Hi,
I am having problems with loading the mgcv package, which is used by the
spatstat package. I did not have similar problems with the latest version of
the spatstat, so I imagine that the problem could lay with the mgcv package
(new parameters).
> library(spatstat)
Loading required package
Hi Wesley,
The proj.4 string definition is available at (bookmark it!):
http://trac.osgeo.org/proj/wiki/GenParms
and then a list of projections is available at:
http://www.remotesensing.org/geotiff/proj_list/
all the best from Split,
Tom Hengl
http://spatial-analyst.net/
Don't forget that you can also use different types of unsupervised
classification methods, such as the fuzzy k-means as implemented in the
"kmeans" method.
Here is an example (with landform classes):
http://spatial-analyst.net/wiki/index.php?title=Analysis_of_DEMs_in_R%2BILWIS/SAGA
If you work
Hi all,
I thought that this might be of interest. I have spent few days on trying to
automate donwload and resampling of MODIS products (under windows machines).
The instructions and a sample script is available here:
http://spatial-analyst.net/wiki/index.php?title=Download_and_resampling_of_M
Dear Alina,
If you load two datasets to R and you want to merge them via some key (e.g. zip
code), you have to consider using the "merge" method e.g.:
> spdata <- merge(x=data.frame(ZIP=zipshape$ZIP, X=zipshape$X, Y=zipshape$Y),
> y=data.frame(ZIP=customers$ZIP, V=customers$V), by=ZIP, all.
1st call: GEOSTAT 2009 Summer School
"Spatio-temporal data analysis with R + SAGA + Google Earth"
Period: 3 10 May 2009
Location: Mediterranean Institute For Life Sciences (MEDILS), Split, Croatia
The objective of the GEOSTAT Summer School is to promote use of open source
tools for the analy
If you work with large shapes/grids, try also SAGA:
> rsaga.get.usage(lib="grid_gridding", 3)
SAGA CMD 2.0.3
library path: C:/Progra~1/saga_vc/modules
library name: grid_gridding
module name : Shapes to Grid
...
But before SAGA, you need to reproject the polygons first (if necessary).
see
Dear list,
I discovered recently that Wikipedia has an extensive list of various
administrative/political/thematic/historic maps (which are regularly updated!):
http://en.wikipedia.org/wiki/Wikipedia:Blank_maps
The maps are provided in PNG and SVG (Scalable Vector Graphics) formats. The
later
take a look also at some examples from the book by Reimann et al. (2008). They
actually load a jpg map (showing coast-line/borders) as a background plot
(method "plotbg" in package "StatDA"):
6.4 Spatial Trends
http://www.statistik.tuwien.ac.at/StatDA/R-scripts/page138.html
8.9 Data Subset
Hi Greg,
Just a small remark: it would not be a special treatment because I am anyway
80% of my time located in Amsterdam ;)
Edzer, thanx for fixing this problem. I did run kriging on LatLon projected
data a lot actually. LatLon coordinates are especially attractive because you
can then dire
Alessandro,
The rsaga.geoprocessor needs to get all SAGA parameters via the argument
"param", e.g.:
> rsaga.geoprocessor(lib="geostatistics_grid", module=6,
> param=list(ZONES="plots.sgrd",
CATLIST=0, STATLIST="DCM_1_power10.sgrd", OUTTAB="stat_DCM_1_power10"))
You seem to often forget that s
geodetic datum (dX, dY, dZ) for baranja hill dataset
(http://geomorphometry.org/data.asp) in SAGA, so I get an empty grid!
Unfortunately SAGA does not (yet!) support EPSG codes, so that the choice of
coordinate systems is relatively limited.
Tom Hengl
http://spatial-analyst.net/wiki/index.php?t
> require(rgdal)
> require(adehabitat)
> (file1 <- paste(system.file(package = "adehabitat"),
"ascfiles/elevation.asc", sep = "/"))
> el <- readGDAL(file1)
> image(el, axes=T)
> el.pix <- as(el, "SpatialPixelsDataFrame")
> el.pol <- as.SpatialPolygons.SpatialPixels(el.pix
Dear Edzer,
I do have a similar problem (extensive computation time, memory limits). I
would like to run interpolation of 60,000 points for 500,000 new points using
OK and nmax of 30 closest points (I am using a windowsxp machine with 2GB RAM).
But I have doubts that it can ever run in less
Dear Friederike, miltinho,
FYI I have tried to create table comparison of various geostatistical software
few years ago (this table is available in
http://dx.doi.org/10.1016/j.cageo.2007.05.001). The things is - it is rather
tricky to say which software is 'better' in doing specific tasks. If
Dear Miltinho,
You might want to take a look at this website:
http://spatialreference.org/ref/epsg/?search=SAD69
It allows you to export an existing coordinate system into a proj4 string, e.g.:
Proj4js.defs["EPSG:29170"] = "+proj=utm +zone=20 +ellps=aust_SA +units=m
+no_defs ";
This you can
Dear Yong/Paul,
Yes, I still feel quite reluctant to use R for processing of 'serious'
images/GIS layers.
David Rossiter recently informed me that there is a new R package to handle
huge matrices:
http://cran.r-project.org/web/packages/bigmemory/
Did not yet tested it. Let me know if it work
ECTED]
Sent: Mon 7/7/2008 4:10 AM
To: Hengl, T.
Cc: r-sig-geo@stat.math.ethz.ch
Subject: RE: [R-sig-Geo] kriging [SEC=UNCLASSIFIED]
Dear Tom,
Many thanks for your comments and the relevant information. It seems that we
need to clarify what is RK. The definition of RK is, however, not quite
mailto:[EMAIL PROTECTED]
Sent: Fri 7/4/2008 11:19 AM
To: Frede Aakmann Tøgersen
Cc: Hengl, T.; r-sig-geo@stat.math.ethz.ch; Dave Depew; [EMAIL PROTECTED]
Subject: Re: SV: [R-sig-Geo] kriging
I completely agree with you that this seems the more coherent
statistical modelling approach to these kin
ersus
localized models" in my lecture notes (I like to refer to it because it is an
open-access material).
T. Hengl
http://spatial-analyst.net
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Sent: Fri 7/4/2008 2:18 AM
To: [EMAIL PROTECTED]; Hengl, T.
Cc:
Hi Paul,
Thanks for your info and script.
A year ago, I also felt that much of image processing is really missing in R.
At the moment, I am increasingly using now RSAGA, which is efficient, fast and
easy (+SAGA is still developing; +it works both on Linux and Windows OS). Here
are few example
http://spatial-analyst.net
Hengl, T., 2007. A Practical Guide to Geostatistical Mapping of
Environmental Variables. EUR 22904 EN Scientific and Technical Research
series, Office for Official Publications of the European Communities,
Luxemburg, 143 pp.
http://bookshop.europa.eu/uri?target
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