[RFC] Deciding on methods for optimizing spatial sampling methods to implement

[gottacatchenall]

This package implements methods for optimizing the location of spatial sampling for biodiversity monitoring as part of the block 3 for the GEOBON manuscript project.

The following methods are primarily what I've seen in the literature for optimizing spatial sampling.

Opening this up as a place for discussion to decide on a final list of methods before implementing.

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1. Fractal Triads Simpson et al 2021
Proposed to measure phenomena across scales. Of those listed here, this is the simplest, as it always recommends a set of nested triangles, and does not use any information about the landscape/species (e.g. an SDM) to design points.

2. Generalised random-tessellation stratification (GRTS) Stevens and Olsen (2004)
Argues spatially balanced sampling is best, where spatially balanced is defined as meaning both points are not clumped together, but also that all spatially contiguous subsamples of this total sample are also spatially balanced. Proposes a method to generate balanced samples better than random sampling.

3. Pivotal Method (Grafstrom 2011)
Proposed improvement to GRTS

4. Balanced acceptance sampling (van Dam-Bates et al 2018)
Proposed improvement to GRTS and Pivotal method

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Methods below this line are different in that they use some spatial information to design samples (e.g. environmental predictors for an SDM, or an SDM with uncertainties)

5. Spatial Simulated Annealing van Groenigen 1998, and computational improvement by (Chen et al 2013). Optimizes spatial locations such that they minimize covariance among a set of spatial covariates (e.g. environmental raster data).

6 Adaptive sampling (Andrade-Pacheco et al 2020)
Prioritizes spatial location based on current uncertainty of measurement there

[tpoisot]

Which one is the easiest, as a starting point to build a proof of concept around?

[gottacatchenall]

I think the shortest to implement will be fractal-triad, and then choose the optimal rotation angle by choosing the angle that maximizes the uncertainty of the points on the SDM. Pivotal method is easier and simpler to weight inclusion probability by uncertainty. Nevermind, Balanced Acceptance was the easiest to implement due to HaltonSequences.jl

Another obstacle to the minimum-viable-product for block 3 is simulating occurrence as a function of changing environmental rasters to test the ability to detect change, I might make an issue as neutral landscape change could be added on to NeutralLandscapes.jl

[gottacatchenall]

implemented BAS in #3

[tpoisot]

working on adaptive spatial sampling on #5