A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps.
v 2.1
18-12-2013
Check out code and latest version at GitHub
Retrieving base maps from Google with gmap function in package dismo
googleVis: visualise data in a web browser using Google Visualisation API
3. SPATIAL VECTOR DATA (points, lines, polygons)
R is great not only for doing statistics, but also for many other tasks, including GIS analysis and working with spatial data. For instance, R is capable of doing wonderful maps such as this or this. In this tutorial I will show some basic GIS functionality in R.
library(sp) # classes for spatial data
library(raster) # grids, rasters
library(rasterVis) # raster visualisation
library(maptools)
library(rgeos)
# and their dependencies
There are many other useful packages, e.g. check CRAN Spatial Task View. Some of them will be used below.
gmap function in package dismo Some examples:
Getting maps for countries:
library(dismo)
mymap <- gmap("France") # choose whatever country
plot(mymap)
Choose map type:
mymap <- gmap("France", type = "satellite")
plot(mymap)
Choose zoom level:
mymap <- gmap("France", type = "satellite", exp = 3)
plot(mymap)
Save the map as a file in your working directory for future use
mymap <- gmap("France", type = "satellite", filename = "France.gmap")
Now get a map for a region drawn at hand
mymap <- gmap("Europe")
plot(mymap)
select.area <- drawExtent()
# now click 2 times on the map to select your region
mymap <- gmap(select.area)
plot(mymap)
# See ?gmap for many other possibilities
RgoogleMaps: Map your data onto Google Map tiles library(RgoogleMaps)
Get base maps from Google (a file will be saved in your working directory)
newmap <- GetMap(center = c(36.7, -5.9), zoom = 10, destfile = "newmap.png",
maptype = "satellite")
# Now using bounding box instead of center coordinates:
newmap2 <- GetMap.bbox(lonR = c(-5, -6), latR = c(36, 37), destfile = "newmap2.png",
maptype = "terrain")
# Try different maptypes
newmap3 <- GetMap.bbox(lonR = c(-5, -6), latR = c(36, 37), destfile = "newmap3.png",
maptype = "satellite")
Now plot data onto these maps, e.g. these 3 points
PlotOnStaticMap(lat = c(36.3, 35.8, 36.4), lon = c(-5.5, -5.6, -5.8), zoom = 10,
cex = 4, pch = 19, col = "red", FUN = points, add = F)
googleVis: visualise data in a web browser using Google Visualisation API library(googleVis)
Run demo(googleVis) to see all the possibilities
data(Exports) # a simple data frame
Geo <- gvisGeoMap(Exports, locationvar="Country", numvar="Profit",
options=list(height=400, dataMode='regions'))
plot(Geo)
Using print(Geo) we can get the HTML code to embed the map in a web page!
data(Andrew)
M1 <- gvisMap(Andrew, "LatLong", "Tip",
options=list(showTip=TRUE, showLine=F, enableScrollWheel=TRUE,
mapType='satellite', useMapTypeControl=TRUE, width=800,height=400))
plot(M1)
RWorldMap: mapping global data Some examples
library(rworldmap)
newmap <- getMap(resolution = "coarse") # different resolutions available
plot(newmap)
mapCountryData()
mapCountryData(mapRegion = "europe")
mapGriddedData()
mapGriddedData(mapRegion = "europe")
Let's create an example dataset: retrieve occurrence data for the laurel tree (Laurus nobilis) from the Global Biodiversity Information Facility (GBIF)
library(dismo) # check also the nice 'rgbif' package!
