plotKML: Methods for plotting results of spatial analysis in Google...
In plotKML: Visualization of Spatial and Spatio-Temporal Objects in Google Earth
plotKML-method R Documentation
Methods for plotting results of spatial analysis in Google Earth
Description
The method writes inputs and outputs of spatial analysis (a list of point, gridded and/or polygon data usually) to KML and opens the KML file in Google Earth (or any other default package used to view KML/KMZ files).
Usage
## S4 method for signature 'sf'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPointsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size, colour, points_names,
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialLinesDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPolygonsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, plot.labpt, labels, metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPixelsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialGridDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'RasterLayer'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialPhotoOverlay'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
dae.name, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SoilProfileCollection'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
var.name, metadata = NULL, kmz = get("kmz", envir = plotKML.opts),
open.kml = TRUE, ...)
## S4 method for signature 'STIDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
points_names, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'STFDF'
plotKML(obj, ...)
## S4 method for signature 'STSDF'
plotKML(obj, ...)
## S4 method for signature 'STTDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, start.icon = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickTimeSeries'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
pngwidth = 680, pngheight = 180, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickSimulations'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
obj.summary = TRUE,
pngwidth = 680, pngheight = 200, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialMaxEntOutput'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
html.file = obj@maxent@html,
iframe.width = 800, iframe.height = 800, pngwidth = 280,
pngheight = 280, pngpointsize = 14, colour,
shape = "http://plotkml.r-forge.r-project.org/icon17.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE,
TimeSpan.begin = obj@TimeSpan.begin, TimeSpan.end = obj@TimeSpan.end, ...)
## S4 method for signature 'SpatialPredictions'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = FALSE, plot.svar = FALSE,
pngwidth = 210, pngheight = 580, pngpointsize = 14,
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialSamplingPattern'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialVectorsSimulations'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = TRUE, plot.svar = FALSE,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'list'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env=parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size = NULL, colour, points_names = "",
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
plot.labpt = TRUE, labels = "", metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
Arguments
obj
input object of specific class; either some sf, or sp, or raster or spacetime package class object, or plotKML composite objects containing both inputs and outputs of analysis
folder.name
character; folder name in the KML file
file.name
character; output KML file name
size
for point objects for plotting (see aesthetics)
colour
colour variable for plotting (see aesthetics)
points_names
vector of characters that can be used as labels
shape
character; icons used for plotting (see aesthetics)
raster_name
(optional) specify the output file name (PNG)
var.name
target variable name (only valid for visualization of "SoilProfileCollection" class data
metadata
(optional) the metadata object
plot.labpt
logical; specifies whether to plot centroids for polygon data
labels
character vector; list of labels that will attached to the centroids
start.icon
icon for the start position (for trajectory data)
dae.name
output DAE file name
html.file
specify the location of the html file containing report data (if the input object is of class "SpatialMaxEntOutput")
iframe.width
integer; width of the screen for iframe
iframe.height
integer; height of the screen for iframe
TimeSpan.begin
object of class "POSIXct"; begin of the sampling period
TimeSpan.end
object of class "POSIXct"; end of the sampling period
pngwidth
integer; width of the PNG plot (screen image)
pngheight
integer; height of the PNG plot (screen image)
pngpointsize
integer; text size in the PNG plot (screen image)
grid2poly
logical; specifies whether to convert gridded object to polygons
obj.summary
logical; specifies whether to print the object summary
plot.svar
logical; specifies whether to plot the model uncertainty
kmz
logical; specifies whether to compress the output KML file
open.kml
logical; specifies whether to directly open the output KML file (i.e. in Google Earth)
...
(optional) arguments passed to the lower level functions
Details
This is a generic function to plot various spatial and spatio-temporal R objects that contain both inputs and outputs of spatial analysis. The resulting plots (referred to as ‘views’) are expected to be cartographically complete as they should contain legends, and data and model descriptions. In principle, plotKML works with both simple spatial objects, and complex objects such as "SpatialPredictions", "SpatialVectorsSimulations", "RasterBrickSimulations", "RasterBrickTimeSeries", "SpatialMaxEntOutput" and similar. To further customize visualizations consider combining the lower level functions kml_open, kml_close, kml_compress, kml_screen into your own plotKML() method.
All ST-classes are coerced to the STIDF format and hence use the plotKML method for STIDFs.
The sf objects are processed according to the class of the geometry column. The aestethics are defined in the same way as for their sp counterparts.
Note
To prepare a list of objects of class "SpatialPointsDataFrame", "SpatialLinesDataFrame", "SpatialPolygonsDataFrame", or "SpatialPixelsDataFrame" consider using the landmap::tile function. Writting large spatial objects via plotKML can be time consuming. Please refer to the package manual for more information.
See Also
SpatialPredictions-class, SpatialVectorsSimulations-class, RasterBrickSimulations-class, RasterBrickTimeSeries-class, SpatialMaxEntOutput-class, SpatialSamplingPattern-class
Examples
plotKML.env(kmz = FALSE)
## -------------- SpatialPointsDataFrame --------- ##
library(sp)
library(rgdal)
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## subset to 20 percent:
eberg <- eberg[runif(nrow(eberg))<.1,]
## Not run: ## bubble type plot:
plotKML(eberg["CLYMHT_A"])
plotKML(eberg["CLYMHT_A"], colour_scale=rep("#FFFF00", 2), points_names="")
## End(Not run)
## plot points with a legend:
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml.file = paste0(tempdir(), "/eberg_CLYMHT_A.kml")
leg.file = paste0(dirname(kml.file), "/kml_legend.png")
kml_open(kml.file)
kml_layer(eberg["CLYMHT_A"], colour=CLYMHT_A, z.lim=c(20,60),
colour_scale=SAGA_pal[[1]], shape=shape, points_names="")
kml_legend.bar(x=eberg$CLYMHT_A,
legend.file=leg.file,
legend.pal=SAGA_pal[[1]], z.lim=c(20,60))
kml_screen(image.file=leg.file)
kml_close(kml.file)
## ----- sf objects with sfc_POINT geometry ----- ##
eberg_sf <- sf::st_as_sf(eberg)
## Not run:
plotKML(eberg_sf["CLYMHT_A"])
plotKML(eberg_sf["CLYMHT_A"], colour_scale = rep("#FFFF00", 2), points_names = "")
## End(Not run)
## -------------- SpatialLinesDataFrame --------- ##
data(eberg_contours)
## Not run:
plotKML(eberg_contours)
## plot contour lines with actual altitudes:
plotKML(eberg_contours, colour=Z, altitude=Z)
## End(Not run)
## ---- sf objects with sfc_LINESTRING geometry ---- ##
eberg_contours_sf <- sf::st_as_sf(eberg_contours)
## Not run:
plotKML(eberg_contours_sf)
plotKML(eberg_contours_sf, colour = Z, altitude = Z)
## End(Not run)
## -------------- SpatialPolygonsDataFrame --------- ##
data(eberg_zones)
## Not run:
plotKML(eberg_zones["ZONES"])
## add altitude:
zmin = 230
plotKML(eberg_zones["ZONES"], altitude=zmin+runif(length(eberg_zones))*500)
## End(Not run)
## ------ sf objects with sfc_POLYGON geometry ------ ##
eberg_zones_sf <- sf::st_as_sf(eberg_zones)
## Not run:
plotKML(eberg_zones_sf["ZONES"])
plotKML(eberg_zones_sf["ZONES"], altitude = zmin + runif(length(eberg_zones)) * 500)
## End(Not run)
## -------------- SpatialPixelsDataFrame --------- ##
library(rgdal)
library(raster)
data(eberg_grid)
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
TWI <- reproject(eberg_grid["TWISRT6"])
data(SAGA_pal)
## Not run: ## set limits manually (increase resolution):
plotKML(TWI, colour_scale = SAGA_pal[[1]])
plotKML(TWI, z.lim=c(12,20), colour_scale = SAGA_pal[[1]])
## End(Not run)
## categorical data:
eberg_grid$LNCCOR6 <- as.factor(paste(eberg_grid$LNCCOR6))
levels(eberg_grid$LNCCOR6)
data(worldgrids_pal)
## attr(worldgrids_pal["corine2k"][[1]], "names")
pal = as.character(worldgrids_pal["corine2k"][[1]][c(1,11,13,14,16,17,18)])
LNCCOR6 <- reproject(eberg_grid["LNCCOR6"])
## Not run:
plotKML(LNCCOR6, colour_scale=pal)
## End(Not run)
## -------------- SpatialPhotoOverlay --------- ##
## Not run:
library(RCurl)
imagename = "Soil_monolith.jpg"
urlExists = url.exists("https://commons.wikimedia.org")
if(urlExists){
x1 <- getWikiMedia.ImageInfo(imagename)
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
# str(sm)
plotKML(sm)
}
## End(Not run)
## -------------- SoilProfileCollection --------- ##
library(aqp)
library(plyr)
## sample profile from Nigeria:
lon = 3.90; lat = 7.50; id = "ISRIC:NG0017"; FAO1988 = "LXp"
top = c(0, 18, 36, 65, 87, 127)
bottom = c(18, 36, 65, 87, 127, 181)
ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
hue = c("7.5YR", "7.5YR", "2.5YR", "5YR", "5YR", "10YR")
value = c(3, 4, 5, 5, 5, 7); chroma = c(2, 4, 6, 8, 4, 3)
## prepare a SoilProfileCollection:
prof1 <- join(data.frame(id, top, bottom, ORCDRC, hue, value, chroma),
data.frame(id, lon, lat, FAO1988), type='inner')
prof1$soil_color <- with(prof1, munsell2rgb(hue, value, chroma))
depths(prof1) <- id ~ top + bottom
site(prof1) <- ~ lon + lat + FAO1988
coordinates(prof1) <- ~ lon + lat
proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
prof1
## Not run:
plotKML(prof1, var.name="ORCDRC", color.name="soil_color")
## End(Not run)
## -------------- STIDF --------- ##
library(sp)
library(spacetime)
## daily temperatures for Croatia:
data(HRtemp08)
## format the time column:
HRtemp08$ctime <- as.POSIXct(HRtemp08$DATE, format="%Y-%m-%dT%H:%M:%SZ")
## create a STIDF object:
sp <- SpatialPoints(HRtemp08[,c("Lon","Lat")])
proj4string(sp) <- CRS("+proj=longlat +datum=WGS84")
HRtemp08.st <- STIDF(sp, time = HRtemp08$ctime, data = HRtemp08[,c("NAME","TEMP")])
## subset to first 500 records:
HRtemp08_jan <- HRtemp08.st[1:500]
str(HRtemp08_jan)
## Not run:
plotKML(HRtemp08_jan[,,"TEMP"], LabelScale = .4)
## End(Not run)
## foot-and-mouth disease data:
data(fmd)
fmd0 <- data.frame(fmd)
coordinates(fmd0) <- c("X", "Y")
proj4string(fmd0) <- CRS("+init=epsg:27700")
fmd_sp <- as(fmd0, "SpatialPoints")
dates <- as.Date("2001-02-18")+fmd0$ReportedDay
library(spacetime)
fmd_ST <- STIDF(fmd_sp, dates, data.frame(ReportedDay=fmd0$ReportedDay))
data(SAGA_pal)
## Not run:
plotKML(fmd_ST, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- STFDF --------- ##
## Not run:
## results of krigeST:
library(gstat)
library(sp)
library(spacetime)
library(raster)
## define space-time variogram
sumMetricVgm <- vgmST("sumMetric",
space=vgm( 4.4, "Lin", 196.6, 3),
time =vgm( 2.2, "Lin", 1.1, 2),
joint=vgm(34.6, "Exp", 136.6, 12),
stAni=51.7)
## example from the gstat package:
data(air)
rural = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
rr <- rural[,"2005-06-01/2005-06-03"]
rr <- as(rr,"STSDF")
x1 <- seq(from=6,to=15,by=1)
x2 <- seq(from=48,to=55,by=1)
DE_gridded <- SpatialPoints(cbind(rep(x1,length(x2)), rep(x2,each=length(x1))),
proj4string=CRS(proj4string(rr@sp)))
gridded(DE_gridded) <- TRUE
DE_pred <- STF(sp=as(DE_gridded,"SpatialPoints"), time=rr@time)
DE_kriged <- krigeST(PM10~1, data=rr, newdata=DE_pred,
modelList=sumMetricVgm)
gridded(DE_kriged@sp) <- TRUE
#stplot(DE_kriged)
## plot in Google Earth:
z.lim = range(DE_kriged@data, na.rm=TRUE)
plotKML(DE_kriged, z.lim=z.lim)
## add observations points:
plotKML(rr, z.lim=z.lim)
## End(Not run)
## -------------- STTDF --------- ##
## Not run:
library(fossil)
library(spacetime)
library(adehabitatLT)
data(gpxbtour)
## format the time column:
gpxbtour$ctime <- as.POSIXct(gpxbtour$time, format="%Y-%m-%dT%H:%M:%SZ")
coordinates(gpxbtour) <- ~lon+lat
proj4string(gpxbtour) <- CRS("+proj=longlat +datum=WGS84")
xy <- as.list(data.frame(t(coordinates(gpxbtour))))
gpxbtour$dist.km <- sapply(xy, function(x) {
deg.dist(long1=x[1], lat1=x[2], long2=xy[[1]][1], lat2=xy[[1]][2])
} )
## convert to a STTDF class:
gpx.ltraj <- as.ltraj(coordinates(gpxbtour), gpxbtour$ctime, id = "th")
gpx.st <- as(gpx.ltraj, "STTDF")
gpx.st$speed <- gpxbtour$speed
gpx.st@sp@proj4string <- CRS("+proj=longlat +datum=WGS84")
str(gpx.st)
plotKML(gpx.st, colour="speed")
## End(Not run)
## -------------- Spatial Metadata --------- ##
## Not run:
eberg.md <- spMetadata(eberg, xml.file=system.file("eberg.xml", package="plotKML"),
Target_variable="SNDMHT_A", Citation_title="Ebergotzen profiles")
plotKML(eberg[1:100,"CLYMHT_A"], metadata=eberg.md)
## End(Not run)
## -------------- RasterBrickTimeSeries --------- ##
library(raster)
library(sp)
data(LST)
gridded(LST) <- ~lon+lat
proj4string(LST) <- CRS("+proj=longlat +datum=WGS84")
dates <- sapply(strsplit(names(LST), "LST"), function(x){x[[2]]})
datesf <- format(as.Date(dates, "%Y_%m_%d"), "%Y-%m-%dT%H:%M:%SZ")
## begin / end dates +/- 4 days:
TimeSpan.begin = as.POSIXct(unclass(as.POSIXct(datesf))-4*24*60*60, origin="1970-01-01")
TimeSpan.end = as.POSIXct(unclass(as.POSIXct(datesf))+4*24*60*60, origin="1970-01-01")
## pick climatic stations in the area:
pnts <- HRtemp08[which(HRtemp08$NAME=="Pazin")[1],]
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Crni Lug - NP Risnjak")[1],])
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Cres")[1],])
coordinates(pnts) <- ~Lon + Lat
proj4string(pnts) <- CRS("+proj=longlat +datum=WGS84")
## get the dates from the file names:
LST_ll <- brick(LST[1:5])
LST_ll@title = "Time series of MODIS Land Surface Temperature images"
LST.ts <- new("RasterBrickTimeSeries", variable = "LST", sampled = pnts,
rasters = LST_ll, TimeSpan.begin = TimeSpan.begin[1:5],
TimeSpan.end = TimeSpan.end[1:5])
data(SAGA_pal)
