[R-sig-Geo] [DKIM] Re: FW: Spatial sampling design using information more than spatial coordinates [SEC=UNCLASSIFIED]
Jin.Li at ga.gov.au
Jin.Li at ga.gov.au
Mon Apr 13 03:57:07 CEST 2015
Tom #1,
After discussion, the needs of the design now have been simplified as:
1) stratify the survey area based on both spatial coordinates and bathymetry zones and
2) then pinpoint the locations for 100 samples.
The purpose of this survey to my understanding is to collect some baseline information of seabed sediment and water properties as currently no information is available for these properties in the region to be surveyed.
I will try what you suggested and see if it makes our client happy. Thank you a lot for your kind help!
BTW, the function spsample.prob seems not available in the current GSIF (I am using R version 3.1.0 and just updated GSIF). Where could I get it or should I install the latest version of R?
Regards,
Jin
-----Original Message-----
From: Tomislav Hengl [mailto:hengl at spatial-analyst.net]
Sent: Friday, 10 April 2015 8:09 PM
To: Li Jin; tephilippi at gmail.com
Cc: r-sig-geo at r-project.org; Ichsani Wheeler
Subject: Re: [DKIM] Re: [R-sig-Geo] FW: Spatial sampling design using information more than spatial coordinates [SEC=UNCLASSIFIED]
Jin,
When it comes to stratification prior to any model building (hence no prior predictions) I think that a robust way to stratify an area would be to use a combination of PCA and unsupervised fuzzy k-means. Here is an example:
R> library(GSIF)
R> library(plotKML)
R> library(sp)
R> data(eberg_grid)
R> gridded(eberg_grid) <- ~x+y
R> proj4string(eberg_grid) <- CRS("+init=epsg:31467") formulaString <- ~
R> PRMGEO6+DEMSRT6+TWISRT6+TIRAST6 eberg_spc <- spc(eberg_grid,
R> formulaString)
Converting PRMGEO6 to indicators...
Converting covariates to principal components...
R> kmeans.eberg <- kmeans(eberg_spc at predicted@data, 4)
R> eberg_grid$cluster.4 <- as.factor(kmeans.eberg$cluster)
R> spplot(eberg_grid["cluster.4"], col.regions=rainbow(4))
I had problems following the procedure you described (a combination of geographical clustering first, then sampling in feature space). Maybe you do only need to use the clhs
(http://www.inside-r.org/packages/cran/clhs/docs) or lhs package because this is a standard statistical method when it comes to sampling in feature space? It all depends on what is the purpose of sampling - validation, model building (maximum representation)...
HTH
Tom #1
On 9-4-2015 3:49, Jin.Li at ga.gov.au wrote:
> Hi Tom 2 :-),
>
> To be specific, we need to do the spatial stratification first and
> need to stratify the survey area into n equal-area strata. The
> function stratify with equalArea in spcosa meets this need well.
>
> And then within each stratum, we need to do further stratification
> based on a raster layer of 'elevation' that is a continuous layer and
> needs to be converted into 3 zones. We need to sample m samples within
> each stratum and these samples need to be evenly distributed within
> each zone. That is we need to use the area of each zone as a weight to
> decide how many samples should be allocated to each zone. And then do
> randomly sampling within each zone.
>
> A further challenge is that they plan to sample 1 to 4 samples a day
> during 25 days, so we need to provide four designs with 25, 50, 75 and
> 100 samples respectively. The samples of the design with smaller
> sample size need to be nested in the samples of the design with larger
> sample size for operational reasons as the survey needs to meet other
> tasks and also to accommodate weather conditions at sea. Now you know
> the 'elevation' is bathymetry that is the depth of water. We plan to
> let n = 25, and m = 1 to 4. But it is hard to stratify if a stratum
> contains 3 zones and m <3.
>
> Hope this clarify things a bit. Any further suggestions are appreciated!
>
> Jin
>
> -----Original Message-----
> From: R-sig-Geo [mailto:r-sig-geo-bounces at r-project.org] On Behalf Of
> Tom Philippi
> Sent: Thursday, 9 April 2015 9:32 AM
> To: Tomislav Hengl
> Cc: r-sig-geo
> Subject: [DKIM] Re: [R-sig-Geo] FW: Spatial sampling design using
> information more than spatial coordinates [SEC=UNCLASSIFIED]
>
> Jin--
>
> Could you provide more information on what the needs of your client
> are that cannot be handled by either stratification or unequal
> probability based on your one or more covariates in spsurvey? Also,
> is your sample frame points, polygons, or raster? I assume your
> covariates are raster (e.g., DEM for elevation) and polygons (most soil maps)?