laurus <- gbif("Laurus", "nobilis")
## Laurus nobilis : 2120 occurrences found
## 1-1000-2000-2120
# get data frame with spatial coordinates (points)
locs <- subset(laurus, select = c("country", "lat", "lon"))
head(locs) # a simple data frame with coordinates
## country lat lon
## 1 Spain 36.12 -5.579
## 2 Spain 38.26 -5.207
## 3 Spain 36.11 -5.534
## 4 Spain 36.87 -5.312
## 5 Spain 37.30 -1.918
## 6 Spain 36.10 -5.545
# Discard data with errors in coordinates:
locs <- subset(locs, locs$lat < 90)
So we have got a simple dataframe containing spatial coordinates. Let's make these data explicitly spatial
coordinates(locs) <- c("lon", "lat") # set spatial coordinates
plot(locs)
Important: define geographical projection. Consult the appropriate PROJ.4 description here: http://www.spatialreference.org/
crs.geo <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84") # geographical, datum WGS84
proj4string(locs) <- crs.geo # define projection system of our data
summary(locs)
## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## lon -123.25 145.04
## lat -37.78 59.84
## Is projected: FALSE
## proj4string :
## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0]
## Number of points: 2109
## Data attributes:
## Length Class Mode
## 2109 character character
plot(locs, pch = 20, col = "steelblue")
library(rworldmap)
# library rworldmap provides different types of global maps, e.g:
data(coastsCoarse)
data(countriesLow)
plot(coastsCoarse, add = T)
table(locs$country) # see localities of Laurus nobilis by country
##
## Australia Canada Croatia France Germany
## 2 1 1 1 1
## Greece Ireland Israel Italy Spain
## 5 69 1231 2 206
## Sweden United Kingdom United States
## 2 578 10
locs.gb <- subset(locs, locs$country == "United Kingdom") # select only locs in UK
plot(locs.gb, pch = 20, cex = 2, col = "steelblue")
title("Laurus nobilis occurrences in UK")
plot(countriesLow, add = T)
summary(locs.gb)
## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## lon -6.392 1.772
## lat 49.951 56.221
## Is projected: FALSE
## proj4string :
## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0]
## Number of points: 578
## Data attributes:
## Length Class Mode
## 578 character character
gmap from dismogbmap <- gmap(locs.gb, type = "satellite")
locs.gb.merc <- Mercator(locs.gb) # Google Maps are in Mercator projection.
# This function projects the points to that projection to enable mapping
plot(gbmap)
points(locs.gb.merc, pch = 20, col = "red")
RgoogleMaps
require(RgoogleMaps)
locs.gb.coords <- as.data.frame(coordinates(locs.gb)) # retrieves coordinates
# (1st column for longitude, 2nd column for latitude)
PlotOnStaticMap(lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = 5,
cex = 1.4, pch = 19, col = "red", FUN = points, add = F)
Download base map from Google Maps and plot onto it
map.lim <- qbbox(locs.gb.coords$lat, locs.gb.coords$lon, TYPE = "all") # define region
# of interest (bounding box)
mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype = "satellite")
## [1] "http://maps.google.com/maps/api/staticmap?center=53.086237,-2.30987445&zoom=6&size=640x640&maptype=satellite&format=png32&sensor=true"
# see the file in the wd
PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = NULL,
cex = 1.3, pch = 19, col = "red", FUN = points, add = F)
Using different background (base map)
mymap <- GetMap.bbox(map.lim$lonR, map.lim$latR, destfile = "gmap.png", maptype = "hybrid")
## [1] "http://maps.google.com/maps/api/staticmap?center=53.086237,-2.30987445&zoom=6&size=640x640&maptype=hybrid&format=png32&sensor=true"
PlotOnStaticMap(mymap, lat = locs.gb.coords$lat, lon = locs.gb.coords$lon, zoom = NULL,
cex = 1.3, pch = 19, col = "red", FUN = points, add = F)
googleVis (internet)points.gb <- as.data.frame(locs.gb)
points.gb$latlon <- paste(points.gb$lat, points.gb$lon, sep=":")
map.gb <- gvisMap(points.gb, locationvar="latlon", tipvar="country",
options = list(showTip=T, showLine=F, enableScrollWheel=TRUE,
useMapTypeControl=T, width=1400,height=800))
plot(map.gb)
#print(map.gb) # get HTML suitable for a webpage
plot(gbmap)
mypolygon <- drawPoly() # click on the map to draw a polygon and press ESC when finished
summary(mypolygon) # now you have a spatial polygon! Easy, isn't it?
writeOGR(locs.gb, dsn = "locsgb.kml", layer = "locs.gb", driver = "KML")
newmap <- readOGR("locsgb.kml", layer = "locs.gb")
## OGR data source with driver: KML
## Source: "locsgb.kml", layer: "locs.gb"
## with 578 features and 2 fields
## Feature type: wkbPoint with 2 dimensions
writePointsShape(locs.gb, "locsgb")
gb.shape <- readShapePoints("locsgb.shp")
plot(gb.shape)
Use readShapePoly to read polygon shapefiles, and readShapeLines to read polylines.
