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Neural Networks and Regional Science Modeling: A Survey of Techniques for Complex Spatial Analysis

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  • Wier, Thomas G.

    (Northeastern Stater University)

  • Phoha, Vir V.

    (Louisiana Tech University)

Abstract

This study examines the value of utilizing neural net modeling for issues relating to optimization across a network of cities in space. Neural nets are made up of many nonlinear computational elements that operate in parallel and are arranged in a manner similar to biological neural nets. Defining a neural net model involves specifying a net topology, arrangement of nodes, training or learning rules, adjustment of weights associated with connections, node characteristics, and rules of transformation from input to output. All of these are the major issues in such regional problems as labor force migration and firm location.

Suggested Citation

  • Wier, Thomas G. & Phoha, Vir V., 2002. "Neural Networks and Regional Science Modeling: A Survey of Techniques for Complex Spatial Analysis," The Review of Regional Studies, Southern Regional Science Association, vol. 32(2), pages 309-324, Summer/Fa.
    • Handle: RePEc:rre:publsh:v:32:y:2002:i:2:p:309-324
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    References listed on IDEAS

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    1. Greenwood, Michael J, 1969. "An Analysis of the Determinants of Geographic Labor Mobility in the United States," The Review of Economics and Statistics, MIT Press, vol. 51(2), pages 189-194, May.
    2. Levy, Mildred B & Wadycki, Walter J, 1973. "The Influence of Family and Friends on Geographic Labor Mobility: An International Comparison," The Review of Economics and Statistics, MIT Press, vol. 55(2), pages 198-203, May.
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    Cited by:

    1. Mildred E Warner & James E Pratt, 2005. "Spatial Diversity in Local Government Revenue Effort under Decentralization: A Neural-Network Approach," Environment and Planning C, , vol. 23(5), pages 657-677, October.

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