1. Aula 2: Introdução ao sp Pedro Ribeiro de Andrade DSA/CCST/INPE São José dos Campos, 2009 Apresentação baseada em: Pebesma & Bivand. S Classes and Methods for Spatial Data: the sp Package

2. Pacotes  install.packages()  require() basedatasets grDevices graphics grid methods splinesstats stats4 tcltk tools utils KernSmooth MASS Matrix boot class cluster codetools foreign lattice mgcv nlme nnet rpart spatial survival mais de 2000 pacotes no CRAN outros pacotes fora do CRAN

3. Rproject -> CRAN -> Mirror ->TaskView-> Spatial

4. sp: Tipos de Dados Espaciais

5. SpatialPoints xc = 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)

6. 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")]

7. SpatialPointsDataFrame – plot require(lattice) trellis.par.set(sp.theme()) data(meuse) coordinates(meuse)=~x+y spplot(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))

8. SpatialLines l1 = 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"))

9. 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)

10. 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"))

11. 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"),])

12. SpatialPolygonsDataFrame – plot data(meuse.riv) meuse.riv p=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))

13. 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()

14. 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()

15. 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"))

16. Grids e Pixels gt = 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)

17. Grids e Pixels attr = 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)

18. 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"])

19. SpatialGrids require(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))

20. SpatialGrids k100 <- 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)

21. Aula 2: Introdução ao sp Pedro Ribeiro de Andrade DSA/CCST/INPE São José dos Campos, 2009 Apresentação baseada em: Pebesma & Bivand. S Classes and Methods for Spatial Data: the sp Package

22. Projeções: rgdal com PROJ.4 require(rgdal) data(state) states <- data.frame(state.x77, state.center) states -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)