See also shapefile in raster package.
spTransform (package sp) will do the projection as long as the original and new projection are correctly specified.
To illustrate, let's project the dataframe with Laurus nobilis coordinates that we obtained above:
summary(locs)
## Object of class SpatialPointsDataFrame
## Coordinates:
## min max
## lon -123.25 145.04
## lat -37.78 59.84
## Is projected: FALSE
## proj4string :
## [+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0]
## Number of points: 2109
## Data attributes:
## Length Class Mode
## 2109 character character
The original coordinates are in lat lon format. Let's define the new desired projection: Lambert Azimuthal Equal Area in this case (look up parameters at http://spatialreference.org)
crs.laea <- CRS("+proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs") # Lambert Azimuthal Equal Area
locs.laea <- spTransform(locs, crs.laea) # spTransform makes the projection
plot(countriesLow) # countries map in geographical projection
country.laea <- spTransform(countriesLow, crs.laea) # project
Let's plot this:
plot(locs.laea, pch = 20, col = "steelblue")
plot(country.laea, add = T)
# define spatial limits for plotting
plot(locs.laea, pch = 20, col = "steelblue", xlim = c(1800000, 3900000), ylim = c(1e+06,
3e+06))
plot(country.laea, add = T)
The getData function from the dismo package will easily retrieve climate data, elevation, administrative boundaries, etc. Check also the excellent rWBclimate package by rOpenSci with additional functionality.
tmin <- getData("worldclim", var = "tmin", res = 10) # this will download
# global data on minimum temperature at 10' resolution
tmin1 <- raster(paste(getwd(), "/wc10/tmin1.bil", sep = "")) # Tmin for January
Easy! The raster function reads many different formats, including Arc ASCII grids or netcdf files (see raster help). And values are stored on disk instead of memory! (useful for large rasters)
fromDisk(tmin1)
## [1] TRUE
Let's examine the raster layer:
tmin1 <- tmin1/10 # Worldclim temperature data come in decimal degrees
tmin1 # look at the info
## class : RasterLayer
## dimensions : 900, 2160, 1944000 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## data source : in memory
## names : tmin1
## values : -54.7, 26.6 (min, max)
plot(tmin1)
A raster stack is collection of many raster layers with the same projection, spatial extent and resolution. Let's collect several raster files from disk and read them as a single raster stack:
library(gtools)
file.remove(paste(getwd(), "/wc10/", "tmin_10m_bil.zip", sep = ""))
## [1] TRUE
list.ras <- mixedsort(list.files(paste(getwd(), "/wc10/", sep = ""), full.names = T,
pattern = ".bil"))
list.ras # I have just collected a list of the files containing monthly temperature values
## [1] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin1.bil"
## [2] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin2.bil"
## [3] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin3.bil"
## [4] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin4.bil"
## [5] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin5.bil"
## [6] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin6.bil"
## [7] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin7.bil"
## [8] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin8.bil"
## [9] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin9.bil"
## [10] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin10.bil"
## [11] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin11.bil"
## [12] "C:/Users/FRS/Dropbox/R.scripts/my.Rcode/R-GIS tutorial/wc10/tmin12.bil"
tmin.all <- stack(list.ras)
tmin.all
## class : RasterStack
## dimensions : 900, 2160, 1944000, 12 (nrow, ncol, ncell, nlayers)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs
## names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12
## min values : -547, -525, -468, -379, -225, -170, -171, -178, -192, -302, -449, -522
## max values : 266, 273, 277, 283, 295, 312, 311, 312, 300, 268, 267, 268
tmin.all <- tmin.all/10
plot(tmin.all)
A rasterbrick is similar to a raster stack (i.e. multiple layers with the same extent and resolution), but all the data must be stored in a single file on disk.
tmin.brick <- brick(tmin.all) # creates rasterbrick
Crop raster manually (drawing region of interest):
plot(tmin1)
newext <- drawExtent() # click twice on the map to select the region of interest
tmin1.c <- crop(tmin1, newext)
plot(tmin1.c)
Alternatively, provide coordinates for the limits of the region of interest:
newext <- c(-10, 10, 30, 50)
tmin1.c <- crop(tmin1, newext)
plot(tmin1.c)
tmin.all.c <- crop(tmin.all, newext)
plot(tmin.all.c)
crs.geo # defined above
## CRS arguments:
## +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
projection(tmin1.c) <- crs.geo
projection(tmin.all.c) <- crs.geo
tmin1.c # notice info at coord.ref.
