Behavior.” Transportation 13, 271–293.CrossRefGoogle Scholar
  11. Holland, P.W., and S. Leinhardt, S. 1979. “Perspectives on Social Network Analysis.” Mathematical Social Science Board’s Advanced Research Symposium on Social Networks. New York, Academic Press.Google Scholar
  12. Jacobs, J. 1984. Cities and the Wealth of Nations. New York, Random House.Google Scholar
  13. Kauffman, S.A. 1988. “The evolution of economics webs.” In P.W. Anderson, K.J. Arrow and D. Pines, eds. The Economy as a Complex Evolving System. New York, Addison-Wesley. Pp. 125–146.Google Scholar
  14. Kilkenny, M., L. Nalbarte, and T. Besser. 1999. “Reciprocated Community Support and Small-Town, Small-Business Success.” Entrepreneurship and Regional Development, 11, 231–246.CrossRefGoogle Scholar
  15. Paine, R. 1969. “A Note on Trophic Complexity and Community Stability.” American Naturalist 103, 91–93. Cited in S. Mills, M. Soule, and D. Doak. 1993. “The Keystone Species Concept in Ecology and Conservation.” BioScience 43, 219–224.CrossRefGoogle Scholar
  16. Robinson, D.F. and L.R. Foulds. 1980. Digraphs: Theory and Techniques. London, Gordon and Breach.Google Scholar
  17. Roy, J.R. 1994. “Trade with and without intermediaries: some alternative model formulations.” Annals of Regional Science, 28, 329–344.CrossRefGoogle Scholar
  18. Roy, J. R. 1995 “Dispersed spatial input demand functions.” Annals of Regional Science, 29, 329–334.CrossRefGoogle Scholar
  19. Scott, J. 1991. Social Network Analysis. London, Sage Publications Ltd.Google Scholar
  20. Sonis, M. and G.J.D. Hewings. 1998. “Economic complexity as network complication: multiregional input-output structural path analysis.” Annals of Regional Science, 32, 407–436.CrossRefGoogle Scholar
  21. Sonis, M., G.J.D. Hewings, and J. Guo. 2000. “A new image of classical key sector analysis: minimum information decomposition of the Leontief inverse.” Economic Systems Research 12, 401–423CrossRefGoogle Scholar
  22. Stone, R. 1995. “Taking a New Look at Life through a Functional Lens.” Science 269, 316–317.CrossRefGoogle Scholar
  23. Wasserman, S. and K. Faust. 1994. Social Network Analysis. Cambridge, Cambridge University Press.Google Scholar
  24. Wasserman, S. and P. Pattison. 1996. “Logit models and logistic regressions for social networks.” Psychometrika, 61, 401–425.CrossRefGoogle Scholar
  25. Wright, C.C. 1979. “Arcs and Cars: An Approach to Road Traffic Management based on Graph Theory. In R.J. Wilson, ed. Graph Theory and Combinatorics. London, Pitman.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Maureen Kilkenny
    • 1
  • Laura Nalbarte
    • 1
  1. 1.Department of EconomicsIowa State UniversityAmesUSA

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