## Not run: ## plot MODIS images in Google Earth:
plotKML(LST.ts, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- RasterBrickSimulations --------- ##
## Not run:
library(sp)
library(gstat)
data(barxyz)
## define the projection system:
prj = "+proj=tmerc +lat_0=0 +lon_0=18 +k=0.9999 +x_0=6500000 +y_0=0
+ellps=bessel +units=m
+towgs84=550.499,164.116,475.142,5.80967,2.07902,-11.62386,0.99999445824"
coordinates(barxyz) <- ~x+y
proj4string(barxyz) <- CRS(prj)
data(bargrid)
coordinates(bargrid) <- ~x+y
gridded(bargrid) <- TRUE
proj4string(bargrid) <- CRS(prj)
## fit a variogram and generate simulations:
Z.ovgm <- vgm(psill=1352, model="Mat", range=650, nugget=0, kappa=1.2)
sel <- runif(length(barxyz$Z))<.2
## Note: this operation can be time consuming
sims <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20,
nsim=10, debug.level=-1)
## specify the cross-section:
t1 <- Line(matrix(c(bargrid@bbox[1,1], bargrid@bbox[1,2], 5073012, 5073012), ncol=2))
transect <- SpatialLines(list(Lines(list(t1), ID="t")), CRS(prj))
## glue to a RasterBrickSimulations object:
library(raster)
bardem_sims <- new("RasterBrickSimulations", variable = "elevations",
sampled = transect, realizations = brick(sims))
## plot the whole project and open in Google Earth:
data(R_pal)
plotKML(bardem_sims, colour_scale = R_pal[[4]])
## End(Not run)
## -------------- SpatialVectorsSimulations --------- ##
## Not run:
data(barstr)
data(bargrid)
library(sp)
coordinates(bargrid) <- ~ x+y
gridded(bargrid) <- TRUE
## output topology:
cell.size = bargrid@grid@cellsize[1]
bbox = bargrid@bbox
nrows = round(abs(diff(bbox[1,])/cell.size), 0)
ncols = round(abs(diff(bbox[2,])/cell.size), 0)
gridT = GridTopology(cellcentre.offset=bbox[,1],
cellsize=c(cell.size,cell.size),
cells.dim=c(nrows, ncols))
bar_sum <- count.GridTopology(gridT, vectL=barstr[1:5])
## NOTE: this operation can be time consuming!
## plot the whole project and open in Google Earth:
plotKML(bar_sum)
## End(Not run)
## -------------- SpatialMaxEntOutput --------- ##
## Not run:
library(maptools)
library(rgdal)
data(bigfoot)
aea.prj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96
+x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
data(USAWgrids)
gridded(USAWgrids) <- ~s1+s2
proj4string(USAWgrids) <- CRS(aea.prj)
bbox <- spTransform(USAWgrids, CRS("+proj=longlat +datum=WGS84"))@bbox
sel = bigfoot$Lon > bbox[1,1] & bigfoot$Lon < bbox[1,2] &
bigfoot$Lat > bbox[2,1] & bigfoot$Lat < bbox[2,2]
bigfoot <- bigfoot[sel,]
coordinates(bigfoot) <- ~Lon+Lat
proj4string(bigfoot) <- CRS("+proj=longlat +datum=WGS84")
library(spatstat)
bigfoot.aea <- as.ppp(spTransform(bigfoot, CRS(aea.prj)))
## Load the covariates:
sel.grids <- c("globedem","nlights03","sdroads","gcarb","twi","globcov")
library(dismo)
## run MaxEnt analysis:
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
if(file.exists(jar)){
bigfoot.smo <- MaxEnt(bigfoot.aea, USAWgrids[sel.grids])
icon = "http://plotkml.r-forge.r-project.org/bigfoot.png"
data(R_pal)
plotKML(bigfoot.smo, colour_scale = R_pal[["bpy_colors"]], shape = icon)
}
## End(Not run)
plotKML documentation built on June 7, 2022, 5:07 p.m.
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| plotKML-method | R Documentation |
Methods for plotting results of spatial analysis in Google Earth
Description
The method writes inputs and outputs of spatial analysis (a list of point, gridded and/or polygon data usually) to KML and opens the KML file in Google Earth (or any other default package used to view KML/KMZ files).
Usage
## S4 method for signature 'sf'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPointsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size, colour, points_names,
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialLinesDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPolygonsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, plot.labpt, labels, metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialPixelsDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialGridDataFrame'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'RasterLayer'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, raster_name, metadata = NULL, kmz = FALSE, open.kml = TRUE, ...)