## class : RasterLayer
## dimensions : 120, 120, 14400 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : in memory
## names : tmin1
## values : -12.3, 10.3 (min, max)
Use projectRaster function:
tmin1.proj <- projectRaster(tmin1.c, crs = "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6378137 +units=m +no_defs") # can also use a template raster, see ?projectRaster
tmin1.proj # notice info at coord.ref.
## class : RasterLayer
## dimensions : 132, 134, 17688 (nrow, ncol, ncell)
## resolution : 18600, 24200 (x, y)
## extent : -1243395, 1249005, 3372876, 6567276 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +a=6378137 +b=6378137 +units=m +no_defs
## data source : in memory
## names : tmin1
## values : -11.59, 10.3 (min, max)
plot(tmin1.proj)
Different plotting functions:
histogram(tmin1.c)
pairs(tmin.all.c)
persp(tmin1.c)
contour(tmin1.c)
contourplot(tmin1.c)
levelplot(tmin1.c)
# plot3D(tmin1.c)
bwplot(tmin.all.c)
densityplot(tmin1.c)
Moran(tmin1.c) # global Moran's I
## [1] 0.9099
tmin1.Moran <- MoranLocal(tmin1.c)
plot(tmin1.Moran)
Use extract function:
head(locs) # we'll obtain tmin values for our points
## country
## 1 Spain
## 2 Spain
## 3 Spain
## 4 Spain
## 5 Spain
## 6 Spain
projection(tmin1) <- crs.geo
locs$tmin1 <- extract(tmin1, locs) # raster values
# are incorporated to the dataframe
head(locs)
## country tmin1
## 1 Spain 6.7
## 2 Spain 2.1
## 3 Spain 6.7
## 4 Spain 4.2
## 5 Spain 6.2
## 6 Spain 6.7
You can also extract values for a given region instead of the whole raster:
plot(tmin1.c)
reg.clim <- extract(tmin1.c, drawExtent()) # click twice to
# draw extent of the region of interest
summary(reg.clim)
Using rasterToPoints:
# rasterToPoints
tminvals <- rasterToPoints(tmin1.c)
head(tminvals)
## x y tmin1
## [1,] -6.4167 49.92 4.2
## [2,] -6.2500 49.92 4.2
## [3,] -5.2500 49.92 2.4
## [4,] 0.5833 49.92 1.0
## [5,] 0.7500 49.92 1.0
## [6,] 0.9167 49.92 1.0
And also, the click function will get values from particular locations in the map
plot(tmin1.c)
click(tmin1.c, n = 3) # click n times in the map to get values
locs2ras <- rasterize(locs.gb, tmin1, field = rep(1, nrow(locs.gb)))
locs2ras
## class : RasterLayer
## dimensions : 900, 2160, 1944000 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -180, 180, -60, 90 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : in memory
## names : layer
## values : 1, 1 (min, max)
plot(locs2ras, xlim = c(-10, 10), ylim = c(45, 60), legend = F)
data(wrld_simpl)
plot(wrld_simpl, add = T)
Use aggregate function:
tmin1.lowres <- aggregate(tmin1.c, fact = 2, fun = mean)
tmin1.lowres
## class : RasterLayer
## dimensions : 60, 60, 3600 (nrow, ncol, ncell)
## resolution : 0.3333, 0.3333 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : in memory
## names : tmin1
## values : -10.57, 10.1 (min, max)
tmin1.c # compare
## class : RasterLayer
## dimensions : 120, 120, 14400 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : in memory
## names : tmin1
## values : -12.3, 10.3 (min, max)
par(mfcol = c(1, 2))
plot(tmin1.c, main = "original")
plot(tmin1.lowres, main = "low resolution")
xy <- data.frame(xyFromCell(tmin1.lowres, 1:ncell(tmin1.lowres))) # get raster cell coordinates
head(xy)
## x y
## 1 -9.833 49.83
## 2 -9.500 49.83
## 3 -9.167 49.83
## 4 -8.833 49.83
## 5 -8.500 49.83
## 6 -8.167 49.83
vals <- getValues(tmin1.lowres)
library(fields)
spline <- Tps(xy, vals) # thin plate spline
intras <- interpolate(tmin1.c, spline)
intras # note new resolution
## class : RasterLayer
## dimensions : 120, 120, 14400 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : in memory
## names : layer
## values : -10.43, 13.16 (min, max)
plot(intras)
intras <- mask(intras, tmin1.c) # mask to land areas only
plot(intras)
title("Interpolated raster")
See spatial_sync_raster function from spatial.tools package.
