It is possible, and one way to see an example would be to look in the
raster package source.
But, whether it will help depends on what the target software is.
(GDAL 1.9.2 doesn't seem to understand the metadata written by raster
for example.)
On Sat, Aug 31, 2013 at 4:52 AM, Dominik Schneider
wr
I had related situation with 85000 non-overlapping polygons, and a raster
dataset with dimensions ~ 1x1. In that case I wanted the full
distribution of pixel values inside each polygon, rather than just the mean.
An efficient approach (much faster than extract) was:
1) Rasterize the polyg
Tom -
Thanks for the suggestion. You're right - for these coarse-scale data, the
value at the centroid of the polygons would work, so I'll play with that
and give it a whirl this weekend.
I was thinking that if I'm using R, I would try to use the weighted average
of the values of climate variabl
> >
Thanks Roger for your comprehensive answer, I've been offline for a couple
of days.
Sarah, I think you may be very close to your first objective. Try removing
the quotes around data3, (because data3 is an object not a string) :
malMap <- joinCountryData2Map(dF = data3, joinCode = "ISO3",
Hi Tom, List members,
Thanks for the reply. My polygons constitute a tiling of 100m or 200m
square polygons, with no overlaps. I'll be trying to make predictions for
the smaller scale, but some of my input variables like Climate are at
coarser (800m) resolution, so I'm trying to combine these data
Mike--
Do your 96000 polygons overlap (e.g., species ranges for 96k species), or
do they constitute a tiling with tiny polygons relative to the raster cell
size (e.g., a vegetation map v. coarse raster climate data)? Also, roughly
how many vertices do your polygons have?
I suspect that there isn'
Thank you for the help, everyone.
Andy, this did fix the country matching problem! Thank you very much.
I am now trying to divide the data into just 3 categories, but I will
move this over to the google group.
Cheers,
Sarah
On Fri, Aug 30, 2013 at 11:04 AM, Andy South wrote:
>> >
>
> Thanks Ro
Is it possible to specify the coordinate system when creating a netcdf file
with the ncdf4 package? I have not been able to see anything in the
documentation.
Thanks
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I cannot seem to find a method for converting a spatial polygon class file to
a spatial lines class. Most of the methods seem to be manually, but I am
hoping there is a simpler method. Any help would be appreciated.
Matt
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That was simple - Thanks! Now I just have to deal with the turning angles
that are too large for the function I am using!
Matt
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I believe it's as simple as:
as(yourObject, 'SpatialLines')
On Fri, Aug 30, 2013 at 3:01 PM, mattguzz...@gmail.com <
mattguzz...@gmail.com> wrote:
> I cannot seem to find a method for converting a spatial polygon class file
> to
> a spatial lines class. Most of the methods seem to be manually,
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Mike--
So that implies that you 96k tiny polygons aren't really polygons with area
that matters, and certainly not area that you need to average the raster
data over. What you have are streams, and you want to extract attributes
from raster climate data along each stream.
You could replace your 9
On Fri, 30 Aug 2013, Paul Bidanset wrote:
Thank you. I'd like to subset into a specific county. Should there be
further partitioning from that level?
No idea. Please re-create your scenario by subsetting georgia and the
coordinates to suit.
Roger
On Fri, Aug 30, 2013 at 10:19 AM, Roger
Do not post HTML!
Yes, you have reversed your eastings and northings, so -115N is out of
bounds for obvious reasons.
Roger
On Fri, 30 Aug 2013, Ludmila Rattis wrote:
Hi everyone,
I tried to convert five different matrix of geographic coordinates using
this code, but just two didn't work:
Hi All,
I've been trying to use the extract function (in the raster packages) to
get values of a raster entered as attributes of series of polygons, and
have tested it successfully with a reduced dataset, but am concerned that
my full dataset might be too big.
My full datasets involve ~46000 and
Thank you. I'd like to subset into a specific county. Should there be
further partitioning from that level?
On Fri, Aug 30, 2013 at 10:19 AM, Roger Bivand wrote:
> On Fri, 30 Aug 2013, Paul Bidanset wrote:
>
> Alrighty then!
>>
>
> Thanks. Now make this your case by subsetting georgia in a way
On Fri, 30 Aug 2013, Paul Bidanset wrote:
Alrighty then!
Thanks. Now make this your case by subsetting georgia in a way that
matches your case (all counties west of x?, random set?), and we may be
getting closer. In the geographical partition, the fit points are all a
long way from the data
Alrighty then!
Say I create this adaptive bandwidth model using the original dataset
"georgia"
coords = cbind(georgia$x, georgia$y)
bwsel <- gwr.sel(PctBach ~ TotPop90 + PctRural + PctEld + PctFB + PctPov +
PctBlack, data=georgia, adapt=TRUE, coords, gweight=gwr.Gauss, method =
"aic" )
bw1 <- gw.
Hi everyone,
I tried to convert five different matrix of geographic coordinates using
this code, but just two didn't work:
*The code:*
gila_centroid_latlong<-data.frame(Y=gila_centroid[,2],X=gila_centroid[,3])
coordinates(gila_centroid_latlong) <- ~ X + Y
##*And the error occurs here:*.
proj4s
Provide a reproducible code example of your problem using a built in data
set. No reproducible example, no response, as I cannot guess (and likely
nobody else can either) what your specific misunderstanding is. Code using
for example the Georgia data set in the package. You seem to be assuming
Roger,
I think all I would like to know is if it is possible to apply a calibrated
GWR model to a hold-out sample, and if so, what the most accurate way to do
so is. I understand the pitfalls of GWR but would like to learn as much as
I can before progressing to the next spatial methodology I learn
Dear allï¼
Currently, I attempted to compare spatial aggregation of species across
latitude. I plan to use pcf.ppp() function in âspatstatâ package. I also
have a question about the parameter âbandwidthâ in pcf function.In
pcf.ppp() function, the argument bw (bandwidth) is a default val
Paul, Luis,
I suspect that your speculations are completely wrong-headed. Please
provide a reproducible example with a built-in data set, so that there is
at least minimal clarity in what you are guessing. Note in addition that
GWR as a technique should not be used for anything other than expl
> Thank you Luis. When calibrating the adaptive model, using adapt=t in the
> bandwidth selection created the proportion you speak of, which then allowed
> me to create a bandwidth matrix using gwr.adapt. However, this has not
> worked for me with holdout samples. Have you had success in this regar
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