sp
This document shows example images created with objects represented by one of the classes for spatial data in packages sp.
The Meuse data set is loaded using a demo script in package
sp
,
## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"
The North Carolina SIDS (sudden infant death syndrome) data set is
available from package sf
, and is loaded by
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
## Reading layer `nc.gpkg' from data source
## `/github/workspace/pkglib/sf/gpkg/nc.gpkg' using driver `GPKG'
## Simple feature collection with 100 features and 14 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## Geodetic CRS: NAD27
The basic plot
command on a Spatial
object
gives just the geometry, without axes:
axes can be added when axes=TRUE
is given; plot elements
can be added incrementally:
plot(meuse, pch = 1, cex = sqrt(meuse$zinc)/12, axes = TRUE)
v = c(100,200,400,800,1600)
legend("topleft", legend = v, pch = 1, pt.cex = sqrt(v)/12)
plot(meuse.riv, add = TRUE, col = grey(.9, alpha = .5))
For local projection systems, such as in this case
## [1] "+proj=sterea +lat_0=52.1561605555556 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs"
the aspect ratio is set to 1, to make sure that one unit north equals one unit east. Even if the data are in long/lat degrees, an aspect ratio is chosen such that in the middle of the plot one unit north approximates one unit east. For small regions, this works pretty well; also note the degree symbols in the axes tic marks:
## Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
## deprecated. It might return a CRS with a non-EPSG compliant axis order.
which looks different from the plot where one degree north (latitude) equals one degree east (longitude):
Instead of axes with ticks and tick marks, maps often have
graticules, a grid with constant longitude and latitude lines.
sp
provides several helper functions to add graticules,
either in the local reference system, or in long/lat. Here is an example
of the local reference system:
par(mar = c(0, 0, 1, 0))
library(methods) # as
plot(as(meuse, "Spatial"), expandBB = c(.05, 0, 0, 0))
plot(gridlines(meuse), add = TRUE, col = grey(.8))
plot(meuse, add = TRUE)
text(labels(gridlines(meuse)), col = grey(.7))
title("default gridlines with Meuse projected data")
par(mar = c(0, 0, 1, 0))
grd <- gridlines(meuse.longlat)
grd_x <- spTransform(grd, CRS(proj4string(meuse)))
plot(as(meuse, "Spatial"), expandBB = c(.05, 0, 0, 0))
plot(grd_x, add=TRUE, col = grey(.8))
plot(meuse, add = TRUE)
text(labels(grd_x, crs.longlat), col = grey(.7))
## Warning in labels.SpatialLines(grd_x, crs.longlat): this labels method is meant
## to operate on SpatialLines created with sp::gridlines
These lines look pretty straight, because it concerns a small area. For
# demonstrate axis labels with angle, both sides:
maps2sp = function(xlim, ylim, l.out = 100, clip = TRUE) {
stopifnot(require(maps))
m = map(xlim = xlim, ylim = ylim, plot = FALSE, fill = TRUE)
as(st_as_sf(m), "Spatial")
}
par(mar = c(0, 0, 1, 0))
m = maps2sp(c(-100,-20), c(10,55))
## Loading required package: maps
sp = SpatialPoints(rbind(c(-101,9), c(-101,55), c(-19,9), c(-19,55)), CRS("+init=epsg:4326"))
laea = CRS("+proj=laea +lat_0=30 +lon_0=-40")
m.laea = spTransform(m, laea)
sp.laea = spTransform(sp, laea)
plot(as(m.laea, "Spatial"), expandBB = c(.1, 0.05, .1, .1))
plot(m.laea, col = grey(.8), add = TRUE)
gl = gridlines(sp, easts = c(-100,-80,-60,-40,-20), norths = c(20,30,40,50))
gl.laea = spTransform(gl, laea)
plot(gl.laea, add = TRUE)
text(labels(gl.laea, crs.longlat))
## Warning in labels.SpatialLines(gl.laea, crs.longlat): this labels method is
## meant to operate on SpatialLines created with sp::gridlines
## Warning in labels.SpatialLines(gl.laea, crs.longlat, side = 3:4): this labels
## method is meant to operate on SpatialLines created with sp::gridlines
# polar:
par(mar = c(0, 0, 1, 0))
pts=SpatialPoints(rbind(c(-180,-70),c(0,-70),c(180,-89),c(180,-70)), CRS("+init=epsg:4326"))
gl = gridlines(pts, easts = seq(-180,180,20), ndiscr = 100)
polar = CRS("+init=epsg:3031")
plot(spTransform(pts, polar), expandBB = c(.05, 0, .05, 0))
gl.polar = spTransform(gl, polar)
lines(gl.polar)
l = labels(gl.polar, crs.longlat, side = 3)
## Warning in labels.SpatialLines(gl.polar, crs.longlat, side = 3): this labels
## method is meant to operate on SpatialLines created with sp::gridlines
l$pos = NULL # pos is too simple, use adj:
text(l, adj = c(0.5, -0.5), cex = .8)
l = labels(gl.polar, crs.longlat, side = 4)
## Warning in labels.SpatialLines(gl.polar, crs.longlat, side = 4): this labels
## method is meant to operate on SpatialLines created with sp::gridlines
l$srt = 0 # otherwise they end up upside-down
text(l, cex = .8)
title("grid line labels on polar projection, epsg 3031")
par(mar = c(0, 0, 1, 0))
m = maps2sp(xlim = c(-180,180), ylim = c(-90,-70), clip = FALSE)
gl = gridlines(m, easts = seq(-180,180,20))
polar = CRS("+init=epsg:3031")
gl.polar = spTransform(gl, polar)
plot(as(gl.polar, "Spatial"), expandBB = c(.05, 0, .05, 0))
plot(gl.polar, add = TRUE)
plot(spTransform(m, polar), add = TRUE, col = grey(0.8, 0.8))
l = labels(gl.polar, crs.longlat, side = 3)
## Warning in labels.SpatialLines(gl.polar, crs.longlat, side = 3): this labels
## method is meant to operate on SpatialLines created with sp::gridlines
# pos is too simple here, use adj:
l$pos = NULL
text(l, adj = c(0.5, -0.3), cex = .8)
l = labels(gl.polar, crs.longlat, side = 2)
## Warning in labels.SpatialLines(gl.polar, crs.longlat, side = 2): this labels
## method is meant to operate on SpatialLines created with sp::gridlines
The following plot shows polygons with county name as labels at their center point:
par(mar = c(0, 0, 1, 0))
plot(nc)
invisible(text(coordinates(nc), labels=as.character(nc$NAME), cex=0.4))
This plot of a SpatialPolygonsDataFrame
uses grey
shades:
## [1] "AREA" "PERIMETER" "CNTY_" "CNTY_ID" "NAME" "FIPS"
## [7] "FIPSNO" "CRESS_ID" "BIR74" "SID74" "NWBIR74" "BIR79"
## [13] "SID79" "NWBIR79"
rrt <- nc$SID74/nc$BIR74
brks <- quantile(rrt, seq(0,1,1/7))
cols <- grey((length(brks):2)/length(brks))
dens <- (2:length(brks))*3
par(mar = c(0, 0, 1, 0))
plot(nc, col=cols[findInterval(rrt, brks, all.inside=TRUE)])
The following plot shows a SpatialPolygonsDataFrame
,
using line densities
rrt <- nc$SID74/nc$BIR74
brks <- quantile(rrt, seq(0,1,1/7))
cols <- grey((length(brks):2)/length(brks))
dens <- (2:length(brks))*3
par(mar = rep(0,4))
plot(nc, density=dens[findInterval(rrt, brks, all.inside=TRUE)])
Plot/image of a grid file, using base plot methods:
Read this blog post to find out more about the options available (and limitations) for plotting gridded data with base plot methods in sp.
The following plot colours points with a legend in the plotting area and adds scales:
spplot(meuse, "zinc", do.log = TRUE,
key.space=list(x = 0.1, y = 0.95, corner = c(0, 1)),
scales=list(draw = TRUE))
The following plot has coloured points plot with legend in plotting area and scales; it has a non-default number of cuts with user-supplied legend entries:
spplot(meuse, "zinc", do.log = TRUE,
key.space=list(x=0.2,y=0.9,corner=c(0,1)),
scales=list(draw = TRUE), cuts = 3,
legendEntries = c("low", "intermediate", "high"))
The following plot adds a scale bar and north arrow:
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(178600,332490), scale = 500, fill=c("transparent","black"))
text1 = list("sp.text", c(178600,332590), "0")
text2 = list("sp.text", c(179100,332590), "500 m")
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(178750,332000), scale = 400)
spplot(meuse, "zinc", do.log=T,
key.space=list(x=0.1,y=0.93,corner=c(0,1)),
sp.layout=list(scale,text1,text2,arrow),
main = "Zinc (top soil)")
The following plot has north arrow and text outside panels
rv = list("sp.polygons", meuse.riv, fill = "lightblue")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 1)
text1 = list("sp.text", c(180500,329900), "0", which = 1)
text2 = list("sp.text", c(181000,329900), "500 m", which = 1)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(178750,332500), scale = 400)
spplot(meuse["zinc"], do.log = TRUE,
key.space = "bottom",
sp.layout = list(rv, scale, text1, text2),
main = "Zinc (top soil)",
legend = list(right = list(fun = mapLegendGrob(layout.north.arrow()))))
The same plot; north arrow now inside panel, with custom panel function instead of sp.layout
spplot(meuse, "zinc", panel = function(x, y, ...) {
sp.polygons(meuse.riv, fill = "lightblue")
SpatialPolygonsRescale(layout.scale.bar(), offset = c(179900,329600),
scale = 500, fill=c("transparent","black"))
sp.text(c(179900,329700), "0")
sp.text(c(180400,329700), "500 m")
SpatialPolygonsRescale(layout.north.arrow(),
offset = c(178750,332500), scale = 400)
panel.pointsplot(x, y, ...)
