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Updated: 1 hour 29 min ago

stConstruct with multiple SpatialPolygonsDataFrame objects

Mon, 05/21/2018 - 19:40
Hi, Can anyone provide an example of using stConstruct() with multiple SpatialPolygonsDataFrame objects?  I have created the latter by reading in multiple shapefiles, one shapefile per year.  Each shapefile contains polygons at several levels of an attribute.  I want to plot these polygons for levels of say 1, 5, 10  using stplot, where the panels are years.  I am having trouble understanding how to do this type of task where the spatial coordinates vary across time.

Thank you,
Scott Waichler
Pacific Northwest National Laboratory
scott.waichler _at_ pnnl.gov

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Running spacetime eof on large raster stack

Mon, 05/21/2018 - 18:55
Hi,

I am trying to run the spatial empirical orthogonal function on a raster stack using eof function in spacetime 1.2-1 package.

My raster stack is

> stk
class       : RasterStack
dimensions  : 2423, 2470, 5984810, 690  (nrow, ncol, ncell, nlayers)
resolution  : 499.9339, 499.9718  (x, y)
extent      : -85163.44, 1149673, 5656775, 6868207  (xmin, xmax, ymin, ymax)
coord. ref. : NA
names       : X2001.01.01, X2001.01.09, X2001.01.17, X2001.01.25, X2001.02.02, X2001.02.10, X2001.02.18, X2001.02.26, X2001.03.06, X2001.03.14, X2001.03.22, X2001.03.30, X2001.04.07, X2001.04.15, X2001.04.23, ...
min values  :           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0,           0, ...
max values  :         100,         100,         100,         100,         100,         100,         100,         100,         100,         100,         100,         100,         100,         100,         100, ...

>object.size(stk)
8699416 bytes

The size of raster stack is ~8 mb but when I try to convert to STFDF it become several GBs until I receive error
about memory.

My system is
sysname        release        version        machine
"Windows"     ">= 8 x64"    "build 9200"      "x86-64"

>memory.limit()
[1] 32659

My R version is
"R version 3.5.0 (2018-04-23)"

I would appreciate any advise on possible solutions.

Thank you.

Shiva


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spatio temporal anisotropy: help with stAni argument

Mon, 05/21/2018 - 08:16
Hi,
I have some problems in understanding the usage of argument 'stAni' of
vgmST and krigeST functions, for.the setting of spatio temporal anisotropy.
How have I to interpret this argument, that is the integer value that I saw
I have to assign to it, what does it means?
How can this argument influence the result of prediction?
Are units of spatial and temporal ranges and lags related to this argument?
How can I study the spatio temporal anisotropy of my data?
Kind regards.

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Re: Intersection of polygons with raster

Sat, 05/19/2018 - 12:40
Thank you Michael, that is exactly what I needed. It is fairly slow for my
case but I only need to do it once :-)

And thank you for providing an example, I'm not too familiar with rasters
and would have had some trouble creating it.

Kent

On Fri, May 18, 2018 at 11:41 PM, Michael Sumner <[hidden email]> wrote:

> Hi Kent, this is pretty straightforward with raster::extract. If speed is
> an issue you can burn the polygon id into the grid and then group values by
> that.
>
> library(raster)
> r <- raster(volcano)
>
> ## very simplistic polygon layer example
> r2 <- raster(r)
> res(r2) <- res(r2) * 20
> p <- sf::st_as_sf(as(r2, "SpatialPolygonsDataFrame"))
>
> ## a dummy threshold value
> onefoot <- 150
> ## grab the first column from extract (it gives multiple columns for
> multi-layer rasters)
> p$onefoot <- extract(r, p, fun = function(x, na.rm = TRUE) any(x >
> onefoot))[,1]
>
> plot(sf::st_geometry(p), col = p$onefoot + 1)
> contour(r, levels = onefoot, col = "white", add = T)
>
>
> ## if speed is an issue, use fasterize for a more abstract workflow
> library(fasterize)
> p$rownum <- 1:nrow(p)
> idgrid <- fasterize(p, r, field = "rownum")
> p$onefoot <- tapply(values(r), values(idgrid), function(x, na.rm = TRUE)
> any(x > onefoot))
> plot(p)
>
> FWIW it's always useful to provide a reprex, since that does take time and
> might miss the mark for your situation. It's also trickier to provide two
> objects that are relevant to each other, so any upfront work you can do in
> an example helps the answerer.
>
> HTH
>
> On Sat, 19 May 2018 at 12:20 Kent Johnson <[hidden email]> wrote:
>
>> Hi,
>>
>> I have a raster object `flood` containing projected flooding levels and a
>> simple features object `parcels` containing MULTIPOLYGONs representing
>> property parcels. I would like to find all the parcel polygons for which
>> there is any flood > 1 foot. What function(s) can do this? Something like
>> raster::intersect(parcels, flood >= 1)
>>
>> I'll put together a reprex if that helps. Hoping someone can point me to
>> the right function to do this, I haven't found anything promising.
>>
>> Thanks,
>> Kent
>>
>>         [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
> --
> Dr. Michael Sumner
> Software and Database Engineer
> Australian Antarctic Division
> 203 Channel Highway
> <https://maps.google.com/?q=203+Channel+Highway+Kingston+Tasmania+7050+Australia&entry=gmail&source=g>
> Kingston Tasmania 7050 Australia
> <https://maps.google.com/?q=203+Channel+Highway+Kingston+Tasmania+7050+Australia&entry=gmail&source=g>
>
>
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Re: Intersection of polygons with raster

