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Computational Neural Networks: A New Paradigm for Spatial Analysis

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  • M M Fischer

    (Department of Economic Geography, Vienna University of Economics and Business Administration, A-1090 Vienna, Augasse 2-6, Austria)

Abstract

In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à -viz conventional modelling is illustrated for an application field with noisy data of limited record length: spatial interaction modelling of telecommunication data in Austria. The computational appeal of neural networks for solving some fundamental spatial analysis problems is summarized and a definition of computational neural network models in mathematical terms is given. Three definitional components of a computational neural network—properties of the processing elements, network topology and learning—are discussed and a taxonomy of computational neural networks is presented, breaking neural networks down according to the topology and type of interconnections and the learning paradigm adopted. The attractiveness of computational neural network models compared with the conventional modelling approach of the gravity type for spatial interaction modelling is illustrated before some conclusions and an outlook are given.

Suggested Citation

  • M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.
    • Handle: RePEc:sae:envira:v:30:y:1998:i:10:p:1873-1891
    DOI: 10.1068/a301873
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    References listed on IDEAS

    as
    1. Sucharita Gopal & Manfred M. Fischer, 2001. "Fuzzy ARTMAP — A Neural Classifier for Multispectral Image Classification," Advances in Spatial Science, in: Manfred M. Fischer & Yee Leung (ed.), GeoComputational Modelling, chapter 7, pages 165-194, Springer.
    2. Yee Leung, 1997. "Feedforward Neural Network Models for Spatial Data Classification and Rule Learning," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 17, pages 336-359, Springer.
    3. Fischer, Manfred M. & Gopal, Sucharita, 1994. "Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows," MPRA Paper 77822, University Library of Munich, Germany.
    4. Manfred M. Fischer & Arthur Getis (ed.), 1997. "Recent Developments in Spatial Analysis," Advances in Spatial Science, Springer, number 978-3-662-03499-6, Fall.
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    Cited by:

    1. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    2. Lin, Huiyan & Lu, Kang Shou & Espey, Molly & Allen, Jeffery, 2005. "Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach," 2005 Annual meeting, July 24-27, Providence, RI 19364, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.
    4. Karima Kourtit & Daniel Arribas-Bel & Peter Nijkamp, 2012. "High performers in complex spatial systems: a self-organizing mapping approach with reference to The Netherlands," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(2), pages 501-527, April.
    5. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
    6. repec:dgr:vuarem:2009-14 is not listed on IDEAS
    7. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    8. Daniel Arribas-Bel & Peter Nijkamp & Henk Scholten, 2011. "Multi-Dimensional Urban Sprawl in Europe: a Self-Organizing Map Approach," ERSA conference papers ersa10p485, European Regional Science Association.
    9. P Rees & I Turton, 1998. "Guest Editorial," Environment and Planning A, , vol. 30(10), pages 1835-1838, October.
    10. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.

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