Just announcing that the package 'spatstat' is now fully covered by a Free Open
Source licence.
In previous versions of spatstat, polygon clipping was (optionally) performed
using the package 'gpclib'
which has a restricted licence. From spatstat version 1.34-0 onward, polygon
clipping is perfo
On Wed, 30 Oct 2013, Alex Mandel wrote:
On 10/30/2013 01:59 PM, Adrian Dușa wrote:
Dear All,
A question from a very beginner with maps and geographic data.
I have a map with many (too many) vertices which I would like to use
interactively over a web page. What I need is to simplify that map,
m
On 10/30/2013 01:59 PM, Adrian Dușa wrote:
> Dear All,
>
> A question from a very beginner with maps and geographic data.
> I have a map with many (too many) vertices which I would like to use
> interactively over a web page. What I need is to simplify that map,
> make it to the original one but
Dear All,
A question from a very beginner with maps and geographic data.
I have a map with many (too many) vertices which I would like to use
interactively over a web page. What I need is to simplify that map,
make it to the original one but discarding 90% of the
current information.
I read that
Dear list,
Im trying to calculate the centroid of each clump existent within a raster
layer.
So far I reached to:
require(raster)
r <- raster(ncols=12, nrows=12)
set.seed(0)
r[] <- round(runif(ncell(r))*0.7 )
r=r>0.3
rc <- clump(r)
# the function should be something like this, but im still sorti
Thank you Roger
2013/10/30 Roger Bivand
> On Wed, 30 Oct 2013, David Villalobos wrote:
>
> Hi Manuel
>>
>> Thank you, I tryed your solution and it works partially
>>
>> xysp <- SpatialPoints(xy)
>>
>> proj4string(xysp) <- CRS("+proj=tmerc +lon_0=-84 +lat_0=0 +x_0=50
>> +k=0. +datum=WGS
On Wed, 30 Oct 2013, David Villalobos wrote:
Hi Manuel
Thank you, I tryed your solution and it works partially
xysp <- SpatialPoints(xy)
proj4string(xysp) <- CRS("+proj=tmerc +lon_0=-84 +lat_0=0 +x_0=50
+k=0. +datum=WGS84")
xysp
SpatialPoints:
X Y
[1,] 815196.4
Hi David,
Could the problem be that the function locoh.a does not actually create a
single convex hull, but a multitude. So you have to select the locoh with the
percentage you want by looking at
as.data.frame(locoh)
This will give you a long list, and you select the row which comes closest to
y
Dear Emmanuel, you may want to look into what gstat::krigeTg does, it
might be close or identical to what you want.
On 10/30/2013 03:47 PM, baremma2002 wrote:
> Dear users,
>
> How can I compute the variances of prediction (ordinary kriging) using
> log-transformed data in R:
>
> library(gstat)
Did you look at your shapefile for the CRS?
Manuel
2013/10/30 David Villalobos
> Hi Manuel
>
> Thank you, I tryed your solution and it works partially
>
> xysp <- SpatialPoints(xy)
>
> proj4string(xysp) <- CRS("+proj=tmerc +lon_0=-84 +lat_0=0 +x_0=50
> +k=0. +datum=WGS84")
>
> > xysp
Hi community!
I have a raster of Global Land Cover with a resolution of 1 km
(http://glcf.umd.edu/).
I want to change its resolution to be similar to that of the rest of the
rasters I am using for my project (0.17 degrees).
Besides, I want to recalculate the value for each new cell. In the
Hi Manuel
Thank you, I tryed your solution and it works partially
xysp <- SpatialPoints(xy)
proj4string(xysp) <- CRS("+proj=tmerc +lon_0=-84 +lat_0=0 +x_0=50
+k=0. +datum=WGS84")
> xysp
SpatialPoints:
X Y
[1,] 815196.4 1152758
[2,] 815196.0 1152759
[3,] 815055.
Hi David,
You don´t have the CRS.
Try this:
proj4string(xysp) <- CRS("+proj=tmerc +lon_0=-84 +lat_0=0 +x_0=50
+k=0. +datum=WGS84")
Best,
Manuel Spínola
2013/10/30 David Villalobos
> Dear list,
>
> Maybe a silly problem, but I need help to solve it.
>
> At this moment I am working w
Dear list,
Maybe a silly problem, but I need help to solve it.
At this moment I am working with the R package "adehabitatHR",
specifically with the LoCoH.a (Adaptative LoCoh). I am calculating the
home range of a neotropical bat (Ectophylla alba: Phyllostomidae). My
commands are the followings:
Dear users,
How can I compute the variances of prediction (ordinary kriging) using
log-transformed data in R:
library(gstat)
library(sp)
data(meuse)
coordinates(meuse)=~x+y
data(meuse.grid)
gridded(meuse.grid) = ~x+y
modv <- vgm(psill=0.5, model="Sph", range=900, nug = 0)
fitv <- fit.variogram(va
15 matches
Mail list logo