>
> Tom 2
>
> On Wed, Apr 8, 2015 at 6:39 AM, Tomislav Hengl
> <hengl at spatial-analyst.net <mailto:hengl at spatial-analyst.net>>
>
> wrote:
>
> >
>
> > The "clhs" package (http://cran.r-project.org/web/packages/clhs/)
> has
>
> > some functionality for latin-hypercube sampling hence it is
> suitable
>
> > for your case. Here are some code examples: http://gsif.isric.org/doku.
>
> > php?id=wiki:tutorial_eberg#testing_the_feature_space_coverage
>
> >
>
> > I have also recently (experimentally) implemented a function to
> test
>
> > 'efficiency of any sampling plan' which looks at both geographical
>
> > space and feature space representation: http://gsif.r-forge.r-project.
>
> > org/spsample.prob.html
>
> > If you discover a bug or some 'strange behavior', please let me know.
>
> >
>
> > HTH,
>
> >
>
> > T. (Tom) Hengl
>
> > Researcher @ ISRIC - World Soil Information Team member Africa Soil
>
> > Information Services http://africasoils.net
>
> > Url: http://www.wageningenur.nl/en/Persons/dr.-T-Tom-Hengl.htm
>
> > Network: http://profiles.google.com/tom.hengl
>
> > Publications: http://scholar.google.com/citations?user=2oYU7S8AAAAJ
>
> >
>
> >
>
> >
>
> > On 8-4-2015 2:24, Jin.Li at ga.gov.au <mailto:Jin.Li at ga.gov.au> wrote:
>
> >
>
> >> Sorry for not ccing to the list!
>
> >>
>
> >> -----Original Message-----
>
> >> From: Li Jin
>
> >> Sent: Tuesday, 7 April 2015 9:36 AM
>
> >> To: 'Roger.Bivand at nhh.no'
>
> >> Subject: RE: [R-sig-Geo] Spatial sampling design using information
>
> >> more than spatial coordinates [SEC=UNCLASSIFIED]
>
> >>
>
> >> Thanks a lot, Roger! Yes, we have looked at spsurvey and it does
> not
>
> >> meet the needs of our client.
>
> >> Kind regards,
>
> >> Jin
>
> >>
>
> >> -----Original Message-----
>
> >> From: Roger Bivand [mailto:Roger.Bivand at nhh.no]
>
> >> Sent: Friday, 3 April 2015 2:06 AM
>
> >> To: Li Jin
>
> >> Cc: r-sig-geo at r-project.org <mailto:r-sig-geo at r-project.org>
>
> >> Subject: Re: [R-sig-Geo] Spatial sampling design using information
>
> >> more than spatial coordinates [SEC=UNCLASSIFIED]
>
> >>
>
> >> On Thu, 2 Apr 2015, Jin.Li at ga.gov.au <mailto:Jin.Li at ga.gov.au> wrote:
>
> >>
>
> >> Hi All,
>
> >>>
>
> >>> I need to consider both spatial information and another variable (e.g.
>
> >>> elevation, soil type) in a sampling design. It seems the
> available
>
> >>> libraries (e.g. sp and spcosa) in R for spatial sampling design
> are
>
> >>> unable to handle it. Please correct me if I am wrong. Before I
> start
>
> >>> to develop my own function for it, I would like to confirm if I
> have
>
> >>> missed some other functions that are available for it in R. Any
>
> >>> suggestions and advice? Thanks in advance!
>
> >>>
>
> >>
>
> >> Have you looked at spsurvey? It may not provide what you need, but
>
> >> checking it would probably help you see whether there is anything
>
> >> meeting your needs. Otherwise you might need to stratify first on
> your covariates.
>
> >>
>
> >> Roger
>
> >>
>
> >>
>
> >>> Regards,
>
> >>> Jin
>
> >>>
>
> >>> Jin Li, PhD
>
> >>> Spatial Modeller/Computational Statistician | National Earth
> and
>
> >>> Marine Observations Group Environmental Geoscience Division |
>
> >>> GEOSCIENCE AUSTRALIA ______________________________
>
> >>> ______________________________
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>
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> >> --
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> >> Roger Bivand
>
> >> Department of Economics, Norwegian School of Economics, Helleveien
>
> >> 30,
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