rcrisp - presubmission inquiry

[cforgaci]

Submitting Author Name: Claudiu Forgaci
Submitting Author Github Handle: @cforgaci
Other Package Authors Github handles: (comma separated, delete if none) @fnattino
Repository: https://github.com/CityRiverSpaces/rcrisp
Submission type: Pre-submission
Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Package: rcrisp
Title: Automate the Delineation of Urban River Spaces
Version: 0.1.4
Authors@R: c(
    person("Claudiu", "Forgaci", , "c.forgaci@tudelft.nl", role = c("aut", "cre", "cph"),
           comment = c(ORCID = "0000-0003-3218-5102")),
    person("Francesco", "Nattino", , "f.nattino@esciencecenter.nl", role = "aut",
           comment = c(ORCID = "0000-0003-3286-0139")),
    person("Fakhereh", "Alidoost", , "f.alidoost@esciencecenter.nl", role = "ctb",
           comment = c(ORCID = "0000-0001-8407-6472")),
    person("Meiert Willem", "Grootes", , "m.grootes@esciencecenter.nl", role = "ctb",
           comment = c(ORCID = "0000-0002-5733-4795")),
    person("Netherlands eScience Center", , , "info@esciencecenter.nl", role = "fnd")
  )
Description: Provides tools to automate the morphological delineation of
    riverside urban areas, based on a method introduced in Forgaci (2018)
    <doi:10.7480/abe.2018.31>. Delineation entails the identification of
    corridor boundaries, segmentation of the corridor, and delineation of
    the river space. The resulting delineation can be used to characterise
    spatial phenomena that can be related to the river as a central element.
License: Apache License (>= 2)
URL: https://cityriverspaces.github.io/rcrisp/, https://doi.org/10.5281/zenodo.15793526
BugReports: https://github.com/CityRiverSpaces/rcrisp/issues
Depends:
    R (>= 4.1.0)
Imports:
    dbscan,
    dplyr,
    lwgeom,
    osmdata,
    rcoins,
    rlang,
    rstac,
    sf,
    sfheaders,
    sfnetworks,
    stringr,
    terra,
    tidygraph,
    units,
    visor
Suggests:
    ggplot2,
    knitr,
    purrr,
    rmarkdown,
    testthat (>= 3.0.0),
    withr
VignetteBuilder:
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Data Lifecycle Packages

  • data retrieval
  • data extraction
  • data munging
  • data deposition
  • data validation and testing
  • workflow automation
  • version control
  • citation management and bibliometrics
  • scientific software wrappers
  • field and lab reproducibility tools
  • database software bindings
  • geospatial data
  • text analysis

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

rcrisp provides tools to automate the spatial morphological delineation of riverside urban areas using OpenStreetMap and Copernicus GLO-30 DEM data.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

• Who is the target audience and what are scientific applications of this package?

The target audience are researchers and practitioners interested in the spatial manifestation of social, environmental, ecological or economic phenomena related to urban rivers. The resulting delineation contributes to tackling the Modifiable Areal Unit Problem by providing a non-arbitrary, neutral (i.e., purely morphological) boundary that considers the morphologies of the river valley and the urban fabric jointly. As such, it also provides a spatial unit for large-scale comparative analyses of phenomena surrounding urban rivers.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

To our knowledge, there is no other R package that accomplishes the same thing.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Not applicable.

• Any other questions or issues we should be aware of?:

Thank you for considering our submission!

[mpadge]

Thanks @cforgaci, your package certainly looks in great shape, and I suspect will be in scope here. It's also very helpful that you've got our pkgcheck-action installed - makes initial overview much easier. Great to see all ✔ too 👍 .

One question before proceeding any further: Have you considered our new-ish Statistical Software Peer Review program? Our general guide there is that a packege is in scope if it is able to comply with > 50% of standards in any category (plus the general standards). In your case, it really seems to me that you'd easily comply with more than half of the spatial standards. If you agree, I propose that your package would be better considered a statistical package, which would then require you to use our srr (Software Review Rockets) system to document how and where your package complies with standards. Please let us know what you think.

As an aside, as author of the osmdata package which I see is used to obtain and transform your input data, I'd be really interested to see how easy it is to get osmdata objects to comply with the general standards for input data. I'd be very happy to help in any way along the way there!

[cforgaci]

Thank you for the prompt response @mpadge!

I did not select "Spatial Analysis" under "Statistical Packages" because I was not sure if our package is "statistical" enough. It certainly serves a broad range of spatial statistical use cases (which also motivated the development of the package in the first place) and we aim to further develop it in a direction that makes use of unsupervised ML for improved output, but the base functionality of the package does not depend on that at this stage.

I had a look at the general and category-specific standards of the Statistical Software Peer Review process and I think we should be able to comply with around 70%; the rest of 30% do not seem to be applicable. Please do consider rcrisp under that program — note that I ticked the "Spatial Analysis" box in the presubmission form above. Also, many thanks for offering to help with getting osmdata objects to comply with the standards!

@fnattino and I look forward to your guidance on the next steps.

[mpadge]

@cforgaci and @fnattino: That's great news! 70% compliance would be fantastic. Your main next step should now be:

1. Read through the Stats Dev Guide procedure and detailed docs on how to comply with standards in the srr package.
2. (Optional) Use autotest to help catch "edge cases" in your code and documentation.
3. Install ropensci-review-tools/srr locally and confirm compliance by running srr::srr_stats_pre_submit() for summary info, or srr::srr_report() for full report. Once pre_submit() gives the all clear:
4. In this issue here, call @ropensci-review-bot check srr to get the bot to give results of srr_stats_pre_submit().

At that point, we should be good to proceed to full submission. Feel free to ask any questions here in the meantime. And most importantly: The statistical standards are intended to be a community endeavour, so please give any feedback you may have via issues in the accompanying repo. For example, you mention that around 30% may not be applicable. If you think any may be better excluded from current standards, or could be tweaked in any way that might make them more applicable to your cases, please let us know and we can discuss there.

Thanks both, and looking forward to progress here 🚀

[fnattino]

Thanks a lot for the clear overview @mpadge . We took note of the steps, we'll take care of these before moving on to submission!