Hi all,

In fact, sometimes I fill a little frustrated with some map handling on R.
I also had problem with (almost to me) not so large maps reading. The
curious is that the simple ArcView 3.2 read the files without problem (in
GRD or ASC formats).

In fact, I think that the most important thing is the dimension (number of
cols and rows) and not the spatial resolution (10 meters, 250 meters etc).
So, Tom, how large (cols and rows) are your maps?

Kind redards,

miltinho



On 4/22/08, Tomislav Hengl <hengl@science.uva.nl> wrote:
>
>
> Dylan,
>
> Thanks for your note.
>
> A student of mine would like to run habitat suitability analysis using the
> adehabitat package
> (
> http://dx.doi.org/10.1890%2F0012-9658%282002%29083%5B2027%3AENFAHT%5D2.0.CO%3B2).
> I encouraged him
> to use R, for many reasons.
>
> At the moment, he is thinking of doing the whole thing in Matlab (or using
> the original Biomapper
> software), because we would not like to give up on the original resolution
> (250 m).
>
> As a GIS person, I definitively do not see ~20 millions pixels as a Huge
> data set.
>
> cheers,
>
> Tom Hengl
>
>
>
> -----Original Message-----
> From: Dylan Beaudette [mailto:dylan.beaudette@gmail.com]
> Sent: dinsdag 22 april 2008 17:22
> To: Tomislav Hengl
> Cc: r-sig-geo@stat.math.ethz.ch; Michalis Vardakis
> Subject: Re: [R-sig-Geo] Memory limit problems in R / import of maps
>
> On Tue, Apr 22, 2008 at 6:49 AM, Tomislav Hengl <hengl@science.uva.nl>
> wrote:
> >
> >  Dear list,
> >
> >  I know that much has already been said about the memory limit problems.
> If there is any progress
> >  about this problem, we would be interested to hear.
> >
> >  In our project, we are importing 24 maps/bands, each consists of
> 1,450,000 pixels. We further
> would
> >  like to glue all maps into a single data frame (e.g. 'ksc' class in
> adehabitat package; or
> >  'SpatialGridDataFrame' in sp package), but this seems to be impossible.
> >
> >  We tried to run this under windows (after following
> >
>
> http://cran.r-project.org/bin/windows/base/rw-FAQ.html#There-seems-to-be-a-limit-on-the-memory-it-us
> >  es_0021 and setting the --max-mem-size) and under Linux Ubuntu, but
> still get the same error
> message
> >  (seems that there is no difference in use of memory under the two OS):
> >
> >  "Error: Cannot allocate vector of size 11.1 Mb"
> >
> >  The R workspace with 24 loaded grids is also really small (18 MB), but
> any further gluing and
> >  calculation is blocked due the vector size error message.
> >
> >  For a comparison, in a GIS such as ArcGIS or SAGA/ILWIS (open source)
> we have no problems of
> loading
> >  and processing 3-4 times more grids.
> >
> >  Should we simply give up on running spatial analysis using large grids
> (>10 million grids) in R?
>
> Hi,
>
> What exactly were you hoping to do with such a massive data frame once
> you overcame the initial memory problems associated with loading the
> data? Any type of multivariate, classification, or inference testing
> would certainly require just as much memory to perform any analysis on
> the stack of grids.
>
> Not knowing what the purpose of this operation is (although I would
> guess something related to soil property or landscape modeling of some
> sort), it is hard to suggest a better approach. For grid that size I
> would use an algorithm that operates on strips or tiles. There are
> several great starting points in the GRASS source code. Doing all of
> the pre-processing, and possibly some aggregating to larger support
> size, in GRASS would allow you to test any R-centric operations on a
> coarser version of the original dataset.
>
> Cheers,
>
> Dylan
>
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