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GRTS sampling - 2-level design

7 messages · John Wilson, Roger Bivand, Thierry Onkelinx

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Hi everyone,

I'm working on a sampling design using GRTS, but I'm running into a
logistics problem. The field crew can set 5 nets per day, but only within a
5 km stretch, due to travel time constraints. With 10 sampling days, that's
a total of 50 sites. The overall sampling area is huge, so running a
regular GRTS design for 50 sites results, of course, in much larger
distances between sampling points.

Is there a legitimate way to create a 2-level GRTS design, where in step 1
we choose 10 spatially-balanced sampling points (one "core" point per
sampling day), and then for each of these "core points", we create a grid
of 5 sampling points that are constrained to all be within 5 km from each
other? I can make that happen code-wise, but am not sure what the
implications on spatial balance are, or if there's a built-in way to do
this.

Would appreciate any thoughts...
John
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On Thu, 7 Oct 2021, John Wilson wrote:

            
Do you have a code example? Are you using BalancedSampling, SDraw or 
Spbsampling or packages (probably SDraw)? Have you run any simulations to 
try to get a first assessment on the impact of constraining your sample? 
Might approach a package author also help?

Roger

  
    
#
Oh, sorry - I normally use the grts() function from the spsurvey package.
My hacky approach was to make 10 balanced points with grts(), followed by
imposing a 5 km buffer around each one, and either systematic sampling
within the buffer circle, or running a separate GRTS for the 5 points
within each 5 km buffer circle. Even writing this makes me cringe though,
so hoping for something legitimate... I'll contact the authors if I don't
get any solid leads on here.
On Thu, Oct 7, 2021 at 10:40 AM Roger Bivand <Roger.Bivand at nhh.no> wrote:

            

  
  
#
On Thu, 7 Oct 2021, John Wilson wrote:

            
That makes sense. Perhaps comparison with the implementation in SDraw 
might be of interest. Please report back on progress (or no progress), 
someone else may well face the same problem. By the way, Task Views are 
being "re-booted", and it seems clear that a section on spatial sampling 
in one or several task views (Spatial, Environmetrics) would be helpful if 
someone could author one. For Spatial, a PR to 
https://github.com/r-spatial/task_views (we will be converting to markdown 
rather than ctv XML markup soon).

Roger

  
    
#
Dear John,

Your procedure will create a spatially balanced level 1 sample (10
"regions") and within those regions a spatially balanced level 2 sample.
When you ignore the structure, there is no longer a spatial balance. So
you'll need to incorporate the two level sampling structure in your
analysis. E.g. by using region as random effect.

I presume you are catching fish along rivers and assume that the rivers are
linear features. I'd consider drawing 10 samples using GRTS to define the
regions. Then use that location as the center point of 5 systematic samples
along the river (-2, -1, 0, +1 and +2 km).

You might want to take a look at our grtsdb package. Available at
https://inbo.r-universe.dev/ It generates a full grid of master samples and
stores it in the database. So you can draw multiple samples from the same
master sample. This is useful in case of monitoring with a changing
population. You draw a sample and keep the lowest ranking locations that
are part of the population. If the population changes over time, then the
new sample will keep a proportion of the original sampling location
relative to the proportion of the population that remained stable. This
allows for repeated measures for stable locations while taking into account
the changes in population.

Best regards,

Thierry

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op do 7 okt. 2021 om 15:54 schreef John Wilson <jhwilson.nb at gmail.com>:

  
  
#
Dear Thierry,

Thank you so much for your reply. Yes, the loss of the spatial balance once
the two-tiered approach is not accounted for was what was worrying me.

The incorporation of region as a random effect has two issues - 1) the
overall sampling area is a lake, and "regions" don't make sense in that
context. 2) The analysis is a mark-recapture for fish (using the MARK
software); I've never seen the incorporation of random effects in the
Jolly-Seber / Cormark-Jolly-Seber models outside of Bayesian framework and
outside of individual random effects... but even if I could do that - the
regions just don't really make sense anyway (that I can see, anyway - maybe
I'm not thinking about it the right way?)

Thank you for the grtsdb suggestion. Do you have any examples of how this
works? I couldn't find any vignettes or worked examples...

Thank you so much,
John

On Fri, Oct 8, 2021 at 10:39 AM Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:

  
  
2 days later
#
Dear John,

A real life example is available at https://doi.org/10.5281/zenodo.2784012.
The idea is that the database returns a randomised set of points. You need
to overlay these points with your sampling framework. The final sample is
the set of points with the lowest ranking. The grtsdb package is a
reimplementation of my GRTS package (https://github.com/ThierryO/grts).
That package has a vignette describing GRTS via the Reversed Randomized
Quadrant-Recursive Raster strategy (
https://doi.org/10.1007/s00267-005-0199-x).

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


Op vr 8 okt. 2021 om 16:08 schreef John Wilson <jhwilson.nb at gmail.com>: