eire: Eire data sets

In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

Description

The Eire data set has been converted to shapefile format and placed in the etc/shapes directory. The initial data objects are now stored as a SpatialPolygonsDataFrame object, from which the contiguity neighbour list is recreated. For purposes of record, the original data set is retained.

The eire.df data frame has 26 rows and 9 columns. In addition, polygons of the 26 counties are provided as a multipart polylist in eire.polys.utm (coordinates in km, projection UTM zone 30). Their centroids are in eire.coords.utm. The original Cliff and Ord binary contiguities are in eire.nb.

Usage

data(eire)

Format

This data frame contains the following columns:

A

Percentage of sample with blood group A

towns

Towns/unit area

pale

Beyond the Pale 0, within the Pale 1

size

number of blood type samples

ROADACC

arterial road network accessibility in 1961

OWNCONS

percentage in value terms of gross agricultural output of each county consumed by itself

POPCHG

1961 population as percentage of 1926

RETSALE

value of retail sales £000

INCOME

total personal income £000

names

County names

Source

Upton and Fingleton 1985, - Bailey and Gatrell 1995, ch. 1 for blood group data, Cliff and Ord (1973), p. 107 for remaining variables (also after O'Sullivan, 1968). Polygon borders and Irish data sourced from Michael Tiefelsdorf's SPSS Saddlepoint bundle, originally hosted at: http://geog-www.sbs.ohio-state.edu/faculty/tiefelsdorf/GeoStat.htm but no longer available.

Examples

require(maptools)
eire <- readShapePoly(system.file("etc/shapes/eire.shp", package="spdep")[1],
  ID="names", proj4string=CRS("+proj=utm +zone=30 +ellps=airy +units=km"))
eire.nb <- poly2nb(eire)
#data(eire)

summary(eire$A)
brks <- round(fivenum(eire$A), digits=2)
cols <- rev(heat.colors(4))
plot(eire, col=cols[findInterval(eire$A, brks, all.inside=TRUE)])
title(main="Percentage with blood group A in Eire")
legend(x=c(-50, 70), y=c(6120, 6050), leglabs(brks), fill=cols, bty="n")
plot(eire)
plot(eire.nb, coordinates(eire), add=TRUE)
lA <- lag.listw(nb2listw(eire.nb), eire$A)
summary(lA)
moran.test(eire$A, nb2listw(eire.nb))
geary.test(eire$A, nb2listw(eire.nb))
cor(lA, eire$A)
moran.plot(eire$A, nb2listw(eire.nb),
 labels=eire$names)
A.lm <- lm(A ~ towns + pale, data=eire)
summary(A.lm)
res <- residuals(A.lm)
brks <- c(min(res),-2,-1,0,1,2,max(res))
cols <- rev(cm.colors(6))
plot(eire, col=cols[findInterval(res, brks, all.inside=TRUE)])
title(main="Regression residuals")
legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks), fill=cols,
  bty="n")
lm.morantest(A.lm, nb2listw(eire.nb))
lm.morantest.sad(A.lm, nb2listw(eire.nb))
lm.LMtests(A.lm, nb2listw(eire.nb), test="LMerr")

brks <- round(fivenum(eire$OWNCONS), digits=2)
cols <- grey(4:1/5)
plot(eire, col=cols[findInterval(eire$OWNCONS, brks, all.inside=TRUE)])
title(main="Percentage own consumption of agricultural produce")
legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks),
  fill=cols, bty="n")
moran.plot(eire$OWNCONS, nb2listw(eire.nb))
moran.test(eire$OWNCONS, nb2listw(eire.nb))
e.lm <- lm(OWNCONS ~ ROADACC, data=eire)
res <- residuals(e.lm)
brks <- c(min(res),-2,-1,0,1,2,max(res))
cols <- rev(cm.colors(6))
plot(eire, col=cols[findInterval(res, brks, all.inside=TRUE)])
title(main="Regression residuals")
legend(x=c(-50, 70), y=c(6120, 6050), legend=leglabs(brks), fill=cm.colors(6),
  bty="n")
lm.morantest(e.lm, nb2listw(eire.nb))
lm.morantest.sad(e.lm, nb2listw(eire.nb))
lm.LMtests(e.lm, nb2listw(eire.nb), test="LMerr")
print(localmoran.sad(e.lm, eire.nb, select=1:length(slot(eire, "polygons"))))

spdep documentation built on Aug. 19, 2017, 3:01 a.m.