IDEAS home Printed from
   My bibliography  Save this article

Modelling small area counts in the presence of overdispersion and spatial autocorrelation


  • Haining, Robert
  • Law, Jane
  • Griffith, Daniel


The problems arising when modelling counts of rare events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present or anticipated are considered. Different models are presented for handling inference in this case. The different strategies are implemented using data on offender counts at the enumeration district scale for Sheffield, England and results compared. This example is chosen because previous research suggests that social processes and social composition variables are key to understanding geographical variation in offender counts which will, as a consequence, show evidence of clustering both at the scale of the enumeration district and at larger scales. This in turn leads the analyst to anticipate the presence of overdispersion and spatial autocorrelation. Diagnostic measures are described and different modelling strategies are implemented. The evidence suggests that modelling strategies based on the use of spatial random effects models or models that include spatial filters appear to work well and provide a robust basis for model inference but gaps remain in the methodology that call for further research.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:2923-2937

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Daniel A. Griffith, 2004. "Distributional properties of georeferenced random variables based on the eigenfunction spatial filter," Journal of Geographical Systems, Springer, vol. 6(3), pages 263-288, October.
    2. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric filtering of spatial autocorrelation: the eigenvector approach," Environment and Planning A, Pion Ltd, London, vol. 39(5), pages 1193-1221, May.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Kawachi, Ichiro & Kennedy, Bruce P. & Wilkinson, Richard G., 1999. "Crime: social disorganization and relative deprivation," Social Science & Medicine, Elsevier, vol. 48(6), pages 719-731, March.
    5. Kaiser, Mark S. & Cressie, Noel, 1997. "Modeling Poisson variables with positive spatial dependence," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 423-432, November.
    6. Griffith, Daniel A., 2002. "A spatial filtering specification for the auto-Poisson model," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 245-251, July.
    7. A. Hirschfield & K.J. Bowers, 1997. "The Effect of Social Cohesion on Levels of Recorded Crime in Disadvantaged Areas," Urban Studies, Urban Studies Journal Limited, vol. 34(8), pages 1275-1295, July.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Jane Law & Matthew Quick, 2013. "Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach," Journal of Geographical Systems, Springer, vol. 15(1), pages 89-113, January.
    2. LeSage, James & Banerjee, Sudipto & Fischer, Manfred M. & Congdon, Peter, 2009. "Spatial statistics: Methods, models & computation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2781-2785, June.
    3. Burgard, Jan Pablo & Münnich, Ralf T., 2012. "Modelling over and undercounts for design-based Monte Carlo studies in small area estimation: An application to the German register-assisted census," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2856-2863.
    4. Rashidi, Parinaz & Wang, Tiejun & Skidmore, Andrew & Mehdipoor, Hamed & Darvishzadeh, Roshanak & Ngene, Shadrack & Vrieling, Anton & Toxopeus, Albertus G., 2016. "Elephant poaching risk assessed using spatial and non-spatial Bayesian models," Ecological Modelling, Elsevier, vol. 338(C), pages 60-68.
    5. Anping Chen & Marlon Boarnet & Mark Partridge & Christopher S. Fowler & Jane K. Cover & Rachel Garshick Kleit, 2014. "The Geography Of Fringe Banking," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 688-710, September.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:8:p:2923-2937. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.