A spatial filtering specification for the auto-Poisson model

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Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach (2007, methodology): “A collaborative one-to-one comparison of both nonparametric spatial filtering approaches is given by their architects (Getis and Griffith, 2002).”
Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM) (2006, methodology): “Getis and Griffith (2002) demonstrated the efficiency of two approaches of spatial filter- on site i.”
Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. (2006, background): “Although the issues of normality here may appear as a concern, and if so can be addressed as in Griffith (2002) and Griffith (2004), the Hellinger-transformation provides reliable estimates in variance partitioning (Peres-Neto et al. 2006) and representation in canonical space (Legendre and…”
Modeling Network Autocorrelation in Space–Time Migration Flow Data: An Eigenvector Spatial Filtering Approach (2011, methodology): “However, an eigenvector spatial filtering technique ( Griffith 2000 , 2002 , 2004 ; Tiefelsdorf and Griffith 2007 ) provides an efficient and effective way to handle these components in mixed models.”