Use case for numeric/continuous sf polygons

[jaseeverett]

Add the ability to interpolate numeric/continuous sf polygons onto planning units. At the moment data_to_planning_grid only deals with binary sf features.

We are exploring some code in this space and may've something to add here shortly.

[jflowernet]

I'm working on adding functionality for categorical sf polygons, e.g. habitat types, but I don't know of the use case for a continuous sf polygon. I can't remember seeing continuous data in sf format since it would create huge files unless there were only a few values/ shapes. Is there a use case you were thinking of Jase?

[jflowernet]

With addition of the option to return gridded sf and raster features based on a column in an sf object: get_data_in_grid(feature_names = ), and to return the grid cell coverage fraction using the argument applycutoff = FALSE, I think this issue is now covered.

[jaseeverett]

Sorry. I was wrong. This isn't totally dealt with. The function still can't deal with continuous data from what I can tell.

The use case for this is regridding continuous data such as cost, fishing, or climate velocity data to planning units.

In this case you would use sf::st_interpolate_aw() and definitely not want a cutoff to be applied.

[jflowernet]

I would expect fishing/ climate data, and anything continuous to be in raster format. If this is the case, spatialgridr works fine. If you are going from raster data to sf grid, it uses exactextractr::exact_extract under the hood to do the raster to sf extraction.

Reprex below shows gridding GFW data to sf grid

library(spatialgridr)

samoa_eez <- system.file("extdata", "samoa_eez.rds", package = "spatialgridr") |> readRDS()

samoa_projection <- '+proj=laea +lon_0=-172.5 +lat_0=0 +datum=WGS84 +units=m +no_defs'

planning_grid <- get_grid(area_polygon = samoa_eez, projection_crs = samoa_projection, resolution = 5000, option = "sf_hex")

gfw_data <- gfwr::get_raster(spatial_resolution = "low",
                             temporal_resolution = "yearly",
                             group_by = "gearType",
                             date_range = "2021-01-01,2021-12-31",
                             region = gfwr::sf_to_geojson(samoa_eez),
                             region_source = "user_json")
#> Rows: 1019 Columns: 6
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): geartype
#> dbl (5): Lat, Lon, Time Range, Vessel IDs, Apparent Fishing Hours
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

gfw_data_ras <- gfw_data |>
  dplyr::group_by(Lon, Lat) |>
  dplyr::summarise(fishing_hours = sum(`Apparent Fishing Hours`, na.rm = TRUE)) |>
  dplyr::ungroup() |>
  terra::rast(type = "xyz", crs = "epsg:4326")
#> `summarise()` has grouped output by 'Lon'. You can override using the `.groups`
#> argument.

terra::plot(gfw_data_ras)
terra::lines(samoa_eez)

gfw_data_gridded <- get_data_in_grid(spatial_grid = planning_grid, dat = gfw_data_ras)
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plot(gfw_data_gridded, border = F)

^{Created on 2024-05-09 with reprex v2.1.0}

[jaseeverett]

Sure that is often the case, but not always. I have plenty of continuous data in vector format. From my perspective, I would like to see spatialgridr supporting the same options for both terra and sf.

If this is something you don't intend to implement, let me know and I will revisit it with spatialplanr.

[jflowernet]

Ok, it shouldn't be too hard to implement I think. Let's talk about this next week at the meeting if we have time. Would be good to make sure spatialgridr covers all use cases and we keep the number of arguments to get_data_in_grid() at a reasonable number!