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 '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 = TRUE, 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

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.

Note

To prepare a list of objects of class "SpatialPointsDataFrame", "SpatialLinesDataFrame", "SpatialPolygonsDataFrame", or "SpatialPixelsDataFrame" consider using the GSIF::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(silent = FALSE, 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_open("eberg_CLYMHT_A.kml")
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="kml_legend.png",
   legend.pal=SAGA_pal[[1]], z.lim=c(20,60))
kml_screen(image.file="kml_legend.png")
kml_close("eberg_CLYMHT_A.kml")

## -------------- 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)

## -------------- 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)

## -------------- 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("http://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 --------- ##

## 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)
## Not run: 
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)

## -------------- Spatial Predictions --------- ##
library(sp)
library(rgdal)
library(gstat)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
## load grids:
data(meuse.grid)
gridded(meuse.grid) <- ~x+y
proj4string(meuse.grid) <- CRS("+init=epsg:28992")
## Not run: ## fit a model:
library(GSIF)
omm <- fit.gstatModel(observations = meuse, formulaString = om~dist, 
   family = gaussian(log), covariates = meuse.grid)
## produce SpatialPredictions:
om.rk <- predict(omm, predictionLocations = meuse.grid)
## plot the whole geostatical mapping project in Google Earth:
plotKML(om.rk, colour_scale = SAGA_pal[[1]])
## plot each cell as polygon:
plotKML(om.rk, colour_scale = SAGA_pal[[1]], grid2poly = TRUE)

## End(Not run)

## -------------- SpatialSamplingPattern --------- ##
## Not run: 
library(spcosa)
library(sp)
## read a polygon map:
shpFarmsum <- readOGR(dsn = system.file("maps", package = "spcosa"), 
  layer = "farmsum")
## stratify `Farmsum' into 50 strata
myStratification <- stratify(shpFarmsum, nStrata = 50)
## sample two sampling units per stratum
mySamplingPattern <- spsample(myStratification, n = 2)
## attach the correct proj4 string:
library(RCurl)
urlExists = url.exists("http://spatialreference.org/ref/sr-org/6781/proj4/")
if(urlExists){
  nl.rd <- getURL("http://spatialreference.org/ref/sr-org/6781/proj4/")
  proj4string(mySamplingPattern@sample) <- CRS(nl.rd) 
  # prepare spatial domain (polygons):
  sp.domain <- as(myStratification@cells, "SpatialPolygons")
  sp.domain <- SpatialPolygonsDataFrame(sp.domain, 
     data.frame(ID=as.factor(myStratification@stratumId)), match.ID = FALSE)
  proj4string(sp.domain) <- CRS(nl.rd) 
  # create new object:
  mySamplingPattern.ssp <- new("SpatialSamplingPattern", 
     method = class(mySamplingPattern), pattern = mySamplingPattern@sample, 
     sp.domain = sp.domain)
  # the same plot now in Google Earth:
  shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
  plotKML(mySamplingPattern.ssp, shape = shape)
}

## 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(GSIF)
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)