gwss | R Documentation |
This function calculates basic and robust GWSS. This includes geographically weighted means, standard deviations and skew. Robust alternatives include geographically weighted medians, inter-quartile ranges and quantile imbalances. This function also calculates basic geographically weighted covariances together with basic and robust geographically weighted correlations.
gwss(data, summary.locat,vars,kernel="bisquare",adaptive=FALSE, bw,p=2,
theta=0, longlat=F,dMat,quantile=FALSE)
## S3 method for class 'gwss'
print(x, ...)
data |
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp |
summary.locat |
a Spatial*DataFrame object for providing summary locations, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp |
vars |
a vector of variable names to be summarized |
bw |
bandwidth used in the weighting function |
kernel |
function chosen as follows: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise; tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise |
adaptive |
if TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance) |
p |
the power of the Minkowski distance, default is 2, i.e. the Euclidean distance |
theta |
an angle in radians to rotate the coordinate system, default is 0 |
longlat |
if TRUE, great circle distances will be calculated |
dMat |
a pre-specified distance matrix, it can be calculated by the function |
quantile |
if TRUE, median, interquartile range, quantile imbalance will be calculated |
x |
an object of class “gwss”, returned by the function gwss |
... |
arguments passed through (unused) |
A list of class “lss”:
SDF |
a SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object (see package “sp”) with local means,local standard deviations,local variance, local skew,local coefficients of variation, local covariances, local correlations (Pearson's), local correlations (Spearman's), local medians, local interquartile ranges, local quantile imbalances and coordinates. |
... |
other information for reporting |
Binbin Lu binbinlu@whu.edu.cn
Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
Brunsdon C, Fotheringham AS, Charlton ME (2002) Geographically weighted summary statistics - a framework for localised exploratory data analysis. Computers, Environment and Urban Systems 26:501-524
Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stochastic Environmental Research and Risk Assessment 28: 1869-1887
## Not run:
data(EWHP)
data(EWOutline)
head(ewhp)
houses.spdf <- SpatialPointsDataFrame(ewhp[, 1:2], ewhp)
localstats1 <- gwss(houses.spdf, vars = c("PurPrice", "FlrArea"), bw = 50000)
head(data.frame(localstats1$SDF))
localstats1
##A function for mapping data
if(require("RColorBrewer"))
{
quick.map <- function(spdf,var,legend.title,main.title)
{
x <- spdf@data[,var]
cut.vals <- pretty(x)
x.cut <- cut(x,cut.vals)
cut.levels <- levels(x.cut)
cut.band <- match(x.cut,cut.levels)
colors <- brewer.pal(length(cut.levels), "YlOrRd")
colors <- rev(colors)
par(mar=c(1,1,1,1))
plot(ewoutline,col="olivedrab",bg="lightblue1")
title(main.title)
plot(spdf,add=TRUE,col=colors[cut.band],pch=16)
legend("topleft",cut.levels,col=colors,pch=16,bty="n",title=legend.title)
}
quick.map(localstats1$SDF, "PurPrice_LM", "1000's Uk Pounds",
"Geographically Weighted Mean")
par(mfrow = c(1, 2))
quick.map(localstats1$SDF, "PurPrice_LSKe", "Skewness Level", "Local Skewness")
quick.map(localstats1$SDF, "PurPrice_LSD", "1000's Pounds", "Local Standard Deviation")
#Exploring Non-Stationarity of Relationships
quick.map(localstats1$SDF, "Corr_PurPrice.FlrArea", expression(rho),
"Geographically Weighted Pearson Correlation")
#Robust, Quantile Based Local Summary Statistics
localstats2 <- gwss(houses.spdf, vars = c("PurPrice", "FlrArea"),
bw = 50000, quantile = TRUE)
quick.map(localstats2$SDF, "PurPrice_Median", "1000 UK Pounds",
"Geographically Weighted Median House Price")
}
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.