landSPDF: Beijing land price data

In HSAR: Hierarchical Spatial Autoregressive Model

Description

The spatial locations (SpatialPointsDataFrame) of the Beijing land price data. It is used to extract the spatial weights matrix among land parcels.

Usage

data(landSPDF)

See Also

landprice, Beijingdistricts

Examples

data(landSPDF)

library(spdep)
plot(landSPDF,col="red",pch=16,cex=0.7)

data(landprice)

library(classInt)
library(RColorBrewer)

# link the variables in the landprice to the spatial data
index.match <- match(landSPDF$obs,landprice$obs)
landSPDF@data <- data.frame(landSPDF@data,landprice[index.match,])

par(mar=c(0,0,0,0))
x <- landSPDF$lnprice
breaks <- classIntervals(x,4,"fisher")$brks
groups <- cut(x,breaks,include.lowest=TRUE,labels=FALSE)
palette <- brewer.pal(4, "Blues")
plot(landSPDF,pch=19,col=palette[groups],cex=0.8)

# extract a spatial weights matrix based on the distances between pairs of land parcels
nb.25 <- spdep::dnearneigh(landSPDF,0,2500)
# to a weights matrix
dist.25 <- spdep::nbdists(nb.25,landSPDF)
dist.25 <- lapply(dist.25,function(x) exp(-0.5 * (x / 2500)^2))
mat.25 <- spdep::nb2mat(nb.25,glist=dist.25,style="W")
W <- as(mat.25,"dgCMatrix")

HSAR documentation built on July 2, 2020, 3:13 a.m.