====== Exemplo: Kernel do Splancs ======

Example of a spatial point proccess\\
Data from the splancs package\\
The example tranfer the data to the DBMS as well a raster with a kernel estimate of the intensity function


** 1. Getting started: loading required packages and the data **
<code R>
require(aRT)
require(splancs)
data(bodmin)
</code>

getting to know the data
<code R>
names(bodmin)
polymap(bodmin$poly)
pointmap(as.points(bodmin), add=TRUE) see the plot!
</code>
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** 2. An analysis: kernel intensity estimate **

Kernel estimate from the intensity function of the
point proccess performed within R using splancs' function
<code R>
ker <- kernel2d(as.points(bodmin), bodmin$poly, h0=2, nx=100, ny=100)
</code>

plot splancs results
<code R>
image(ker, asp=1, col=rev(heat.colors(21)))
polygon(bodmin$poly, lwd=2)
pointmap(as.points(bodmin), add=TRUE, pch=19) see the plot
</code>

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** 3. Creating a database and storing the results **

connecting to the DBMS
<code R>
con = openConn()
con
</code>

how to delete an existing database (if already exists)
<code R>
if(any(showDbs(con)=="bodmin"))
  deleteDb(con, "bodmin", force=T)
</code>

creating a new database
<code R>
bod = createDb(con, "bodmin")
bod
</code>

aRT uses "sp" representations of spatial objects.
So we start converting the data to the "sp" format

a. Preparing and transfering the points
<code R>
xy <- as.data.frame(bodmin[1:2])
coordinates(xy) <- c("x", "y")
##
l_points <- importSpData(bod, xy, "coords")
</code>

checking the status of the database
<code R>
bod
</code>

Note: the importSpData() command above encapsulates several steps
(which could also be done individually with aRT):
  - converting to SpatialPointsDataFrame (with ID specification).
  - creating a layers, addpoints() and creating a table


b. Preparing and transfering the polygon 
<code R>
pol <- SpatialPolygons(list(Polygons(list(Polygon(bodmin$poly)),"1")),1)
l_pol <- createLayer(bod, "contorno")
addPolygons(l_pol, pol)
createTable(l_pol, "t_pol")
</code>

c. Preparing and transfering the image (raster)
general approach: converting the kernel to "sp" format
<code R>
g <- cbind(expand.grid(x = ker$x, y = ker$y), as.vector(ker$z))
coordinates(g) <- c("x", "y")
gridded(g) <- TRUE

l_kernel <- createLayer(bod, "kernel")
addRaster(l_kernel, g)
</code>


alternativelly, splancs kernel objects are also directly accepted
<code R>
l_splancs <- createLayer(bod, "kernelsplancs")
addRaster(l_splancs, ker)
</code>


checking the status of the data base
<code R>
bod
</code>

**//open and inspect the database in terraView!!!//**

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** 4. visualisation of results in R **
a. plot from the "sp" objects
<code R>
image(g, col=rev(heat.colors(21)))
contour(ker, add = T)  
fullgrid(g)=TRUE
plot(pol, add=T, lwd=2)
</code>

Ploting the DB layers -- plot from aRT
<code R>
plot(l_kernel, col=rev(heat.colors(21)))
plot(l_points, add=T, pch=19)
plot(l_pol, add=T, lwd=2)
</code>

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** 5. (Optional) Setting visualisations for terraView **
<code R>
thp <- createTheme(l_points, "points", view="vbod1")
th <- createTheme(l_pol, "borders", view="vbod1")
th <- createTheme(l_kernel, "raster", view="vbod1")
setVisual(th, visualRaster(color=rev(heat.colors(21))), mode="r")
</code>


other view using the alternative splancs layer
<code R>
thp <- createTheme(l_points, "points", view="vbod2")
th <- createTheme(l_pol, "borders", view="vbod2")

th <- createTheme(l_splancs, "kernelsplancs", view="vbod2")
setVisual(th, visualRaster(color=rev(heat.colors(21))), mode="r")
</code>

checking the sattus of the data base
<code R>
bod
</code>
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**// open and visualise the database in terraView!!! //**