## S4 method for signature 'SpatialPhotoOverlay'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
dae.name, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SoilProfileCollection'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
var.name, metadata = NULL, kmz = get("kmz", envir = plotKML.opts),
open.kml = TRUE, ...)
## S4 method for signature 'STIDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
points_names, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'STFDF'
plotKML(obj, ...)
## S4 method for signature 'STSDF'
plotKML(obj, ...)
## S4 method for signature 'STTDF'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, start.icon = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickTimeSeries'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
pngwidth = 680, pngheight = 180, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'RasterBrickSimulations'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
obj.summary = TRUE,
pngwidth = 680, pngheight = 200, pngpointsize = 14,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialMaxEntOutput'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
html.file = obj@maxent@html,
iframe.width = 800, iframe.height = 800, pngwidth = 280,
pngheight = 280, pngpointsize = 14, colour,
shape = "http://plotkml.r-forge.r-project.org/icon17.png",
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE,
TimeSpan.begin = obj@TimeSpan.begin, TimeSpan.end = obj@TimeSpan.end, ...)
## S4 method for signature 'SpatialPredictions'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = FALSE, plot.svar = FALSE,
pngwidth = 210, pngheight = 580, pngpointsize = 14,
metadata = NULL, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialSamplingPattern'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
colour, kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'SpatialVectorsSimulations'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env = parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""), colour,
grid2poly = FALSE, obj.summary = TRUE, plot.svar = FALSE,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
## S4 method for signature 'list'
plotKML(obj,
folder.name = normalizeFilename(deparse(substitute(obj, env=parent.frame()))),
file.name = paste(folder.name, ".kml", sep=""),
size = NULL, colour, points_names = "",
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png",
plot.labpt = TRUE, labels = "", metadata = NULL,
kmz = get("kmz", envir = plotKML.opts), open.kml = TRUE, ...)
Arguments
obj |
input object of specific class; either some sf, or sp, or raster or spacetime package class object, or plotKML composite objects containing both inputs and outputs of analysis |
folder.name |
character; folder name in the KML file |
file.name |
character; output KML file name |
size |
for point objects for plotting (see aesthetics) |
colour |
colour variable for plotting (see aesthetics) |
points_names |
vector of characters that can be used as labels |
shape |
character; icons used for plotting (see aesthetics) |
raster_name |
(optional) specify the output file name (PNG) |
var.name |
target variable name (only valid for visualization of |
metadata |
(optional) the metadata object |
plot.labpt |
logical; specifies whether to plot centroids for polygon data |
labels |
character vector; list of labels that will attached to the centroids |
start.icon |
icon for the start position (for trajectory data) |
dae.name |
output DAE file name |
html.file |
specify the location of the html file containing report data (if the input object is of class |
iframe.width |
integer; width of the screen for iframe |
iframe.height |
integer; height of the screen for iframe |
TimeSpan.begin |
object of class |
TimeSpan.end |
object of class |
pngwidth |
integer; width of the PNG plot (screen image) |
pngheight |
integer; height of the PNG plot (screen image) |
pngpointsize |
integer; text size in the PNG plot (screen image) |
grid2poly |
logical; specifies whether to convert gridded object to polygons |
obj.summary |
logical; specifies whether to print the object summary |
plot.svar |
logical; specifies whether to plot the model uncertainty |
kmz |
logical; specifies whether to compress the output KML file |
open.kml |
logical; specifies whether to directly open the output KML file (i.e. in Google Earth) |
... |
(optional) arguments passed to the lower level functions |
Details
This is a generic function to plot various spatial and spatio-temporal R objects that contain both inputs and outputs of spatial analysis. The resulting plots (referred to as ‘views’) are expected to be cartographically complete as they should contain legends, and data and model descriptions. In principle, plotKML works with both simple spatial objects, and complex objects such as "SpatialPredictions", "SpatialVectorsSimulations", "RasterBrickSimulations", "RasterBrickTimeSeries", "SpatialMaxEntOutput" and similar. To further customize visualizations consider combining the lower level functions kml_open, kml_close, kml_compress, kml_screen into your own plotKML() method.
All ST-classes are coerced to the STIDF format and hence use the plotKML method for STIDFs.
The sf objects are processed according to the class of the geometry column. The aestethics are defined in the same way as for their sp counterparts.
Note
To prepare a list of objects of class "SpatialPointsDataFrame", "SpatialLinesDataFrame", "SpatialPolygonsDataFrame", or "SpatialPixelsDataFrame" consider using the landmap::tile function. Writting large spatial objects via plotKML can be time consuming. Please refer to the package manual for more information.
