Introduction 1

The Moran eigenvector approach involved the spatial patterns represented by maps of eigenvectors; by choosing suitable orthogonal patterns and adding them to a linear or generalised linear model, the spatial dependence present in the residuals can be moved into the model.

It uses brute force to search the set of eigenvectors of the matrix $$\mathbf{M W M}$$, where

$\mathbf{M} = \mathbf{I} - \mathbf{X}(\mathbf{X}^{\rm T} \mathbf{X})^{-1}\mathbf{X}^{\rm T}$ is a symmetric and idempotent projection matrix and $$\mathbf{W}$$ are the spatial weights. In the spatial lag form of SpatialFiltering and in the GLM ME form below, $$\mathbf{X}$$ is an $$n$$-vector of ones, that is the intercept only.

In its general form, SpatialFiltering chooses the subset of the $$n$$ eigenvectors that reduce the residual spatial autocorrelation in the error of the model with covariates. The lag form adds the covariates in assessment of which eigenvectors to choose, but does not use them in constructing the eigenvectors. SpatialFiltering was implemented and contributed by Yongwan Chun and Michael Tiefelsdorf, and is presented in Tiefelsdorf and Griffith (2007); ME is based on Matlab code by Pedro Peres-Neto and is discussed in Dray, Legendre, and Peres-Neto (2006) and Griffith and Peres-Neto (2006).

library(spdep)
require("sf", quietly=TRUE)
if (packageVersion("spData") >= "2.3.2") {
NY8 <- sf::st_read(system.file("shapes/NY8_utm18.gpkg", package="spData"))
} else {
NY8 <- sf::st_read(system.file("shapes/NY8_bna_utm18.gpkg", package="spData"))
sf::st_crs(NY8) <- "EPSG:32618"
NY8$Cases <- NY8$TRACTCAS
}
## Reading layer sf_bna2_utm18' from data source
##   /home/rsb/lib/r_libs/spData/shapes/NY8_bna_utm18.gpkg' using driver GPKG'
## Simple feature collection with 281 features and 12 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 357628 ymin: 4649538 xmax: 480360.3 ymax: 4808317
## Projected CRS: UTM Zone 18, Northern Hemisphere
NY_nb <- read.gal(system.file("weights/NY_nb.gal", package="spData"), override.id=TRUE)
library(spatialreg)
nySFE <- SpatialFiltering(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8, nb=NY_nb, style="W", verbose=FALSE)
nylmSFE <- lm(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME+fitted(nySFE), data=NY8)
summary(nylmSFE)
##
## Call:
## lm(formula = Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME + fitted(nySFE),
##     data = NY8)
##
## Residuals:
##     Min      1Q  Median      3Q     Max
## -1.5184 -0.3523 -0.0105  0.3221  3.1964
##
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -0.51728    0.14606  -3.542 0.000469 ***
## PEXPOSURE           0.04884    0.03230   1.512 0.131717
## PCTAGE65P           3.95089    0.55776   7.083 1.25e-11 ***
## PCTOWNHOME         -0.56004    0.15688  -3.570 0.000423 ***
## fitted(nySFE)vec13 -2.09397    0.60534  -3.459 0.000630 ***
## fitted(nySFE)vec44 -2.24003    0.60534  -3.700 0.000261 ***
## fitted(nySFE)vec6   1.02979    0.60534   1.701 0.090072 .
## fitted(nySFE)vec38  1.29282    0.60534   2.136 0.033613 *
## fitted(nySFE)vec20  1.10064    0.60534   1.818 0.070150 .
## fitted(nySFE)vec14 -1.05105    0.60534  -1.736 0.083662 .
## fitted(nySFE)vec75  1.90600    0.60534   3.149 0.001826 **
## fitted(nySFE)vec21 -1.06331    0.60534  -1.757 0.080138 .
## fitted(nySFE)vec36  1.17861    0.60534   1.947 0.052578 .
## fitted(nySFE)vec61 -1.08582    0.60534  -1.794 0.073986 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6053 on 267 degrees of freedom
## Multiple R-squared:  0.3401, Adjusted R-squared:  0.308
## F-statistic: 10.58 on 13 and 267 DF,  p-value: < 2.2e-16
nylm <- lm(Z~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8)
anova(nylm, nylmSFE)
## Analysis of Variance Table
##
## Model 1: Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME
## Model 2: Z ~ PEXPOSURE + PCTAGE65P + PCTOWNHOME + fitted(nySFE)
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)
## 1    277 119.619
## 2    267  97.837 10    21.782 5.9444 3.988e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Since the SpatialFiltering approach does not allow weights to be used, we see that the residual autocorrelation of the original linear model is absorbed, or â€˜whitenedâ€™ by the inclusion of selected eigenvectors in the model, but that the covariate coefficients change little. The addition of these eigenvectors â€“ each representing an independent spatial pattern â€“ relieves the residual autocorrelation, but otherwise makes few changes in the substantive coefficient values.

The ME function also searches for eigenvectors from the spatial lag variant of the underlying model, but in a GLM framework. The criterion is a permutation bootstrap test on Moranâ€™s $$I$$ for regression residuals, and in this case, because of the very limited remaining spatial autocorrelation, is set at $$\alpha = 0.5$$. Even with this very generous stopping rule, only few eigenvectors are chosen; their combined contribution only just improves the fit of the GLM model.

NYlistwW <- nb2listw(NY_nb, style = "W")
set.seed(111)
nyME <- ME(Cases~PEXPOSURE+PCTAGE65P+PCTOWNHOME, data=NY8, offset=log(POP8), family="poisson", listw=NYlistwW, alpha=0.46)
nyME
##   Eigenvector ZI pr(ZI)
## 0          NA NA   0.31
## 1          24 NA   0.46
## 2         164 NA   0.41
## 3         113 NA   0.44
## 4          60 NA   0.50
NY8$eigen_1 <- fitted(nyME)[,1] NY8$eigen_2 <- fitted(nyME)[,2]
#gry <- brewer.pal(9, "Greys")[-1]
plot(NY8[,c("eigen_1", "eigen_2")])`