SpatEntropy (version 2.2-4)

turin: Turin urban data.

Description

A lattice dataset with Turin's Urban Morphological Zones (UMZ, see EEA, 2011).

Usage

turin

Format

A matrix with 111 rows and 113 columns. Values are either 0 (non-urban) or 1 (urban). Pixels outside the administrative borders are classified as NA.

Details

This raster/pixel/lattice dataset comes from the EU CORINE Land Cover project (EEA, 2011) and is dated 2011. It is the result of classifying the original land cover data into urbanised and non-urbanised zones, known as 'Urban Morphological Zones' (UMZ, see EEA, 2011). UMZ data are useful to identify shapes and patterns of urban areas, and thus to detect what is known as urban sprawl. Turin's metropolitan area is extracted from the European dataset and is composed by the municipality of Turin and the surrounding municipalities: Beinasco, Venaria Reale, San Mauro Torinese, Grugliasco, Borgaro Torinese, Collegno, Pecetto Torinese, Pino Torinese, Moncalieri, Nichelino, Settimo Torinese, Baldissero Torinese, Rivoli, Orbassano. The dataset is made of 111x113 pixels of size 250x250 metres.

Examples

data(turin)
#plot(as.im(turin), main="", col=gray(c(0.8,0)), ribbon=FALSE)

#shannon's entropy
shannon(turin)

#shannon's entropy of Z (urban/non-urban pairs)
shannonZ(turin)

#oneill's entropy
oneill(turin)

#leibovici's entropy only on Collegno's municipality
data(turinTess)
cell.size=250; ncl=ncol(turin); nrw=nrow(turin)
coords=expand.grid(rev(seq(cell.size/2, (nrw*cell.size-cell.size/2), l=nrw)),
                   seq(cell.size/2, (ncl*cell.size-cell.size/2), l=ncl))
data.pp=ppp(x=coords[which(!is.na(c(turin))),2],
            y=coords[which(!is.na(c(turin))),1],
            window=owin(xrange=c(0, ncl*cell.size), yrange=c(0,nrw*cell.size)),
            marks=c(turin)[which(!is.na(c(turin)))])
data=data.pp[turinTess$tiles[[which(turinTess$names=="Collegno")]]]
#plot(data, pch=16, cex=0.4)
outp=leibovici(data, cell.size=250, ccdist=400, verbose=TRUE)

#altieri's entropy only on Collegno's municipality
outp=altieri(data, cell.size=250, distbreak=c(cell.size, 2*cell.size), verbose=TRUE)

#batty's entropy
#on all points, with a random partition in 10 sub-areas
batty.ent=batty(turin, cell.size=250, partition=10)
#plot with partition
data(turinW)
#plot(as.im(turin, W=turinW), main="", col=gray(c(0.8,0)), ribbon=FALSE)
#plot(batty.ent$area.tess, add=TRUE, border=2)

#batty's entropy with a partition based on the administrative areas
data(turinTess)
batty.ent=batty(turin, cell.size=250, partition=turinTess)
#plot(as.im(turin, W=turinW), main="", col=gray(c(0.8,0)), ribbon=FALSE)
#for(i in 1:turinTess$n) plot(turinTess$tiles[[i]], add=TRUE, border=2)

#karlstrom and ceccato's entropy
data(turinW)
KC.ent=karlstrom(turin, cell.size=250, partition=15, neigh=3)
#plot with partition
#plot(as.im(turin, W=turinW), main="", col=gray(c(0.8,0)), ribbon=FALSE)
#plot(KC.ent$area.tess, add=TRUE, border=2)

#karlstrom and ceccato's entropy with a partition based on the administrative areas
data(turinTess)
KC.ent=karlstrom(turin, cell.size=250, partition=turinTess, neigh=5000, method="distance")
#plot(as.im(turin, W=turinW), main="", col=gray(c(0.8,0)), ribbon=FALSE)
#for(i in 1:turinTess$n) plot(turinTess$tiles[[i]], add=TRUE, border=2)