rcrisp: Automate the Delineation of Urban River Spaces

[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
Version submitted:
Submission type: Stats
Badge grade: gold
Editor: @mpadge
Reviewers: @atsyplenkov, @khondula

Due date for @atsyplenkov: 2025-11-03

Due date for @khondula: 2025-11-21
Archive: TBD
Version accepted: TBD
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 using two-dimensional spatial information from street
    network data and digital elevation data in a projected CRS. 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:
    checkmate,
    dbscan,
    dplyr,
    lwgeom,
    osmdata,
    rcoins,
    rlang,
    rstac,
    sf (>= 0.9.0),
    sfheaders,
    sfnetworks,
    stringr,
    terra,
    tidygraph,
    units,
    visor
Suggests:
    knitr,
    purrr,
    rmarkdown,
    testthat (>= 3.0.0),
    withr
VignetteBuilder:
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.2

Scope

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

  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

Pre-submission Inquiry

• A pre-submission inquiry has been approved in issue#716

General Information

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

The target audience are researchers and practitioners concerned with the analysis of riverside urban phnomena. As a tool for automatic spatial delineation, rcrisp tackles the Modifiable Areal Unit Problem by providing an alternative to arbitrary spatial units used in current practice (e.g., circular, rectangular or polygonal "cut-outs", economically or politically driven development boundaries, country-specific census boundaries), namely spatial-morphological units derived from both the topography of the river valley and the configuration of the urban fabric. As a tool for automated river space delineation, rcrisp enables (1) local integrated analysis because it provices neutral boundaries that can be used to reliably integrate various data sources, and (2) global cross-case comparative analyses as the resulting delineations are neither too context-specific (e.g, a country-specific census tracts) nor too out-of-context (e.g., arbitrary cut-outs) than other delineation approaches.

• Paste your responses to our General Standard G1.1 here, describing whether your software is:

rcrisp is the first implementation of a novel algorithm.

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

N/A

Badging

• What grade of badge are you aiming for? (bronze, silver, gold)

gold

• If aiming for silver or gold, describe which of the four aspects listed in the Guide for Authors chapter the package fulfils (at least one aspect for silver; three for gold)

rcrisp fulfils at least three of the four aspects, as follows:

1. We are confident that we have identified most (if not all) standards that are currently applicable and we consider the current level of compliance to be well above minimal. We look forward to determining together with the reviewer(s) the potential level of compliance and will do our best to reach it.

2. rcrisp currently demonstrates extensive compliance with sub-categories G1, G2 and G5 and it meets most standards in sub-categories SP1, SP2, and SP4. It also complies with at least two standards in each of G4, SP3 and SP6.

3. As a package meant to define analytical spatial units, rcrisp presents broad applicability in any kind of analysis concerned with riverside phenomena that can benefit from morphological boundaries. This also means that the package is not bound to any specific discipline: it can be used in ecology, hydrology, urban studies, transport planning, to name a few. In terms of scale, rcrisp enables use cases of local analyses (e.g., delineation of a specific riverspace, segment or corridor of interest in a city) as well as cross-case analyses (e.g., delineation of any of those spatial units for a large number of river-crossed cities for purposes of comparison and large-scale understanding of a phenomenon of interest). Locally it enables integration of various data sources within a single neutral boundary, while globally it allows for scalability.

4. The current design and API of rcrisp is structured around the main user-facing delineate_* functions. In addition to modules dedicated to each of those delineation tasks, we designed specific modules for delineation data retrieval (currently limited to OSM and GLO-30 DEM data), example data retrieval, spatial network data pre-processing, and utility functions. This design is the result of our efforts to make the package both easily maintainable and understandable to users. While this design has been effective for our purposes, we look forward to any suggestions on how we can improve it further.

Technical checks

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or have another maintainer identified.
• I/we have read the Statistical Software Peer Review Guide for Authors.
• I/we have run autotest checks on the package, and ensured no tests fail.

As mentioned in the presubmission issue,

We [...] did our best to address the issues flagged by autotest, discarding those we considered to be false positives. However, test-autotest only passes when run interactively (see a detailed description of this issue in CityRiverSpaces/rcrisp#321 (comment)).

• The srr_stats_pre_submit() function confirms this package may be submitted.
• The pkgcheck() function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.

This package:

• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.

Publication options

• Do you intend for this package to go on CRAN?
• Do you intend for this package to go on Bioconductor?