},
do.log = TRUE, cuts = 7,
key.space = list(x = 0.1, y = 0.93, corner = c(0,1)),
main = "Top soil zinc concentration (ppm)")
A multi-panel plot, scales + north arrow only in last plot: using the
which
argument in a layout component (if
which=4
was set as list component of sp.layout, the river
would as well be drawn only in that (last) panel)
rv = list("sp.polygons", meuse.riv, fill = "lightblue")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(181300,329800),
scale = 400, which = 4)
cuts = c(.2,.5,1,2,5,10,20,50,100,200,500,1000,2000)
spplot(meuse, c("cadmium", "copper", "lead", "zinc"), do.log = TRUE,
key.space = "right", as.table = TRUE,
sp.layout=list(rv, scale, text1, text2, arrow), # note that rv is up front!
main = "Heavy metals (top soil), ppm", cex = .7, cuts = cuts)
Comparing four kriging varieties in a multi-panel plot with shared scale:
rv = list("sp.polygons", meuse.riv, fill = "blue", alpha = 0.1)
pts = list("sp.points", meuse, pch = 3, col = "grey", alpha = .5)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
library(gstat)
v.ok = variogram(log(zinc)~1, meuse)
ok.model = fit.variogram(v.ok, vgm(1, "Exp", 500, 1))
# plot(v.ok, ok.model, main = "ordinary kriging")
v.uk = variogram(log(zinc)~sqrt(dist), meuse)
uk.model = fit.variogram(v.uk, vgm(1, "Exp", 300, 1))
# plot(v.uk, uk.model, main = "universal kriging")
meuse[["ff"]] = factor(meuse[["ffreq"]])
meuse.grid[["ff"]] = factor(meuse.grid[["ffreq"]])
v.sk = variogram(log(zinc)~ff, meuse)
sk.model = fit.variogram(v.sk, vgm(1, "Exp", 300, 1))
# plot(v.sk, sk.model, main = "stratified kriging")
zn.ok = krige(log(zinc)~1, meuse, meuse.grid, model = ok.model, debug.level = 0)
zn.uk = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, model = uk.model, debug.level = 0)
zn.sk = krige(log(zinc)~ff, meuse, meuse.grid, model = sk.model, debug.level = 0)
zn.id = krige(log(zinc)~1, meuse, meuse.grid, debug.level = 0)
zn = zn.ok
zn[["a"]] = zn.ok[["var1.pred"]]
zn[["b"]] = zn.uk[["var1.pred"]]
zn[["c"]] = zn.sk[["var1.pred"]]
zn[["d"]] = zn.id[["var1.pred"]]
spplot(zn, c("a", "b", "c", "d"),
names.attr = c("ordinary kriging", "universal kriging with dist to river",
"stratified kriging with flood freq", "inverse distance"),
as.table = TRUE, main = "log-zinc interpolation",
sp.layout = list(rv, scale, text1, text2)
)
Reuse these results; universal kriging standard errors; grid plot with point locations and polygon (river):
rv = list("sp.polygons", meuse.riv, fill = "blue", alpha = 0.1)
pts = list("sp.points", meuse, pch = 3, col = "grey", alpha = .7)
spplot(zn.uk, "var1.pred",
sp.layout = list(rv, scale, text1, text2, pts),
main = "log(zinc); universal kriging using sqrt(dist to Meuse)")
zn.uk[["se"]] = sqrt(zn.uk[["var1.var"]])
spplot(zn.uk, "se", sp.layout = list(rv, pts),
main = "log(zinc); universal kriging standard errors")
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(-76,34), scale = 0.5, which = 2)
spplot(nc, c("SID74", "SID79"), names.attr = c("1974","1979"),
colorkey=list(space="bottom"), scales = list(draw = TRUE),
main = "SIDS (sudden infant death syndrome) in North Carolina",
sp.layout = list(arrow), as.table = TRUE)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(-76,34), scale = 0.5, which = 2)
