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Flow autocorrelation: a dyadic approach

Author

Listed:
  • F. Bavaud

    (University of Lausanne)

  • M. Kordi

    (University of Lausanne)

  • C. Kaiser

    (University of Lausanne)

Abstract

The paper proposes and investigates a new index of flow autocorrelation, based upon a generalization of Moran’s I, and made of two ingredients. The first one consists of a family of spatial weights matrix, the exchange matrix, possessing a freely adjustable parameter interpretable as the age of the network, and controlling for the distance decay range. The second one is a matrix of chi-square dissimilarities between outgoing or incoming flows. Flows have to be adjusted, that is their diagonal part must first be calibrated from their off-diagonal part, thanks to a new iterative procedure procedure aimed at making flows as independent as possible. Commuter flows in Western Switzerland as well as migration flows in Western US illustrate the statistical testing of flow autocorrelation, as well as the computation, mapping and interpretation of local indicators of flow autocorrelation. We prove the present dyadic formalism to be equivalent to the “origin-based” tetradic formalism found in alternative studies of flow autocorrelation.

Suggested Citation

  • F. Bavaud & M. Kordi & C. Kaiser, 2018. "Flow autocorrelation: a dyadic approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 95-111, July.
    • Handle: RePEc:spr:anresc:v:61:y:2018:i:1:d:10.1007_s00168-018-0860-y
    DOI: 10.1007/s00168-018-0860-y
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    References listed on IDEAS

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    Cited by:

    1. Yingxia Pu & Xinyi Zhao & Guangqing Chi & Jin Zhao & Fanhua Kong, 2019. "A spatial dynamic panel approach to modelling the space-time dynamics of interprovincial migration flows in China," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(31), pages 913-948.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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