Code of conduct

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

[ropensci-review-bot]

Thanks for submitting to rOpenSci, our editors and @ropensci-review-bot will reply soon. Type @ropensci-review-bot help for help.

[ropensci-review-bot]

🚀

Editor check started

👋

[ropensci-review-bot]

Checks for rcrisp (v0.1.4)

git hash: 65355f4c

• ✔️ Package is already on CRAN.
• ✔️ has a 'codemeta.json' file.
• ✔️ has a 'contributing' file.
• ✔️ uses 'roxygen2'.
• ✔️ 'DESCRIPTION' has a URL field.
• ✔️ 'DESCRIPTION' has a BugReports field.
• ✔️ Package has at least one HTML vignette
• ✔️ All functions have examples.
• ✔️ Package has continuous integration checks.
• ✔️ Package coverage is 88.1%.
• ✔️ All goodpractice linters passed.
• ✔️ This is a statistical package which complies with all applicable standards
• ✔️ R CMD check found no errors.
• ✔️ R CMD check found no warnings.
• 👀 Function names are duplicated in other packages
• 👀 Package has unusually large number of 16 Imports (> 95% of all packages)

(Checks marked with 👀 may be optionally addressed.)

Package License: Apache License (>= 2)

---

1. rOpenSci Statistical Standards (srr package)

This package is in the following category:

• Spatial

✔️ All applicable standards [v0.2.0] have been documented in this package (338 complied with; 43 N/A standards)

Click to see the report of author-reported standards compliance of the package with links to associated lines of code, which can be re-generated locally by running the srr_report() function from within a local clone of the repository.

---

2. Package Dependencies

Details of Package Dependency Usage (click to open)

The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.

type	package	ncalls
internal	rcrisp	154
internal	base	121
internal	graphics	19
internal	stats	12
internal	tools	2
internal	utils	1
imports	sf	80
imports	checkmate	29
imports	terra	11
imports	sfnetworks	7
imports	sfheaders	5
imports	lwgeom	4
imports	rlang	4
imports	rcoins	3
imports	tidygraph	3
imports	dbscan	2
imports	dplyr	2
imports	osmdata	1
imports	rstac	1
imports	stringr	1
imports	units	1
imports	visor	1
suggests	knitr	NA
suggests	purrr	NA
suggests	rmarkdown	NA
suggests	testthat	NA
suggests	withr	NA
linking_to	NA	NA

Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table.

rcrisp

as_bbox (6), filter_network (6), osmdata_as_sf (6), buffer (5), as_linestring (3), as_network (3), bbox_as_str (3), cache_directory (3), corridor_edge (3), find_intersections (3), get_example_data_file (3), get_rds_filename (3), get_river_aoi (3), nearest_node (3), as_crs (2), as_polygon (2), build_river_network (2), calc_distance (2), calc_rolling_sum (2), clip_and_filter (2), combine_river_features (2), corridor_end_points (2), fetch_boundary (2), filter_clusters (2), find_longest (2), get_cd_char (2), get_corridor_edges (2), get_crossing_edges (2), get_dem_cache_filepath (2), get_download_url (2), get_example_cache_filepath (2), get_intersecting_edges (2), get_intersection_points (2), get_osm_bb (2), get_osm_buildings (2), get_osm_city_boundary (2), get_osm_railways (2), get_osm_river (2), get_osm_streets (2), get_osmdata (2), get_osmdata_cache_filepath (2), get_river_banks (2), get_stac_asset_urls (2), get_utm_zone (2), initial_edges (2), insert_intersections (2), load_dem (2), mask_slope (2), read_data_from_cache (2), split_by (2), add_weights (1), as_sfc (1), cap_corridor (1), check_cache (1), check_invalid_geometry (1), clean_network (1), clear_cache (1), delineate (1), delineate_corridor (1), delineate_riverspace (1), delineate_segments (1), delineate_valley (1), find_largest (1), flatten_network (1), get_cost_distance (1), get_dem (1), get_dem_example_data (1), get_osm_example_data (1), get_slope (1), get_valley_polygon (1), get_valley_polygon_no_hole (1), get_valley_polygon_raw (1), is_point_in_edge (1), load_raster (1), mask_cost_distance (1), match_osm_name (1), river_buffer (1), shortest_path (1), smooth_dem (1)