#scale = list("SpatialPolygonsRescale", layout.scale.bar(),
# offset = c(-77.5,34), scale = 1, fill=c("transparent","black"), which = 2)
#text1 = list("sp.text", c(-77.5,34.15), "0", which = 2)
#text2 = list("sp.text", c(-76.5,34.15), "1 degree", which = 2)
# create a fake lines data set:
## multi-panel plot with coloured lines: North Carolina SIDS
spplot(nc, c("SID74","SID79"), names.attr = c("1974","1979"),
colorkey=list(space="bottom"),
main = "SIDS (sudden infant death syndrome) in North Carolina",
sp.layout = arrow, as.table = TRUE)
Bubble plots for cadmium and zinc:
b1 = bubble(meuse, "cadmium", maxsize = 1.5, main = "cadmium concentrations (ppm)",
key.entries = 2^(-1:4))
b2 = bubble(meuse, "zinc", maxsize = 1.5, main = "zinc concentrations (ppm)",
key.entries = 100 * 2^(0:4))
print(b1, split = c(1,1,2,1), more = TRUE)
print(b2, split = c(2,1,2,1), more = FALSE)
Factor variables using spplot
:
# create two dummy factor variables, with equal labels:
set.seed(31)
nc$f = factor(sample(1:5, 100,replace = TRUE),labels=letters[1:5])
nc$g = factor(sample(1:5, 100,replace = TRUE),labels=letters[1:5])
library(RColorBrewer)
## Two (dummy) factor variables shown with qualitative colour ramp; degrees in axes
spplot(nc, c("f","g"), col.regions=brewer.pal(5, "Set3"), scales=list(draw = TRUE))
(it is recommended to migrate to sf
, and use
geom_sf()
for this)
R packages leaflet and mapview provide interactive, browser-based maps building upon the leaflet javascript library. Example with points, grid and polygons follow:
Mapview also allows grids of view that are synced
m1 <- mapview(meuse, zcol = "soil", burst = TRUE, legend = TRUE)
m2 <- mapview(meuse, zcol = "lead", legend = TRUE)
m3 <- mapview(meuse, zcol = "landuse", map.types = "Esri.WorldImagery", legend = TRUE)
m4 <- mapview(meuse, zcol = "dist.m", legend = TRUE)
sync(m1, m2, m3, m4) # 4 panels synchronised
# latticeView(m1, m2, m3, m4) # 4 panels
more examples are found here.
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] mapview_2.11.2 RColorBrewer_1.1-3 gstat_2.1-2 maps_3.4.2
## [5] sf_1.0-18 raster_3.6-30 sp_2.1-4
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 class_7.3-22 KernSmooth_2.23-24
## [4] lattice_0.22-6 digest_0.6.37 magrittr_2.0.3
## [7] evaluate_1.0.1 grid_4.4.1 fastmap_1.2.0
## [10] jsonlite_1.8.9 e1071_1.7-16 DBI_1.2.3
## [13] crosstalk_1.2.1 scales_1.3.0 brew_1.0-10
## [16] codetools_0.2-20 jquerylib_0.1.4 cli_3.6.3
## [19] rlang_1.1.4 leafpop_0.1.0 units_0.8-5
## [22] munsell_0.5.1 intervals_0.15.5 base64enc_0.1-3
## [25] cachem_1.1.0 yaml_2.3.10 FNN_1.1.4.1
## [28] tools_4.4.1 uuid_1.2-1 colorspace_2.1-1
## [31] spacetime_1.3-2 buildtools_1.0.0 R6_2.5.1
## [34] png_0.1-8 stats4_4.4.1 zoo_1.8-12
## [37] proxy_0.4-27 lifecycle_1.0.4 classInt_0.4-10
## [40] leaflet_2.2.2 leaflet.providers_2.0.0 htmlwidgets_1.6.4
## [43] terra_1.7-83 bslib_0.8.0 glue_1.8.0
## [46] Rcpp_1.0.13 systemfonts_1.1.0 xfun_0.48
## [49] highr_0.11 sys_3.4.3 knitr_1.48
## [52] farver_2.1.2 htmltools_0.5.8.1 svglite_2.1.3
## [55] leafem_0.2.3 rmarkdown_2.28 xts_0.14.1
## [58] maketools_1.3.1 satellite_1.0.5 compiler_4.4.1