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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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    Cited by:

    1. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
    2. Buendía Azorín, José Daniel & Sánchez de la Vega, María del Mar, 2017. "Output growth thresholds for the creation of employment and the reduction of unemployment: A spatial analysis with panel data from the Spanish provinces, 2000–2011," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 42-49.
    3. Isabel Proença & Ludgero Glórias, 2021. "Revisiting the Spatial Autoregressive Exponential Model for Counts and Other Nonnegative Variables, with Application to the Knowledge Production Function," Sustainability, MDPI, vol. 13(5), pages 1-22, March.
    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. Lan Hu & Yongwan Chun & Daniel A. Griffith, 2020. "Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010," Journal of Geographical Systems, Springer, vol. 22(3), pages 291-308, July.
    7. Shawn Banasick & Ge Lin & Robert Hanham, 2009. "Deviance Residual Moran's I Test and Its Application to Spatial Clusters of Small Manufacturing Firms in Japan," International Regional Science Review, , vol. 32(1), pages 3-18, January.
    8. 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.
    9. Oshan, Taylor M., 2022. "Spatial Interaction Modeling," OSF Preprints m3ah8, Center for Open Science.
    10. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    11. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    12. 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.
    13. 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.
    14. 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.
    15. 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, December.
    16. R. Kelley Pace & James P. Lesage & Shuang Zhu, 2013. "Interpretation and Computation of Estimates from Regression Models using Spatial Filtering," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 352-369, September.

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