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Spatial interaction modelling

In: Fifty Years of Regional Science

Author

Listed:
  • John R. Roy

    (ETUDES)

  • Jean-Claude Thill

    (The State University of New York)

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.

Suggested Citation

  • John R. Roy & Jean-Claude Thill, 2004. "Spatial interaction modelling," Advances in Spatial Science, in: Raymond J. G. M. Florax & David A. Plane (ed.), Fifty Years of Regional Science, pages 339-361, Springer.
  • Handle: RePEc:spr:adspcp:978-3-662-07223-3_15
    DOI: 10.1007/978-3-662-07223-3_15
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    Citations

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    Cited by:

    1. David Guerrero & Jean-Claude Thill, 2023. "Port competition in contestable hinterlands: The case of preferential relationships and barrier effects in Central Europe," Post-Print hal-04166277, HAL.
    2. Manuel Acosta & Joaqu�n M. Azagra-Caro & Daniel Coronado, 2016. "Access to Universities' Public Knowledge: Who is More Regionalist?," Regional Studies, Taylor & Francis Journals, vol. 50(3), pages 446-459, March.
    3. M. Alonso & M. Beamonte & P. Gargallo & M. Salvador, 2014. "Labour and residential accessibility: a Bayesian analysis based on Poisson gravity models with spatial effects," Journal of Geographical Systems, Springer, vol. 16(4), pages 409-439, October.
    4. Šveda, Martin & Madajová, Michala Sládeková, 2023. "Estimating distance decay of intra-urban trips using mobile phone data: The case of Bratislava, Slovakia," Journal of Transport Geography, Elsevier, vol. 107(C).
    5. João O. Borba & Tomaz Ponce Dentinho, 2016. "Evaluation of urban scenarios using bid-rents of spatial interaction models as hedonic price estimators: an application to the Terceira Island, Azores," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(3), pages 671-685, May.
    6. Carlos Llano & Almudena Esteban & Julian Pérez & Antonio Pulido, 2010. "Opening the Interregional Trade ‘‘Black Box’’: The C-Intereg Database for the Spanish Economy (1995—2005)," International Regional Science Review, , vol. 33(3), pages 302-337, July.
    7. Fuhrmeister, Clemens & Titze, Mirko & Blum, Ulrich & Marek, Philipp, 2014. "Does Proximity Matter in the Choice of Partners in Collaborative R&D Projects? – An Empirical Analysis of Granted Projects in Germany," IWH Discussion Papers 12/2014, Halle Institute for Economic Research (IWH).
    8. Kress, Dominik & Pesch, Erwin, 2012. "Sequential competitive location on networks," European Journal of Operational Research, Elsevier, vol. 217(3), pages 483-499.
    9. Vladimir Marianov & H. A. Eiselt, 2016. "On agglomeration in competitive location models," Annals of Operations Research, Springer, vol. 246(1), pages 31-55, November.
    10. Tiller, Kara Carroll & Thill, Jean-Claude, 2017. "Spatial patterns of landside trade impedance in containerized South American exports," Journal of Transport Geography, Elsevier, vol. 58(C), pages 272-285.
    11. Tian Qiu & John Hudson, 2010. "Private returns to education in urban China," Economic Change and Restructuring, Springer, vol. 43(2), pages 131-150, May.
    12. Gabela, Julio Gustavo Fournier, 2020. "On the accuracy of gravity-RAS approaches used for inter-regional trade estimation: evidence using the 2005 inter-regional input–output table of Japan," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 32(4), pages 521-539.
    13. Pratt, Matthew D. & Wright, Jim A. & Cockings, Samantha & Sterland, Iain, 2014. "Delineating retail conurbations: A rules-based algorithmic approach," Journal of Retailing and Consumer Services, Elsevier, vol. 21(5), pages 667-675.
    14. repec:elg:eechap:14395_23 is not listed on IDEAS
    15. Stanley Frederick W. T. Lim & Elliot Rabinovich & Sungho Park & Minha Hwang, 2021. "Shopping Activity at Warehouse Club Stores and Its Competitive and Network Density Implications," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 28-46, January.
    16. J. Elhorst & Jan Oosterhaven, 2006. "Forecasting the impact of transport improvements on commuting and residential choice," Journal of Geographical Systems, Springer, vol. 8(1), pages 39-59, March.
    17. Bojesen, M. & Birkin, M. & Clarke, G., 2014. "Spatial competition for biogas production using insights from retail location models," Energy, Elsevier, vol. 68(C), pages 617-628.
    18. Jarno Hoekman & Koen Frenken & Frank Oort, 2009. "The geography of collaborative knowledge production in Europe," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(3), pages 721-738, September.
    19. Yin, Ping & Lin, Zhibin & Prideaux, Bruce, 2019. "The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin," Journal of Transport Geography, Elsevier, vol. 80(C).
    20. Xu Wang & Hong Fang & Fang Zhang & Siran Fang, 2018. "The Spatial Analysis of Regional Innovation Performance and Industry-University-Research Institution Collaborative Innovation—An Empirical Study of Chinese Provincial Data," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    21. Chi Chiang & Tsui-Yii Shih, 2015. "Establishment Decision of Experience Stores: Insights into Marketing Effect," Journal of Social Science Studies, Macrothink Institute, vol. 2(1), pages 165-185, January.
    22. Mengyao Ren & Yaoyu Lin & Meihan Jin & Zhongyuan Duan & Yongxi Gong & Yu Liu, 2020. "Examining the effect of land-use function complementarity on intra-urban spatial interactions using metro smart card records," Transportation, Springer, vol. 47(4), pages 1607-1629, August.
    23. Mattia Cai, 2021. "Doubly constrained gravity models for interregional trade estimation," Papers in Regional Science, Wiley Blackwell, vol. 100(2), pages 455-474, April.
    24. Lee, Sang-Hyun & Choi, Jin-Yong & Yoo, Seung-Hwan & Oh, Yun-Gyeong, 2013. "Evaluating spatial centrality for integrated tourism management in rural areas using GIS and network analysis," Tourism Management, Elsevier, vol. 34(C), pages 14-24.
    25. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.

    More about this item

    Keywords

    Spatial interaction; gravity models; entropy; probabilistic; spatial structure; neural networks;
    All these keywords.

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General

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