Fri, 05/18/2018 - 22:41
Hi Kent, this is pretty straightforward with raster::extract. If speed is
an issue you can burn the polygon id into the grid and then group values by
that.

library(raster)
r <- raster(volcano)

## very simplistic polygon layer example
r2 <- raster(r)
res(r2) <- res(r2) * 20
p <- sf::st_as_sf(as(r2, "SpatialPolygonsDataFrame"))

## a dummy threshold value
onefoot <- 150
## grab the first column from extract (it gives multiple columns for
multi-layer rasters)
p$onefoot <- extract(r, p, fun = function(x, na.rm = TRUE) any(x >
onefoot))[,1]

plot(sf::st_geometry(p), col = p$onefoot + 1)
contour(r, levels = onefoot, col = "white", add = T)


## if speed is an issue, use fasterize for a more abstract workflow
library(fasterize)
p$rownum <- 1:nrow(p)
idgrid <- fasterize(p, r, field = "rownum")
p$onefoot <- tapply(values(r), values(idgrid), function(x, na.rm = TRUE)
any(x > onefoot))
plot(p)

FWIW it's always useful to provide a reprex, since that does take time and
might miss the mark for your situation. It's also trickier to provide two
objects that are relevant to each other, so any upfront work you can do in
an example helps the answerer.

HTH

On Sat, 19 May 2018 at 12:20 Kent Johnson <[hidden email]> wrote:

> Hi,
>
> I have a raster object `flood` containing projected flooding levels and a
> simple features object `parcels` containing MULTIPOLYGONs representing
> property parcels. I would like to find all the parcel polygons for which
> there is any flood > 1 foot. What function(s) can do this? Something like
> raster::intersect(parcels, flood >= 1)
>
> I'll put together a reprex if that helps. Hoping someone can point me to
> the right function to do this, I haven't found anything promising.
>
> Thanks,
> Kent
>
>         [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Dr. Michael Sumner
Software and Database Engineer
Australian Antarctic Division
203 Channel Highway
Kingston Tasmania 7050 Australia

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Intersection of polygons with raster

Fri, 05/18/2018 - 21:20
Hi,

I have a raster object `flood` containing projected flooding levels and a
simple features object `parcels` containing MULTIPOLYGONs representing
property parcels. I would like to find all the parcel polygons for which
there is any flood > 1 foot. What function(s) can do this? Something like
raster::intersect(parcels, flood >= 1)

I'll put together a reprex if that helps. Hoping someone can point me to
the right function to do this, I haven't found anything promising.

Thanks,
Kent

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Re: Raster extract by polygon generating NAs

Thu, 05/17/2018 - 04:45
Thank you Ben, setting na.rm = T did the job.

Best wishes
Joao

On 16 May 2018 at 19:36, Ben Tupper <[hidden email]> wrote:
> Hi,
>
> It's hard to know without any reproducible code, but you will want to pay
> close attention to the value of the na.rm argument to
> raster::extract(Raster,SpatialPolygons)
>
> See ?extract for all the details.
>
> Cheers,
> Ben
>
> On May 16, 2018, at 2:27 PM, João Carreiras <[hidden email]> wrote:
>
> Greetings!
>
> I'm using the extract command (raster package) with a raster layer (x)
> and a spatial polygons dataframe (y). I'm using it to extract the sum
> of raster values by each spatial polygon. However, I'm getting NAs as
> a result for some polygon IDs, which I know isn't true because that
> doesn't happen in ArcGIS.
>
> Does anyone experienced the same issue?
>
> Thanks!
> Joao
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
>
> Ben Tupper
> Bigelow Laboratory for Ocean Sciences
> 60 Bigelow Drive, P.O. Box 380
> East Boothbay, Maine 04544
> http://www.bigelow.org
>
> Tick Forecasting: https://eco.bigelow.org/
>
>
>
>
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Re: Raster extract by polygon generating NAs

Wed, 05/16/2018 - 13:36
Hi,

It's hard to know without any reproducible code, but you will want to pay close attention to the value of the na.rm argument to raster::extract(Raster,SpatialPolygons)  

See ?extract for all the details.

Cheers,
Ben

> On May 16, 2018, at 2:27 PM, João Carreiras <[hidden email]> wrote:
>
> Greetings!
>
> I'm using the extract command (raster package) with a raster layer (x)
> and a spatial polygons dataframe (y). I'm using it to extract the sum
> of raster values by each spatial polygon. However, I'm getting NAs as
> a result for some polygon IDs, which I know isn't true because that
> doesn't happen in ArcGIS.
>
> Does anyone experienced the same issue?
>
> Thanks!
> Joao
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
Ben Tupper
Bigelow Laboratory for Ocean Sciences
60 Bigelow Drive, P.O. Box 380
East Boothbay, Maine 04544
http://www.bigelow.org

Tick Forecasting: https://eco.bigelow.org/





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Raster extract by polygon generating NAs

Wed, 05/16/2018 - 13:27
Greetings!

I'm using the extract command (raster package) with a raster layer (x)
and a spatial polygons dataframe (y). I'm using it to extract the sum
of raster values by each spatial polygon. However, I'm getting NAs as
a result for some polygon IDs, which I know isn't true because that
doesn't happen in ArcGIS.

Does anyone experienced the same issue?

Thanks!
Joao

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Re: Will upcoming ggplot2::calc() clash with raster::calc()?

Wed, 05/16/2018 - 13:05
We are tracking at https://github.com/tidyverse/ggplot2/issues/2614 —
and have come up with a name that I prefer to calc(): stat().

Hadley

On Wed, May 16, 2018 at 9:42 AM, Barry Rowlingson
<[hidden email]> wrote:
> It seems the options are:
>
> 1. ggplot2 and raster use calc - scripts will have to use raster::calc or
> ggplot2::calc to be ambiguous.
>
>  This is the painful solution. Scripts will break. Users will have to type
> raster::calc or ggplot2::calc depending on the order they do
> library(raster);library(ggplot2). Both packages are often used together.
>
> 2. `calc` becomes a "generic" and despatches to the right specific function
> when called on a raster or whatever ggplot2::calc needs.
>
>  This either requires "ggplot2" to have "raster" as a dependency or for the
> generic to be moved to a package that each of these will depend on. An S3
> or S4 generic? Will it even work? Nobody wants to manage the dependency.
>
> 3. Use a different name in `ggplot2`, for example, `val`, which has the
> same sense - compare:
>
>    geom_histogram(aes(y = *calc*(count)))
>    geom_histogram(aes(y = *val*(count)))
>
> 4. ggplot2 does something else - maybe the aesthetic could be specified as
> a formula?
>
>   geom_histogram(aes(y = ~count))
>
>  but that would require a chunk of ggplot2 rewriting
>
> Barry
>
>
>
>
>
> On Tue, May 15, 2018 at 6:58 PM, Edzer Pebesma <
> [hidden email]> wrote:
>
>> This was asked on twitter:
>> https://twitter.com/hadleywickham/status/996430251499536385 , by Hadley
>> Wickham:
>>
>> "Can any raster user comment on if the new ggplot2::calc() function is
>> going to cause significant pain because it clashes with raster::calc()?"
>>
>> I'll report answers back to twitter, or directly to Hadley if it doesn't
>> fit in 280 chars.
>> --
>> Edzer Pebesma
>> Institute for Geoinformatics
>> Heisenbergstrasse 2, 48151 Muenster, Germany
>> Phone: +49 251 8333081
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
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>
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--
http://hadley.nz

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Re: Will upcoming ggplot2::calc() clash with raster::calc()?

Wed, 05/16/2018 - 11:42
It seems the options are:

1. ggplot2 and raster use calc - scripts will have to use raster::calc or
ggplot2::calc to be ambiguous.