base

list (14), c (9), append (6), which (6), file (5), lapply (4), length (4), vapply (4), for (3), inherits (3), lengths (3), list.files (3), names (3), paste (3), seq_len (3), sum (3), unlist (3), data.frame (2), file.info (2), if (2), match (2), max (2), nrow (2), rep (2), suppressWarnings (2), Sys.getenv (2), tryCatch (2), abs (1), apply (1), as.matrix (1), as.POSIXct (1), cumsum (1), diff (1), file.path (1), force (1), grepl (1), is.null (1), matrix (1), mode (1), ncol (1), numeric (1), options (1), readRDS (1), round (1), seq_along (1), sort (1), split (1), sqrt (1), units (1)

sf

st_intersects (12), st_as_sf (9), st_geometry (6), st_intersection (6), st_union (5), st_crs (4), st_sf (4), st_boundary (3), st_buffer (3), st_filter (3), st_length (3), st_sfc (3), st_bbox (2), st_collection_extract (2), st_coordinates (2), st_distance (2), st_read (2), st_area (1), st_as_sfc (1), st_cast (1), st_centroid (1), st_contains (1), st_crosses (1), st_is_longlat (1), st_layers (1), st_nearest_feature (1)

checkmate

assert_character (11), assert_logical (7), assert_numeric (4), assert_vector (3), assert_date (2), assert_multi_class (2)

graphics

points (9), lines (8), polygon (1), stem (1)

stats

density (4), weights (4), window (3), line (1)

terra

focal (3), terrain (3), as.polygons (1), costDist (1), mask (1), rast (1), vect (1)

sfnetworks

as_sfnetwork (2), st_network_cost (2), sfnetwork (1), st_network_paths (1), to_spatial_subdivision (1)

sfheaders

sfc_cast (2), sf_cast (1), sfc_linestring (1), sfc_to_df (1)

lwgeom

st_startpoint (2), st_endpoint (1), st_split (1)

rlang

sym (4)

rcoins

stroke (3)

tidygraph

convert (1), morph (1), to_components (1)

dbscan

dbscan (2)

dplyr

bind_rows (1), pull (1)

tools

R_user_dir (2)

osmdata

getbb (1)

rstac

stac_search (1)

stringr

str_extract (1)

units

set_units (1)

utils

head (1)

visor

get_viewpoints (1)

---

3. Statistical Properties

This package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing.

Details of statistical properties (click to open)

The package has:

• code in R (100% in 15 files) and
• 2 authors
• 7 vignettes
• 1 internal data file
• 16 imported packages
• 70 exported functions (median 12 lines of code)
• 104 non-exported functions in R (median 9 lines of code)

---

Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
The following terminology is used:

• loc = "Lines of Code"
• fn = "function"
• exp/not_exp = exported / not exported

All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the checks_to_markdown() function

The final measure (fn_call_network_size) is the total number of calls between functions (in R), or more abstract relationships between code objects in other languages. Values are flagged as "noteworthy" when they lie in the upper or lower 5th percentile.

measure	value	percentile	noteworthy
files_R	15	71.2
files_vignettes	9	99.0
files_tests	16	92.6
loc_R	1165	69.5
loc_vignettes	449	73.5
loc_tests	1619	89.7
num_vignettes	7	98.3	TRUE
data_size_total	136152	84.4
data_size_median	136152	92.7
n_fns_r	174	86.2
n_fns_r_exported	70	91.8
n_fns_r_not_exported	104	82.6
n_fns_per_file_r	7	82.5
num_params_per_fn	3	29.3
loc_per_fn_r	11	32.6
loc_per_fn_r_exp	12	28.1
loc_per_fn_r_not_exp	9	27.2
rel_whitespace_R	18	70.0
rel_whitespace_vignettes	24	64.5
rel_whitespace_tests	13	83.2
doclines_per_fn_exp	12	3.7	TRUE
doclines_per_fn_not_exp	0	0.0	TRUE
fn_call_network_size	153	84.2

---

3a. Network visualisation

Click to see the interactive network visualisation of calls between objects in package

---

4. goodpractice and other checks

Details of goodpractice checks (click to open)

3a. Continuous Integration Badges

GitHub Workflow Results

id	name	conclusion	sha	run_number	date
16673089208	Commands	skipped	e763db	356	2025-08-01
16673806919	lint	success	65355f	1028	2025-08-01
16673909341	pages build and deployment	success	3f3b42	158	2025-08-01
16673806918	pkgcheck	success	65355f	168	2025-08-01
16673806911	pkgdown	success	65355f	1048	2025-08-01
16673806912	R-CMD-check	success	65355f	1051	2025-08-01
16673806914	test-coverage	success	65355f	1051	2025-08-01

---

3b. goodpractice results

Test coverage with covr

Package coverage: 88.12

Cyclocomplexity with cyclocomp

The following function have cyclocomplexity >= 15:

function	cyclocomplexity
delineate	17

Static code analyses with lintr

lintr found no issues with this package!

