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Spatial Difusion and Commuting Flows

Author

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  • Constanza Fosco

    (IDEAR - Department of Economics, Universidad Católica del Norte - Chile)

Abstract

In this paper, I investigate the effects of short- and long-distance commuting flows on the spatial diffusion of ideas or practices, through a stylized computational model of two spatial units. Located at fixed places within each spatial unit, individuals interact exclusively with their spatial neighbors. Commuters live and work at different locations, therefore, alternate between two different neighborhoods. The expansion diffusion occurs by local contagion: the probability that any individual adopts the diffusion item is increasing on the adopting neighbors she/he observes. Commuting flows affect in a fundamentally way the speed of the diffusion process. Short-distance commuting flows always accelerate the diffusion within a spatial unit. The effect of long-distance commuting flows depends on the spatial distribution of workplaces in the destination city. If commuters’ workplaces are uniformly dispersed, both cities’ adoption dynamics tend to be coupled. If, instead, workplaces are concentrated in some area, the diffusion in the destination city is much more slow paced than in the origin spatial unit.

Suggested Citation

  • Constanza Fosco, 2012. "Spatial Difusion and Commuting Flows," Documentos de Trabajo en Economia y Ciencia Regional 30, Universidad Catolica del Norte, Chile, Department of Economics, revised Sep 2012.
    • Handle: RePEc:cat:dtecon:dt201216
    as

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    File URL: https://sites.google.com/a/ucn.cl/wpeconomia/archivos/WP2012-16.pdf
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    References listed on IDEAS

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    1. Fu, Shihe, 2007. "Smart Cafe Cities: Testing human capital externalities in the Boston metropolitan area," Journal of Urban Economics, Elsevier, vol. 61(1), pages 86-111, January.
    2. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Julien Arino & P. van den Driessche, 2003. "A multi-city epidemic model," Mathematical Population Studies, Taylor & Francis Journals, vol. 10(3), pages 175-193.
    5. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    6. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    7. Floriana Gargiulo & Maxime Lenormand & Sylvie Huet & Omar Baqueiro Espinosa, 2012. "Commuting Network Models: Getting the Essentials," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(2), pages 1-6.
    8. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    9. Paul Almeida & Bruce Kogut, 1999. "Localization of Knowledge and the Mobility of Engineers in Regional Networks," Management Science, INFORMS, vol. 45(7), pages 905-917, July.
    10. Gargiulo, Floriana & Lenormand, Maxime & Huet, Sylvie & Baqueiro Espinosa, Omar, 2012. "Commuting network models: Getting the essentials," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(2).
    11. Piergiuseppe Morone & Richard Taylor, 2010. "Knowledge Diffusion and Innovation," Books, Edward Elgar Publishing, number 13143.
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    More about this item

    Keywords

    Spatial Diffusion; Contagion; Commuting Flows.;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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