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Spatial standards - review by JN #29
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Thanks so much @Nowosad for that extremely useful feedback, responses to which are documented here. Note that these do not follow your numbers, and that 1-5 below all form part of your point 1.
Your concerns about SP3.3, that it may not always be possible to distinguish autocovariant processes from covariant processes not directly related to spatial structure is in this context okay, because not all standards are necessarily expected to be applicable to all software.
That standard comes under the general sub-heading of Input Data, and has been reformulated like this:
It now says, "should wherever possible enable neighbour contributions to be weighted by distance", with the second statement modified to say software should, "not rely exclusively on a uniform-weight rectangular cut-off." The qualifying statement has been slightly modified to read, "(or other continuous weighting variable)", and is intended to indicate that such weighting is to be interpreted in terms sufficiently general to apply where possible to categorical variables, which are often associated with continuous variables able to be used to weight associations between data. Application to dissimilarity measures is also general possible in ways intended by this standard.
has been addressed through revising that standard to now read,
Similarly, SP2.9 has been revised to read,
along with a disclaimer immediately after those standards that,
Your second concern about things like huge raster datasets not being able to be visualised has not been directly addressed. While that concern remains valid, the standards have hopefully been flagged as only ever conditionally applicable, so developers can readily judge that particular one to be non-applicable, yet having it there in that form may nevertheless motivate attempts to develop new routines anyway.
We are deeply indebted to your for taking the time to consider these standards in such depth, and for devising such constructive and useful suggestions. This alone is a great contribution to the project as a whole! Please feel free to offer any further suggestions, edits, comments, at any stage in the future. Thank you! |
@mpadge I am very happy that you find my comments useful. |
Hi @mpadge,
I read the spatial standards section and added some tiny changes to the text. I also have some comments:
I think changing from "should" to "should consider" could be an improvement.
For example:
"The latter standard, SP3.6, is commonly met by applying some form of spatial partitioning to data, and using spatially distinct partitions to define test and training data." - I think some references could be added to this standard (e.g., https://geocompr.robinlovelace.net/spatial-cv.html, https://doi.org/10.1109/IGARSS.2012.6352393, https://www.sciencedirect.com/science/article/abs/pii/S0304380019302145, https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13107)
What does "Testing" mean in 5.9?
5.9: "Return the result of that algorithmic application" - in my opinion, it is obvious that an analytic algorithm will return the result. Maybe this point is not necessary?
"Many developers of spatial software in R, including those responsible for the CRAN Task view on “Analysis of Spatial Data”," - what do you mean in the second part of this sentence? Roger is the only person "responsible" for this task view... How about "Many spatial software in R, including those featured in the CRAN Task view on “Analysis of Spatial Data”"?
I do not understand the following standard: "SP3.2 Spatial software which relies on sampling from input data (even if only of spatial coordinates) should enable sampling procedures to be based on local spatial densities of those input data."
I also think it would be good to inform/ask for feedback from other #rspatial developers, for example, by opening a new issue at https://github.com/r-spatial/discuss/issues.
Code/data examples are not part of the spatial standards, but maybe they should be? Quality examples/documentation/vignettes are vital for users. I also think that, in most cases, example data should be stored in spatial data formats rather than R objects (of some class).
I often see some new R packages that extend existing R packages (and thus could be great together), however they use different standards (different function naming, arguments' names and order, output structures, etc.). I think it could be useful if developers should list similar (related) packages during the review process and explain why they decided on using different standards (if applicable).
I hope that, at least, some of my comments make sense and could be useful in this project.
Best,
Jakub