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Log-Linear Models with Dependent Spatial Data

Author

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  • B Fingleton

    (Department of Humanities, Cambridgeshire College of Arts and Technology, Cambridge, England)

Abstract

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

Suggested Citation

  • B Fingleton, 1983. "Log-Linear Models with Dependent Spatial Data," Environment and Planning A, , vol. 15(6), pages 801-813, June.
    • Handle: RePEc:sae:envira:v:15:y:1983:i:6:p:801-813
    DOI: 10.1068/a150801
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    Cited by:

    1. Julie Le Gallo & Coro Chasco, 2009. "Spatial analysis of urban growth in Spain, 1900–2001," Studies in Empirical Economics, in: Giuseppe Arbia & Badi H. Baltagi (ed.), Spatial Econometrics, pages 59-80, Springer.
    2. Frank Bickenbach & Eckhardt Bode, 2003. "Evaluating the Markov Property in Studies of Economic Convergence," International Regional Science Review, , vol. 26(3), pages 363-392, July.
    3. Fischer, M.M. & Nijkamp, P., 1985. "Explanatory discrete spatial data and choice analysis : a state-of-the-art review," Serie Research Memoranda 0006, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    4. Julie Le Gallo, 2004. "Space-Time Analysis of GDP Disparities among European Regions: A Markov Chains Approach," International Regional Science Review, , vol. 27(2), pages 138-163, April.
    5. Bernard Fingleton, 1999. "Estimates of Time to Economic Convergence: An Analysis of Regions of the European Union," International Regional Science Review, , vol. 22(1), pages 5-34, April.
    6. Tonglin Zhang & Ge Lin, 2008. "Identification of local clusters for count data: a model-based Moran's I test," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 293-306.

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