 This is the painful solution. Scripts will break. Users will have to type
raster::calc or ggplot2::calc depending on the order they do
library(raster);library(ggplot2). Both packages are often used together.

2. `calc` becomes a "generic" and despatches to the right specific function
when called on a raster or whatever ggplot2::calc needs.

 This either requires "ggplot2" to have "raster" as a dependency or for the
generic to be moved to a package that each of these will depend on. An S3
or S4 generic? Will it even work? Nobody wants to manage the dependency.

3. Use a different name in `ggplot2`, for example, `val`, which has the
same sense - compare:

   geom_histogram(aes(y = *calc*(count)))
   geom_histogram(aes(y = *val*(count)))

4. ggplot2 does something else - maybe the aesthetic could be specified as
a formula?

  geom_histogram(aes(y = ~count))

 but that would require a chunk of ggplot2 rewriting

Barry





On Tue, May 15, 2018 at 6:58 PM, Edzer Pebesma <
[hidden email]> wrote:

> This was asked on twitter:
> https://twitter.com/hadleywickham/status/996430251499536385 , by Hadley
> Wickham:
>
> "Can any raster user comment on if the new ggplot2::calc() function is
> going to cause significant pain because it clashes with raster::calc()?"
>
> I'll report answers back to twitter, or directly to Hadley if it doesn't
> fit in 280 chars.
> --
> Edzer Pebesma
> Institute for Geoinformatics
> Heisenbergstrasse 2, 48151 Muenster, Germany
> Phone: +49 251 8333081
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
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Will upcoming ggplot2::calc() clash with raster::calc()?

Tue, 05/15/2018 - 12:58
This was asked on twitter:
https://twitter.com/hadleywickham/status/996430251499536385 , by Hadley
Wickham:

"Can any raster user comment on if the new ggplot2::calc() function is
going to cause significant pain because it clashes with raster::calc()?"

I'll report answers back to twitter, or directly to Hadley if it doesn't
fit in 280 chars.
--
Edzer Pebesma
Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081

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Resources and Technical issues about Spatio Temporal Kriging

Mon, 05/14/2018 - 08:52
Hi,
I am trying to do spatio temporal kriging. I have produced some prediction
maps, but during the process, I noticed something of strange:
during the execution of the function krigeST I saw a high usage of RAM
memory and Disk, which sometimes produced the crash of RStudio. It is
normal? I am using a spatio-temporal grid of about 2000 points as 'newdata'
parameter in krigeST function.
Another issues is that the result of the function krigeST seems to be a
multiple map (for example I made a temporal grid of length 2, so I got 2 in
1 prediction  map as the result of krigeST). I am not sure if the "number"
of prediction maps depends on the length of temporal grid. In any way, it
could.be possible to obtain a unique prediction map as the result of
krigeST (as in the pure spatial context, for example)?

Kind regards.

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Re: Help

Mon, 05/14/2018 - 06:42
Dear Soufianou,
this is just a framework. Let's say that you have a vector ('variables')
containing the name of the environmental variables.

library(raster)
library(dismo)
library(corrplot)

for (variable in variables) {
     assign(variable, raster(paste0(variable, ".asc"))
}
environment <- brick(variables)
environment_standardized <- data.frame(scale(x =
as.data.frame(environment), center = TRUE, scale = TRUE))

correlation_matrix <- cor(environment_standardized, use = "na.or.complete")
corrplot(corr = correlation_matrix)
VIF <- vif(environment_standardized)
CN <- kappa(na.omit(environment_standardized), exact = TRUE)
# You can select variables that fulfill your criteria about correlation
structure
selected_variables <- variables[c()] # subsetting

maxent(x = environment[[selected_variables]] , p = presence_points)

