Aula 2:Introdução ao spPedro Ribeiro de AndradeDSA/CCST/INPESão José dos Campos, 2009

Apresentação baseada em:Pebesma & Bivand. S Classes and Methods for Spatial Data: the sp Package

Pacotes

install.packages() require()

base datasetsgrDevices graphicsgrid methods splines stats stats4 tcltktools utils

KernSmooth MASSMatrix bootclass clustercodetools foreignlattice mgcvnlme nnetrpart spatialsurvival

mais de 2000 pacotes no CRAN

outros pacotes fora do CRAN

Rproject -> CRAN -> Mirror ->TaskView-> Spatial

sp: Tipos de Dados Espaciais

SpatialPointsxc = round(runif(10), 2)

yc = round(runif(10), 2)

xy = cbind(xc, yc)xy.sp = SpatialPoints(xy)class(xy.sp)xy.sp[1:3,]xy.sp[1:3]bbox(xy.sp)summary(xy.sp)coordinates(xy.sp)as(xy.sp, "data.frame")plot(xy.sp)

SpatialPointsDataFrame

df = data.frame(ID=paste(1:10), z1 = round(5 + rnorm(10), 2), z2 = 20:29)xy.spdf = SpatialPointsDataFrame(xy, df)xy.spdf = SpatialPointsDataFrame(xy.sp, df)

names(xy.spdf)

coordinates(xy.spdf)

xy.spdf[1:2, ]

xy.spdf[,1]

xy.spdf[,"ID"]

xy.spdf[,c("ID","z2")]

xy.spdf[2:5,c("ID","z2")]

SpatialPointsDataFrame – plotrequire(lattice) trellis.par.set(sp.theme())

data(meuse)coordinates(meuse)=~x+yspplot(meuse)spplot(meuse[,"zinc"], scales=list(draw=T))spplot(meuse[1:100,"zinc"], do.log = T)spplot(meuse[,"zinc"], do.log = T, cuts = 3,

legendEntries = c("low", "intermediate", "high"))spplot(meuse[,c("cadmium", "copper")], do.log = T)bubble(meuse,"cadmium", maxsize = 1.5, key.entries =

2^(-1:4))

SpatialLinesl1 = cbind(c(1, 2, 3), c(3, 2, 2))

l1a = cbind(l1[, 1] + 0.05, l1[, 2] + 0.05)

l2 = cbind(c(1, 2, 3), c(1, 1.5, 1))

Sl1 = Line(l1)

Sl1a = Line(l1a)

Sl2 = Line(l2)

S1 = Lines(list(Sl1, Sl1a), ID = "a")

S2 = Lines(list(Sl2), ID = "b")

Sl = SpatialLines(list(S1, S2))

summary(Sl)

plot(Sl, col = c("red", "blue"))

SpatialLinesDataFrame

df = data.frame(z = c(1, 2), row.names = c("a", "b"))

Sldf = SpatialLinesDataFrame(Sl, data = df)

as.data.frame(Sldf)

as(Sldf, "data.frame")

summary(Sldf)

spplot(Sldf)

SpatialPolygons

Sr1 = Polygon(cbind(c(2, 4, 4, 1, 2), c(2, 3, 5, 4, 2)))

Sr2 = Polygon(cbind(c(5, 4, 2, 5), c(2, 3, 2, 2)))

Sr3 = Polygon(cbind(c(4, 4, 5, 10, 4), c(5, 3, 2, 5, 5)))

Sr4 = Polygon(cbind(c(5, 6, 6, 5, 5), c(4, 4, 3, 3, 4)),

hole = TRUE)

Srs1 = Polygons(list(Sr1), "s1")

Srs2 = Polygons(list(Sr2), "s2")

Srs3 = Polygons(list(Sr3, Sr4), "s34")

SpP = SpatialPolygons(list(Srs1, Srs2, Srs3), 1:3)

plot(SpP)

plot(SpP, col=c("red","blue","green"))

SpatialPolygonsDataFrame

attr = data.frame(a = 1:3, b = 3:1,

row.names = c("s34", "s2", "s1"))

SrDf = SpatialPolygonsDataFrame(SpP, attr)

as(SrDf, "data.frame")

summary(SrDf)

plot(SrDf)

spplot(SrDf)

spplot(SrDf[c("s1","s2"),])