Download elevation data from internet:
elevation <- getData("alt", country = "ESP")
Some quick maps:
x <- terrain(elevation, opt = c("slope", "aspect"), unit = "degrees")
plot(x)
slope <- terrain(elevation, opt = "slope")
aspect <- terrain(elevation, opt = "aspect")
hill <- hillShade(slope, aspect, 40, 270)
plot(hill, col = grey(0:100/100), legend = FALSE, main = "Spain")
plot(elevation, col = rainbow(25, alpha = 0.35), add = TRUE)
Saving raster to file:
writeRaster(tmin1.c, filename = "tmin1.c.grd")
## class : RasterLayer
## dimensions : 120, 120, 14400 (nrow, ncol, ncell)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : C:\Users\FRS\Dropbox\R.scripts\my.Rcode\R-GIS tutorial\tmin1.c.grd
## names : tmin1
## values : -12.3, 10.3 (min, max)
writeRaster(tmin.all.c, filename = "tmin.all.grd")
## class : RasterBrick
## dimensions : 120, 120, 14400, 12 (nrow, ncol, ncell, nlayers)
## resolution : 0.1667, 0.1667 (x, y)
## extent : -10, 10, 30, 50 (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0
## data source : C:\Users\FRS\Dropbox\R.scripts\my.Rcode\R-GIS tutorial\tmin.all.grd
## names : tmin1, tmin2, tmin3, tmin4, tmin5, tmin6, tmin7, tmin8, tmin9, tmin10, tmin11, tmin12
## min values : -12.3, -12.5, -10.8, -8.6, -4.2, -0.8, 1.8, 1.6, -0.1, -3.3, -8.1, -10.8
## max values : 10.3, 10.8, 12.5, 14.5, 19.7, 24.7, 27.6, 26.7, 22.9, 16.9, 13.7, 11.3
writeRaster can export to many different file types, see help.
Exporting to KML (Google Earth)
tmin1.c <- raster(tmin.all.c, 1)
KML(tmin1.c, file = "tmin1.kml")
KML(tmin.all.c) # can export multiple layers
Some useful packages:
library(spatial)
library(spatstat)
library(spatgraphs)
library(ecespa) # ecological focus
Let's do a quick example with Ripley's K function:
data(fig1)
plot(fig1) # point pattern
data(Helianthemum)
cosa12 <- K1K2(Helianthemum, j = "deadpl", i = "survpl", r = seq(0, 200, le = 201),
nsim = 99, nrank = 1, correction = "isotropic")
## 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
## 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
## 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
## 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
## 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75,
## 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,
## 91, 92, 93, 94, 95, 96, 97, 98, 99.
plot(cosa12$k1k2, lty = c(2, 1, 2), col = c(2, 1, 2), xlim = c(0, 200), main = "survival- death",
ylab = expression(K[1] - K[2]), legend = FALSE)
## lty col key label
## lo 2 2 lo lo(r)
## K1-K2 1 1 K1-K2 K1(r) - K2(r)
## hi 2 2 hi hi(r)
## meaning
## lo lower pointwise envelope of simulations
## K1-K2 differences of Ripley isotropic correction estimate of expression(K[1] - K[2])
## hi upper pointwise envelope of simulations
Some useful packages:
library(gstat)
library(geoR)
library(akima) # for spline interpolation
library(spdep) # dealing with spatial dependence
library(spgrass6) # GRASS
library(RPyGeo) # ArcGis (Python)
library(RSAGA) # SAGA
library(spsextante) # Sextante
library(Metadata) # automatically collates data from online GIS datasets (land cover, pop density, etc) for a given set of coordinates
# library(GeoXp) # Interactive exploratory spatial data analysis
example(columbus)
histomap(columbus, "CRIME")
library(maptools)
# readGPS
library(rangeMapper) # plotting species distributions, richness and traits
# Species Distribution Modelling
library(dismo)
library(biomod2)
library(SDMTools)
library(BioCalc) # computes 19 bioclimatic variables from monthly climatic values (tmin, tmax, prec)