See Also
SpatialPredictions-class, SpatialVectorsSimulations-class, RasterBrickSimulations-class, RasterBrickTimeSeries-class, SpatialMaxEntOutput-class, SpatialSamplingPattern-class
Examples
plotKML.env(kmz = FALSE)
## -------------- SpatialPointsDataFrame --------- ##
library(sp)
library(rgdal)
data(eberg)
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
## subset to 20 percent:
eberg <- eberg[runif(nrow(eberg))<.1,]
## Not run: ## bubble type plot:
plotKML(eberg["CLYMHT_A"])
plotKML(eberg["CLYMHT_A"], colour_scale=rep("#FFFF00", 2), points_names="")
## End(Not run)
## plot points with a legend:
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml.file = paste0(tempdir(), "/eberg_CLYMHT_A.kml")
leg.file = paste0(dirname(kml.file), "/kml_legend.png")
kml_open(kml.file)
kml_layer(eberg["CLYMHT_A"], colour=CLYMHT_A, z.lim=c(20,60),
colour_scale=SAGA_pal[[1]], shape=shape, points_names="")
kml_legend.bar(x=eberg$CLYMHT_A,
legend.file=leg.file,
legend.pal=SAGA_pal[[1]], z.lim=c(20,60))
kml_screen(image.file=leg.file)
kml_close(kml.file)
## ----- sf objects with sfc_POINT geometry ----- ##
eberg_sf <- sf::st_as_sf(eberg)
## Not run:
plotKML(eberg_sf["CLYMHT_A"])
plotKML(eberg_sf["CLYMHT_A"], colour_scale = rep("#FFFF00", 2), points_names = "")
## End(Not run)
## -------------- SpatialLinesDataFrame --------- ##
data(eberg_contours)
## Not run:
plotKML(eberg_contours)
## plot contour lines with actual altitudes:
plotKML(eberg_contours, colour=Z, altitude=Z)
## End(Not run)
## ---- sf objects with sfc_LINESTRING geometry ---- ##
eberg_contours_sf <- sf::st_as_sf(eberg_contours)
## Not run:
plotKML(eberg_contours_sf)
plotKML(eberg_contours_sf, colour = Z, altitude = Z)
## End(Not run)
## -------------- SpatialPolygonsDataFrame --------- ##
data(eberg_zones)
## Not run:
plotKML(eberg_zones["ZONES"])
## add altitude:
zmin = 230
plotKML(eberg_zones["ZONES"], altitude=zmin+runif(length(eberg_zones))*500)
## End(Not run)
## ------ sf objects with sfc_POLYGON geometry ------ ##
eberg_zones_sf <- sf::st_as_sf(eberg_zones)
## Not run:
plotKML(eberg_zones_sf["ZONES"])
plotKML(eberg_zones_sf["ZONES"], altitude = zmin + runif(length(eberg_zones)) * 500)
## End(Not run)
## -------------- SpatialPixelsDataFrame --------- ##
library(rgdal)
library(raster)
data(eberg_grid)
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
TWI <- reproject(eberg_grid["TWISRT6"])
data(SAGA_pal)
## Not run: ## set limits manually (increase resolution):
plotKML(TWI, colour_scale = SAGA_pal[[1]])
plotKML(TWI, z.lim=c(12,20), colour_scale = SAGA_pal[[1]])
## End(Not run)
## categorical data:
eberg_grid$LNCCOR6 <- as.factor(paste(eberg_grid$LNCCOR6))
levels(eberg_grid$LNCCOR6)
data(worldgrids_pal)
## attr(worldgrids_pal["corine2k"][[1]], "names")
pal = as.character(worldgrids_pal["corine2k"][[1]][c(1,11,13,14,16,17,18)])
LNCCOR6 <- reproject(eberg_grid["LNCCOR6"])
## Not run:
plotKML(LNCCOR6, colour_scale=pal)
## End(Not run)
## -------------- SpatialPhotoOverlay --------- ##
## Not run:
library(RCurl)
imagename = "Soil_monolith.jpg"
urlExists = url.exists("https://commons.wikimedia.org")
if(urlExists){
x1 <- getWikiMedia.ImageInfo(imagename)
sm <- spPhoto(filename = x1$url$url, exif.info = x1$metadata)
# str(sm)
plotKML(sm)
}
## End(Not run)
## -------------- SoilProfileCollection --------- ##
library(aqp)
library(plyr)
## sample profile from Nigeria:
lon = 3.90; lat = 7.50; id = "ISRIC:NG0017"; FAO1988 = "LXp"
top = c(0, 18, 36, 65, 87, 127)
bottom = c(18, 36, 65, 87, 127, 181)
ORCDRC = c(18.4, 4.4, 3.6, 3.6, 3.2, 1.2)
hue = c("7.5YR", "7.5YR", "2.5YR", "5YR", "5YR", "10YR")
value = c(3, 4, 5, 5, 5, 7); chroma = c(2, 4, 6, 8, 4, 3)
## prepare a SoilProfileCollection:
prof1 <- join(data.frame(id, top, bottom, ORCDRC, hue, value, chroma),
data.frame(id, lon, lat, FAO1988), type='inner')
