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Spatial interaction modelling

Author

Listed:
  • John Roy
  • Jean-Claude Thill

Abstract

Spatial interaction (SI) is the process whereby entities at different points in physical space make contacts, demand/supply decisions or locational choices. The entities can be individuals or firms and the choices can include housing, jobs, production quantities, exports, imports, face-to-face contacts, schools, retail centres and activity centres. The first SI models can be grouped under the generic heading gravity models. Their main characteristic is that they model the behaviour of demand or supply segments, rather than that of individuals and firms. This article traces the development of these models from their inception in the early part of the twentieth century to the present. The key advances include the replacement of the gravity analogy by the more general concepts of entropy or information theory, a statistical framework commonly used in physics. With the arrival of the regional science paradigm over 50 years ago, a key challenge has been to broaden these models compared to those arising in spatial economics, thus arriving at a more inclusive probabilistic framework. These efforts are discussed here, as well as inclusion of geographical advances, embracing activities as generators of travel, time-geography, recognition of spatial interdependencies, and use of neuro-computing principles. Copyright Springer-Verlag Berlin/Heidelberg 2003

Suggested Citation

  • John Roy & Jean-Claude Thill, 2003. "Spatial interaction modelling," Papers in Regional Science, Springer;Regional Science Association International, vol. 83(1), pages 339-361, October.
  • Handle: RePEc:spr:presci:v:83:y:2003:i:1:p:339-361
    DOI: 10.1007/s10110-003-0189-4
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    Citations

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    Cited by:

    1. Arvis, Jean-François, 2013. "Integrating gravity: the role of scale invariance in gravity models of spatial interactions and trade," Policy Research Working Paper Series 6347, The World Bank.
    2. José Miguel Barrios & Willem W. Verstraeten & Piet Maes & Jean-Marie Aerts & Jamshid Farifteh & Pol Coppin, 2012. "Using the Gravity Model to Estimate the Spatial Spread of Vector-Borne Diseases," IJERPH, MDPI, vol. 9(12), pages 1-19, November.
    3. Kyosang Hwang & Tooba Binte Asif & Taesik Lee, 2022. "Choice-driven location-allocation model for healthcare facility location problem," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 1040-1065, December.
    4. Martina Neuländtner & Thomas Scherngell, 2020. "Geographical or relational: What drives technology-specific R&D collaboration networks?," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 65(3), pages 743-773, December.
    5. Richard Fry & Scott Orford & Sarah Rodgers & Jennifer Morgan & David Fone, 2020. "A best practice framework to measure spatial variation in alcohol availability," Environment and Planning B, , vol. 47(3), pages 381-399, March.
    6. Chakraborty, A. & Beamonte, M.A. & Gelfand, A.E. & Alonso, M.P. & Gargallo, P. & Salvador, M., 2013. "Spatial interaction models with individual-level data for explaining labor flows and developing local labor markets," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 292-307.

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