HTH,
Ákos Bede-Fazekas
Hungarian Academy of Sciences

2018.05.14. 12:28 keltezéssel, Soufianou Abou via R-sig-Geo írta:
> Dear Rolf Turner,
>
>   I have points of presence of cowpea in Niger in CSV format; in addition to other variables (soil texture, soil pH, altitude, I downloaded from worldclim archives, the 19 environmental variables, I cut them all at the Niger scale and I converted them under ASCUI format. The idea for me is to choose the best variables to include in the model.
>   NB. I'm using Maxent model, but I'm not good in R software.
>
> Merci
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> SADDA Abou-Soufianou
>
> --------------------------------------
>
> Doctorant
>
> Université Dan Dicko Dankoulodo deMaradi-Niger
>
> BP 465 120, avenue MamanKoraou- ADS
>
>                     &
>
> Institut d’Ecologie et des Sciencesde l’Environnement de Paris (iEES-Paris)
>
> Centre IRD France Nord-(iEES Paris)-32,av.Henri Varangnat 93143 BONDY cedex.
>
> |
> Lien: https://ieesparis.ufr918.upmc.fr/index.php?page=fiche&id=378&droit=1
>
>
>   [hidden email]
>
> GSM : Niger : (+227) 96-26-99-87/91-56-35-19 ; France (+ 33)  07-55-79-39-93
>
>  
>    |
>  
>    |
>
>
>
>
>
>
>
>
>
>
>      Le lundi 14 mai 2018 à 12:05:40 UTC+2, Rolf Turner <[hidden email]> a écrit :
>
>
>
> Please keep your posts "on-list".  You are much more likely to get a
> useful answer that way.  There are many others on the list whose
> knowledge and insight are far greater than mine.
>
> I have therefore cc-ed the list in this reply.
>
> On 14/05/18 21:48, Soufianou Abou wrote:
>
>> Thank you for advice, Rolf Turner
>>
>> My question is as follows:
>>
>> I'd use maxent to model the potential distribution of cowpea on the
>> basis of the only presence data. Indeed, I have acquired a number of
>> environmental variables and bioclimatic regarding my area of study. But
>> to choose the most contributive variables in the model; I would like to
>> make a correlation analysis of these. On this, could you explain to me
>> the step by step procedures to follow in R? I would like to say scripts
>> for:- compile and call all environmental variables;- run the correlation
>> test to select the least correlated ones.
> As I said before, I don't think this is the right approach, but I can't
> be sure without knowing more about your data.  I find your description
> to be vague.
>
> How are your data stored?  What information do you have about the
> "distribution of cowpea".  Do you have *points* where cowpea is present
> or more extensive *regions* where it is present?  (And could these
> regions be "considered to be points" on the scale of interest?) How are
> your predictors stored?  Are the values of these predictors known at
> every point of your study area?  Can you show us a bit of your data (use
> the function dput() to include *a small sample* of your data in the body
> of your email).
>
> If you insist on mucking about with correlation and testing, perhaps the
> function cor.test() will give you what you want.  I reiterate however
> that this seems to me to be a wrong approach.
>
> cheers,
>
> Rolf Turner
>
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Help

Mon, 05/14/2018 - 05:28
Dear Rolf Turner,

 I have points of presence of cowpea in Niger in CSV format; in addition to other variables (soil texture, soil pH, altitude, I downloaded from worldclim archives, the 19 environmental variables, I cut them all at the Niger scale and I converted them under ASCUI format. The idea for me is to choose the best variables to include in the model.
 NB. I'm using Maxent model, but I'm not good in R software.

Merci














SADDA Abou-Soufianou

--------------------------------------

Doctorant

Université Dan Dicko Dankoulodo deMaradi-Niger

BP 465 120, avenue MamanKoraou- ADS

                   &

Institut d’Ecologie et des Sciencesde l’Environnement de Paris (iEES-Paris)

Centre IRD France Nord-(iEES Paris)-32,av.Henri Varangnat 93143 BONDY cedex.

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    Le lundi 14 mai 2018 à 12:05:40 UTC+2, Rolf Turner <[hidden email]> a écrit :  



Please keep your posts "on-list".  You are much more likely to get a
useful answer that way.  There are many others on the list whose
knowledge and insight are far greater than mine.

I have therefore cc-ed the list in this reply.

On 14/05/18 21:48, Soufianou Abou wrote:

> Thank you for advice, Rolf Turner
>
> My question is as follows:
>
> I'd use maxent to model the potential distribution of cowpea on the
> basis of the only presence data. Indeed, I have acquired a number of
> environmental variables and bioclimatic regarding my area of study. But
> to choose the most contributive variables in the model; I would like to
> make a correlation analysis of these. On this, could you explain to me
> the step by step procedures to follow in R? I would like to say scripts
> for:- compile and call all environmental variables;- run the correlation
> test to select the least correlated ones.
As I said before, I don't think this is the right approach, but I can't
be sure without knowing more about your data.  I find your description
to be vague.

How are your data stored?  What information do you have about the
"distribution of cowpea".  Do you have *points* where cowpea is present
or more extensive *regions* where it is present?  (And could these
regions be "considered to be points" on the scale of interest?) How are
your predictors stored?  Are the values of these predictors known at
every point of your study area?  Can you show us a bit of your data (use
the function dput() to include *a small sample* of your data in the body
of your email).

