Paper Info
Title
Modelling small area counts in the presence of overdispersion and spatial autocorrelation
Abstract
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.
Year
DOI
Venue
2009
10.1016/j.csda.2008.08.014
Computational Statistics & Data Analysis
Keywords
DocType
Volume
modelling count,different strategy,spatial random effects model,different model,spatial autocorrelation,small area count,different modelling strategy,residual spatial autocorrelation,offender count,spatial filter,modelling strategy,spatial filtering,random effects model
Journal
53
Issue
ISSN
Citations 
8
Computational Statistics and Data Analysis
3
PageRank 
References 
Authors
1.01
2
3
Name
Order
Citations
PageRank
Robert Haining131.01
Jane Law294.19
Daniel A. Griffith39123.76