---

5. Other Checks

Details of other checks (click to open)

✖️ The following 5 function names are duplicated in other packages:

• • as_network from egor, migraph, ruta
• • clear_cache from catalog, lintr, quincunx, rmonad
• • delineate from pks
• • load_dem from parseRPDR
• • reproject from stplanr, stplanr

---

Package Versions

package	version
pkgstats	0.2.0.67
pkgcheck	0.1.2.207
srr	0.1.4.8

---

Editor-in-Chief Instructions:

This package is in top shape and may be passed on to a handling editor

[mpadge]

Thanks @cforgaci , very happy to see this reach the full-submission stage! We'll start trying to find a handling editor as soon as we can, but please note that several of our editors will be away for the next couple of weeks, so this may take some time. You'll be notified here as soon as we've found somebody.

[ldecicco-USGS]

@cforgaci ...we haven't forgotten about this! I'm still looking for a handling editor. August is a particularly tough time to find people since a lot of folks plan vacations.

[cforgaci]

@cforgaci ...we haven't forgotten about this! I'm still looking for a handling editor. August is a particularly tough time to find people since a lot of folks plan vacations.

Thank you @ldecicco-USGS for the update.

[mpadge]

@ropensci-review-bot assign @mpadge as editor

[ropensci-review-bot]

Assigned! @mpadge is now the editor

[mpadge]

@cforgaci I've decided to handle this one myself. A general statement for the record: The input data for this package come from my own package, https://github.com/ropensci/osmdata. While that is potentially a conflict of interest, I'm genuinely interested in learning how that package is used by others. Also ping @jmaspons, who has recently become the primary maintainer of osmdata.

[mpadge]

Editor checks:

• Documentation: The package has sufficient documentation available online (README, pkgdown docs) to allow for an assessment of functionality and scope without installing the package. In particular,
  • Is the case for the package well made?
  • Is the reference index page clear (grouped by topic if necessary)?
  • Are vignettes readable, sufficiently detailed and not just perfunctory?
• Fit: The package meets criteria for fit and overlap.
• Installation instructions: Are installation instructions clear enough for human users?
• Tests: If the package has some interactivity / HTTP / plot production etc. are the tests using state-of-the-art tooling?
• Contributing information: Is the documentation for contribution clear enough e.g. tokens for tests, playgrounds?
• License: The package has a CRAN or OSI accepted license.
• Project management: Are the issue and PR trackers in a good shape, e.g. are there outstanding bugs, is it clear when feature requests are meant to be tackled?

---

Editor comments

• The package seems very well developed, and the documentation is extensive and generally excellent. One thing I would nevertheless have liked to see is a general overview of a typical workflow. The README provides an example of "how to solve a common problem," but gives no real indication of what that problem might be, or why it might be good to solve it. The vignettes clearly have all the pieces needed for the general workflow of the package to be used, yet there is no real demonstration of how everything can or should be used together. I suspect that the review process would be greatly aided by first providing an overview of how the various pieces of the package can and should be used within single workflows. If you decide to tackle this before we proceed to review, please do so via an issue in your repo, and link to this issue. Then we'll see your issue linked below, and will also be able to see when it has been closed.

• Your use of issues on GitHub is really good, and provides great insight into thoughts behind the package development. There are nevertheless a lot of currently open issues, many of which are pegged to a milestone on GitHub which has a due date of 31 Aug 2025. That suggests the package might change quite a bit in the next short while - can you provide an update on current status, stability, and likely changes before we proceed to review? More generally, it might be good to try to close as many outstanding issues as possible before proceeding to review.

• The first sentence of the README states that the tool "follow[s] the CRiSp method", yet without explaining what this is, or linking to anywhere else. It would be better to provide a brief initial explanation, and then to provide links there to both to the method vignette and the main paper. (See also first comment above.)

[cforgaci]

@mpadge, thank you for taking this on and for your valuable feedback. We'll start working on your comments right away and let you know once they've been addressed.