If you insist on mucking about with correlation and testing, perhaps the
function cor.test() will give you what you want.  I reiterate however
that this seems to me to be a wrong approach.

cheers,

Rolf Turner

--
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Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
 
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Re: [FORGED] Help

Mon, 05/14/2018 - 04:37

On 14/05/18 20:17, Soufianou Abou via R-sig-Geo wrote:

> Bonjour , j'aimerais utiliser  maxent pour modéliser la distribution
> potentielle du niébé sur la base des données de présence seuls. En
> effet, jai acquis un certains nombre de variables environnementales
> et bioclimatiques concernant ma zone d'étude.  Mais pour choisir les
> variables les plus contributives dans le modèle; j'aimerai faire une
> analyse de correlation de celles-ci. Sur ce, pourriez vous
> m'expliquer etape par etape les procedures à suivre sous R ?
> J'aimerais dire par là le scripts pour:  -    compiler et appeler
> toutes les variables environnementales et les données d'occurence; -
> executer le tester de correlation;-    pour faire une analyse
> discriminante?
>
> Merci par avance. La langue de cette liste est l'anglais.
S'il vous plaît exprimer votre question en anglais.

I'm afraid that my French is insufficient to follow your question
properly, but I gather that you have presence-only data (for some
phenomenon) and a number of environmental variables from which you hope
to predict occurrences of this phenomenon.

You also express an interest in undertaking a correlation analysis of
your predictors and performing "tests of correlation".

Given that I am understanding you correctly, I would advise against
this.  The proper strategy (IMHO) is to *fit a model* using your
predictors and then assess their predictive power in this model,in some way.

If the "presence only" data, that you have, can be considered to be
point locations, and if the values of your predictors are available at
all points of your study region, then you may be able to effect the
required model fitting using the facilities of the spatstat package.

Anyway, please re-post your question en anglais, if you can.  You are
much more likely to get a useful answer if you do.  Bon chance.

Cordialement,

Rolf Turner

--
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Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276

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Help

Mon, 05/14/2018 - 03:17
Bonjour , j'aimerais utiliser  maxent pour modéliser la distribution potentielle du niébé sur la base des données de présence seuls. En effet, jai acquis un certains nombre de variables environnementales et bioclimatiques concernant ma zone d'étude.  Mais pour choisir les variables les plus contributives dans le modèle; j'aimerai faire une analyse de correlation de celles-ci. Sur ce, pourriez vous m'expliquer etape par etape les procedures à suivre sous R ? J'aimerais dire par là le scripts pour:  -    compiler et appeler toutes les variables environnementales et les données d'occurence; -    executer le tester de correlation;-    pour faire une analyse discriminante?

Merci par avance














SADDA Abou-Soufianou











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INLA

Mon, 05/14/2018 - 01:16
Hi,
I am new on INLA. I was wondering, how di i extract the parameters
estimates from an sdm.inla object? does sdm_inla also work for spatial
durbin probit model? I will highly appreciate your inputs.
Thanks,
Jose
University of Maryland at College Park
Department of Geographical Sciences

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Re: Help about gstat's functions for spatio temporal data interpolation

Sun, 05/13/2018 - 04:56
Dear Bruno,

you'll find everything at https://github.com/edzer/gstat

On 05/12/2018 09:05 PM, Bruno Sesti wrote:
> Hi, I am trying to do spatio-temporal kriging and I am interested
> especially in the software implementation of the algorithm and of data
> analysis. I saw that R have many libraries and routines to perform spatial
> and spatii-temporal kriging. I would like to know if it could be possible
> to get the source code of some functions that package gstat offers to
> perform spatio-temporal kriging, that are:
> variogramST(), vgmST(), fit.StVariogram().
>
> Kind regards.
>
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>
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>
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Institute for Geoinformatics
Heisenbergstrasse 2, 48151 Muenster, Germany
Phone: +49 251 8333081

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Help about gstat's functions for spatio temporal data interpolation

Sat, 05/12/2018 - 14:05
Hi, I am trying to do spatio-temporal kriging and I am interested
especially in the software implementation of the algorithm and of data
analysis. I saw that R have many libraries and routines to perform spatial
and spatii-temporal kriging. I would like to know if it could be possible
to get the source code of some functions that package gstat offers to
perform spatio-temporal kriging, that are:
variogramST(), vgmST(), fit.StVariogram().

Kind regards.

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