SpatialPolygonsDataFrame – plot

data(meuse.riv)meuse.rivp=Polygon(meuse.riv)P=Polygons(list(p), "meuse.riv")meuse.sr =SpatialPolygons(list(P))rv = list("sp.polygons", meuse.sr, fill = "lightblue")spplot(meuse[,"zinc"], do.log=TRUE, sp.layout=list(rv))

SpatialPolygonsDataFrame – plot

library(maptools)

nc <- readShapePoly(system.file("shapes/sids.shp", package="maptools")[1], proj4string=CRS("+proj=longlat +datum=NAD27"))

summary(nc)

nc2=nc[c(67:71,84:86),]

plot(nc2,asp=1)

invisible(text(getSpPPolygonsLabptSlots(nc), labels=as.character(nc$NAME), cex=0.75))

plot(nc, add=T,asp=1)

box()

SpatialPolygonsDataFrame – plot

spplot(nc[c("SID74", "SID79")])

rrt <- nc$SID74/nc$BIR74

brks <- quantile(rrt, seq(0,1,1/7))

dens <- (2:length(brks))*15

plot(nc, density=dens[findInterval(rrt, brks, all.inside=TRUE)])

box()

S4 – objetos

getSlots("SpatialPoints")slotNames(xy.sp)slot(xy.sp,"bbox")xy.sp@bbox

getSlots("Line")getSlots("Lines")getSlots("SpatialLines")sapply(slot(Sl, "lines"), function(x) slot(x, "ID"))

Grids e Pixelsgt = GridTopology(cellcentre.offset = c(1, 1),

cellsize = c(1, 1), cells.dim = c(3, 4))

grd = SpatialGrid(gt)

summary(grd)

gridparameters(grd)

plot(grd)

pts = expand.grid(x = 1:3, y = 1:4)

grd.pts = SpatialPixels(SpatialPoints(pts))

summary(grd.pts)

grd = as(grd.pts, "SpatialGrid")

summary(grd)

Grids e Pixelsattr = expand.grid(xc = 1:3, yc = 1:3)

grd.attr = data.frame(attr, z1 = 1:9, z2 = 9:1)

coordinates(grd.attr) = ~xc + yc

gridded(grd.attr)

gridded(grd.attr) = TRUE

gridded(grd.attr)

summary(grd.attr)

Pontos ou Matrizes?

fullgrid(grd); fullgrid(grd.pts); fullgrid(grd.attr)

fullgrid(grd.pts) = TRUE

fullgrid(grd.attr) = TRUE

fullgrid(grd.pts)

fullgrid(grd.attr)

fullgrid(grd.attr) = FALSE

image(grd.attr[1:5, "z1"])

fullgrid(grd.attr) = TRUE

image(grd.attr[1])

image(grd.attr["z2"])

SpatialGridsrequire(splancs)

data(bodmin)

b.xy <- coordinates(bodmin[1:2])

r = apply(bodmin$poly, 2, range)

(r[2,]-r[1,])/0.2

grd1 <- GridTopology(cellcentre.offset=c(-5.2, -11.5), cellsize=c(0.2, 0.2), cells.dim=c(75,100))

(r[2,]-r[1,])/0.1

grd1 <- GridTopology(cellcentre.offset=c(-5.2, -11.5), cellsize=c(0.1, 0.1), cells.dim=c(150,200))

SpatialGridsk100 <- spkernel2d(b.xy, bodmin$poly, h0=1, grd1)

k150 <- spkernel2d(b.xy, bodmin$poly, h0=1.5, grd1)

k200 <- spkernel2d(b.xy, bodmin$poly, h0=2, grd1)

k250 <- spkernel2d(b.xy, bodmin$poly, h0=2.5, grd1)

df <- data.frame(k100, k150, k200, k250)

kernels <- SpatialGridDataFrame(grd1, data=df)

spplot(kernels, col.regions=terrain.colors(16), cut=15)

image(kernels[1])

contour(kernels[1],add=T, nlev=5)

Aula 2:Introdução ao spPedro Ribeiro de AndradeDSA/CCST/INPESão José dos Campos, 2009

Apresentação baseada em:Pebesma & Bivand. S Classes and Methods for Spatial Data: the sp Package

Projeções: rgdal com PROJ.4

require(rgdal)

data(state)

states <- data.frame(state.x77, state.center)

states <- states[states$x > -121,]

coordinates(states) <- c("x", "y")

proj4string(states) <- CRS("+proj=longlat +ellps=clrk66")

summary(states)

state.merc <- spTransform(states, CRS=CRS("+proj=merc +ellps=GRS80"))

summary(state.merc)