prof1$soil_color <- with(prof1, munsell2rgb(hue, value, chroma))
depths(prof1) <- id ~ top + bottom
site(prof1) <- ~ lon + lat + FAO1988
coordinates(prof1) <- ~ lon + lat
proj4string(prof1) <- CRS("+proj=longlat +datum=WGS84")
prof1
## Not run:
plotKML(prof1, var.name="ORCDRC", color.name="soil_color")
## End(Not run)
## -------------- STIDF --------- ##
library(sp)
library(spacetime)
## daily temperatures for Croatia:
data(HRtemp08)
## format the time column:
HRtemp08$ctime <- as.POSIXct(HRtemp08$DATE, format="%Y-%m-%dT%H:%M:%SZ")
## create a STIDF object:
sp <- SpatialPoints(HRtemp08[,c("Lon","Lat")])
proj4string(sp) <- CRS("+proj=longlat +datum=WGS84")
HRtemp08.st <- STIDF(sp, time = HRtemp08$ctime, data = HRtemp08[,c("NAME","TEMP")])
## subset to first 500 records:
HRtemp08_jan <- HRtemp08.st[1:500]
str(HRtemp08_jan)
## Not run:
plotKML(HRtemp08_jan[,,"TEMP"], LabelScale = .4)
## End(Not run)
## foot-and-mouth disease data:
data(fmd)
fmd0 <- data.frame(fmd)
coordinates(fmd0) <- c("X", "Y")
proj4string(fmd0) <- CRS("+init=epsg:27700")
fmd_sp <- as(fmd0, "SpatialPoints")
dates <- as.Date("2001-02-18")+fmd0$ReportedDay
library(spacetime)
fmd_ST <- STIDF(fmd_sp, dates, data.frame(ReportedDay=fmd0$ReportedDay))
data(SAGA_pal)
## Not run:
plotKML(fmd_ST, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- STFDF --------- ##
## Not run:
## results of krigeST:
library(gstat)
library(sp)
library(spacetime)
library(raster)
## define space-time variogram
sumMetricVgm <- vgmST("sumMetric",
space=vgm( 4.4, "Lin", 196.6, 3),
time =vgm( 2.2, "Lin", 1.1, 2),
joint=vgm(34.6, "Exp", 136.6, 12),
stAni=51.7)
## example from the gstat package:
data(air)
rural = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
rr <- rural[,"2005-06-01/2005-06-03"]
rr <- as(rr,"STSDF")
x1 <- seq(from=6,to=15,by=1)
x2 <- seq(from=48,to=55,by=1)
DE_gridded <- SpatialPoints(cbind(rep(x1,length(x2)), rep(x2,each=length(x1))),
proj4string=CRS(proj4string(rr@sp)))
gridded(DE_gridded) <- TRUE
DE_pred <- STF(sp=as(DE_gridded,"SpatialPoints"), time=rr@time)
DE_kriged <- krigeST(PM10~1, data=rr, newdata=DE_pred,
modelList=sumMetricVgm)
gridded(DE_kriged@sp) <- TRUE
#stplot(DE_kriged)
## plot in Google Earth:
z.lim = range(DE_kriged@data, na.rm=TRUE)
plotKML(DE_kriged, z.lim=z.lim)
## add observations points:
plotKML(rr, z.lim=z.lim)
## End(Not run)
## -------------- STTDF --------- ##
## Not run:
library(fossil)
library(spacetime)
library(adehabitatLT)
data(gpxbtour)
## format the time column:
gpxbtour$ctime <- as.POSIXct(gpxbtour$time, format="%Y-%m-%dT%H:%M:%SZ")
coordinates(gpxbtour) <- ~lon+lat
proj4string(gpxbtour) <- CRS("+proj=longlat +datum=WGS84")
xy <- as.list(data.frame(t(coordinates(gpxbtour))))
gpxbtour$dist.km <- sapply(xy, function(x) {
deg.dist(long1=x[1], lat1=x[2], long2=xy[[1]][1], lat2=xy[[1]][2])
} )
## convert to a STTDF class:
gpx.ltraj <- as.ltraj(coordinates(gpxbtour), gpxbtour$ctime, id = "th")
gpx.st <- as(gpx.ltraj, "STTDF")
gpx.st$speed <- gpxbtour$speed
gpx.st@sp@proj4string <- CRS("+proj=longlat +datum=WGS84")
str(gpx.st)
plotKML(gpx.st, colour="speed")
## End(Not run)
## -------------- Spatial Metadata --------- ##
## Not run:
eberg.md <- spMetadata(eberg, xml.file=system.file("eberg.xml", package="plotKML"),
Target_variable="SNDMHT_A", Citation_title="Ebergotzen profiles")
plotKML(eberg[1:100,"CLYMHT_A"], metadata=eberg.md)
## End(Not run)
## -------------- RasterBrickTimeSeries --------- ##
library(raster)
library(sp)
data(LST)
gridded(LST) <- ~lon+lat
proj4string(LST) <- CRS("+proj=longlat +datum=WGS84")
dates <- sapply(strsplit(names(LST), "LST"), function(x){x[[2]]})
datesf <- format(as.Date(dates, "%Y_%m_%d"), "%Y-%m-%dT%H:%M:%SZ")
## begin / end dates +/- 4 days:
TimeSpan.begin = as.POSIXct(unclass(as.POSIXct(datesf))-4*24*60*60, origin="1970-01-01")
TimeSpan.end = as.POSIXct(unclass(as.POSIXct(datesf))+4*24*60*60, origin="1970-01-01")
