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A spatial filtering specification for the auto-Poisson model

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  • Griffith, Daniel A.

Abstract

The auto-Poisson model describes georeferenced data consisting of counts exhibiting spatial dependence. Its conventional specification is plagued by being restricted to only situations involving negative spatial autocorrelation, and an intractable normalizing constant. Work summarized here accounts for spatial autocorrelation in the mean response specification by incorporating latent map pattern components. Results are reported for seven empirical datasets available in the literature.

Suggested Citation

  • Griffith, Daniel A., 2002. "A spatial filtering specification for the auto-Poisson model," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 245-251, July.
  • Handle: RePEc:eee:stapro:v:58:y:2002:i:3:p:245-251
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    References listed on IDEAS

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    1. Kaiser, Mark S. & Cressie, Noel, 1997. "Modeling Poisson variables with positive spatial dependence," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 423-432, November.
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    Citations

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    Cited by:

    1. Mohamed Amara & Mohamed Ayadi, 2011. "Local Employment Growth in the Coastal Area of Tunisia: A Dynamic Spatial Panel Approach," Working Papers 650, Economic Research Forum, revised 12 Jan 2011.
    2. Daniel Griffith, 2009. "Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows," Journal of Geographical Systems, Springer, vol. 11(2), pages 117-140, June.
    3. repec:eee:regeco:v:67:y:2017:i:c:p:42-49 is not listed on IDEAS
    4. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    5. Lambert, Dayton M. & Brown, Jason P. & Florax, Raymond J.G.M., 2010. "A two-step estimator for a spatial lag model of counts: Theory, small sample performance and an application," Regional Science and Urban Economics, Elsevier, vol. 40(4), pages 241-252, July.
    6. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    7. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989.

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