Abstract
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.
Financial support in the form of grants from TVA Rural Studies Program 1997–8 is gratefully acknowledged.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Berge, C. 1962. The Theory of Graphs and Its Applications. New York, Wiley.
Campbell, J. 1975. “Application of graph theoretic analysis to interindustry relationships: The example of Washington state.” Regional Science and Urban Economics, 5, 91–106.
Cella, G. 1984. “The input-output measurement of interindustry linkages.” Oxford Bulletin of Economics and Statistics, 46, 73–84.
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.
Fienberg, S.E., M.M. Meyer, and S. Wasserman. 1985. “Statistical Analysis of multiple sociometric relations.” Journal of American Statistician Association, 80. 51–67.
Freeman, L. 1977. “A set of Measures of Centrality Based on Betweenness.” Sociometry, 1, 35–41.
Galaskiewicz, J. and P.V. Mardsen. 1978. “Interorganizational resource networks: formal patterns of overlap.” Social Science Research, 7, 89–107.
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.
Granovetter, M. 1973. “The Strength of Weak Ties.” American Journal of Sociology, 78, 1360–1380.
Hanson, S. and J. Huff. 1986. “Classification Issues in the Analysis of Complex Travel Behavior.” Transportation 13, 271–293.
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.
Jacobs, J. 1984. Cities and the Wealth of Nations. New York, Random House.
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.
Kilkenny, M., L. Nalbarte, and T. Besser. 1999. “Reciprocated Community Support and Small-Town, Small-Business Success.” Entrepreneurship and Regional Development, 11, 231–246.
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.
Robinson, D.F. and L.R. Foulds. 1980. Digraphs: Theory and Techniques. London, Gordon and Breach.
Roy, J.R. 1994. “Trade with and without intermediaries: some alternative model formulations.” Annals of Regional Science, 28, 329–344.
Roy, J. R. 1995 “Dispersed spatial input demand functions.” Annals of Regional Science, 29, 329–334.
Scott, J. 1991. Social Network Analysis. London, Sage Publications Ltd.
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.
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, 401–423
Stone, R. 1995. “Taking a New Look at Life through a Functional Lens.” Science 269, 316–317.
Wasserman, S. and K. Faust. 1994. Social Network Analysis. Cambridge, Cambridge University Press.
Wasserman, S. and P. Pattison. 1996. “Logit models and logistic regressions for social networks.” Psychometrika, 61, 401–425.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kilkenny, M., Nalbarte, L. (2002). Keystone Sector Identification. In: Hewings, G.J.D., Sonis, M., Boyce, D. (eds) Trade, Networks and Hierarchies. Advances in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04786-6_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-04786-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07712-8
Online ISBN: 978-3-662-04786-6
eBook Packages: Springer Book Archive