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Introdução ao pacote sp

Introdução ao pacote sp. Pedro Ribeiro de Andrade DSA/CCST/INPE São José dos Campos, 2012. Apresentação baseada em: Pebesma & Bivand . S Classes and Methods for Spatial Data: the sp Package. Pacotes. KernSmooth MASS Matrix boot class cluster codetools foreign

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Introdução ao pacote sp

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  1. Introdução ao pacote sp Pedro Ribeiro de Andrade DSA/CCST/INPE São José dos Campos, 2012 Apresentação baseada em: Pebesma & Bivand. S Classes and Methods for Spatial Data: the sp Package

  2. Pacotes KernSmooth MASS Matrix boot class cluster codetools foreign lattice mgcv nlme nnet rpart spatial survival base datasetsgrDevices graphicsgrid methods splines stats stats4 tcltktools utils • install.packages() • require() 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 = 5 + rnorm(10), 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) 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[,c("cadmium", "copper")], do.log = T) bubble(meuse,"cadmium", maxsize = 1.5, key.entries = 2^(-1:4)) bubble(meuse[meuse$cadmium > 10,],"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))) Srs1 = Polygons(list(Sr1), "s1") Srs2 = Polygons(list(Sr2), "s2") Srs3 = Polygons(list(Sr3), "s3") 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("s3", "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. Introdução ao pacote sp Pedro Ribeiro de Andrade DSA/CCST/INPE São José dos Campos, 2012 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 <- 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)

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