## pick climatic stations in the area:
pnts <- HRtemp08[which(HRtemp08$NAME=="Pazin")[1],]
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Crni Lug - NP Risnjak")[1],])
pnts <- rbind(pnts, HRtemp08[which(HRtemp08$NAME=="Cres")[1],])
coordinates(pnts) <- ~Lon + Lat
proj4string(pnts) <- CRS("+proj=longlat +datum=WGS84")
## get the dates from the file names:
LST_ll <- brick(LST[1:5])
LST_ll@title = "Time series of MODIS Land Surface Temperature images"
LST.ts <- new("RasterBrickTimeSeries", variable = "LST", sampled = pnts,
rasters = LST_ll, TimeSpan.begin = TimeSpan.begin[1:5],
TimeSpan.end = TimeSpan.end[1:5])
data(SAGA_pal)
## Not run: ## plot MODIS images in Google Earth:
plotKML(LST.ts, colour_scale=SAGA_pal[[1]])
## End(Not run)
## -------------- RasterBrickSimulations --------- ##
## Not run:
library(sp)
library(gstat)
data(barxyz)
## define the projection system:
prj = "+proj=tmerc +lat_0=0 +lon_0=18 +k=0.9999 +x_0=6500000 +y_0=0
+ellps=bessel +units=m
+towgs84=550.499,164.116,475.142,5.80967,2.07902,-11.62386,0.99999445824"
coordinates(barxyz) <- ~x+y
proj4string(barxyz) <- CRS(prj)
data(bargrid)
coordinates(bargrid) <- ~x+y
gridded(bargrid) <- TRUE
proj4string(bargrid) <- CRS(prj)
## fit a variogram and generate simulations:
Z.ovgm <- vgm(psill=1352, model="Mat", range=650, nugget=0, kappa=1.2)
sel <- runif(length(barxyz$Z))<.2
## Note: this operation can be time consuming
sims <- krige(Z~1, barxyz[sel,], bargrid, model=Z.ovgm, nmax=20,
nsim=10, debug.level=-1)
## specify the cross-section:
t1 <- Line(matrix(c(bargrid@bbox[1,1], bargrid@bbox[1,2], 5073012, 5073012), ncol=2))
transect <- SpatialLines(list(Lines(list(t1), ID="t")), CRS(prj))
## glue to a RasterBrickSimulations object:
library(raster)
bardem_sims <- new("RasterBrickSimulations", variable = "elevations",
sampled = transect, realizations = brick(sims))
## plot the whole project and open in Google Earth:
data(R_pal)
plotKML(bardem_sims, colour_scale = R_pal[[4]])
## End(Not run)
## -------------- SpatialVectorsSimulations --------- ##
## Not run:
data(barstr)
data(bargrid)
library(sp)
coordinates(bargrid) <- ~ x+y
gridded(bargrid) <- TRUE
## output topology:
cell.size = bargrid@grid@cellsize[1]
bbox = bargrid@bbox
nrows = round(abs(diff(bbox[1,])/cell.size), 0)
ncols = round(abs(diff(bbox[2,])/cell.size), 0)
gridT = GridTopology(cellcentre.offset=bbox[,1],
cellsize=c(cell.size,cell.size),
cells.dim=c(nrows, ncols))
bar_sum <- count.GridTopology(gridT, vectL=barstr[1:5])
## NOTE: this operation can be time consuming!
## plot the whole project and open in Google Earth:
plotKML(bar_sum)
## End(Not run)
## -------------- SpatialMaxEntOutput --------- ##
## Not run:
library(maptools)
library(rgdal)
data(bigfoot)
aea.prj <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96
+x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m +no_defs"
data(USAWgrids)
gridded(USAWgrids) <- ~s1+s2
proj4string(USAWgrids) <- CRS(aea.prj)
bbox <- spTransform(USAWgrids, CRS("+proj=longlat +datum=WGS84"))@bbox
sel = bigfoot$Lon > bbox[1,1] & bigfoot$Lon < bbox[1,2] &
bigfoot$Lat > bbox[2,1] & bigfoot$Lat < bbox[2,2]
bigfoot <- bigfoot[sel,]
coordinates(bigfoot) <- ~Lon+Lat
proj4string(bigfoot) <- CRS("+proj=longlat +datum=WGS84")
library(spatstat)
bigfoot.aea <- as.ppp(spTransform(bigfoot, CRS(aea.prj)))
## Load the covariates:
sel.grids <- c("globedem","nlights03","sdroads","gcarb","twi","globcov")
library(dismo)
## run MaxEnt analysis:
jar <- paste(system.file(package="dismo"), "/java/maxent.jar", sep='')
if(file.exists(jar)){
bigfoot.smo <- MaxEnt(bigfoot.aea, USAWgrids[sel.grids])
icon = "http://plotkml.r-forge.r-project.org/bigfoot.png"
data(R_pal)
plotKML(bigfoot.smo, colour_scale = R_pal[["bpy_colors"]], shape = icon)
}
## End(Not run)
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