Area Weight Generation for Polygon Intersections

This article demonstrates how to create area weights for two sets of polygons.

It is a comparison with the gdptools python package demonstration here.

See calculate_area_intersection_weights() for additional demonstration and info.

gdptools_weights <- read.csv(system.file("extdata/gdptools_prl_out.csv", package = "ncdfgeom"), 
                             colClasses = c("character", "character", "numeric"))

gdptools_weights <- dplyr::rename(gdptools_weights, gdptools_wght = wght)

gage_id <- "USGS-01482100"
basin <- nhdplusTools::get_nldi_basin(list(featureSource = "nwissite", featureId = gage_id))
huc08 <- nhdplusTools::get_huc(id = na.omit(unique(gdptools_weights$huc8)), type = "huc08")
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on
huc12 <- nhdplusTools::get_huc(id = na.omit(unique(gdptools_weights$huc12)), type = "huc12")
#> Spherical geometry (s2) switched off
#> Spherical geometry (s2) switched on

org_par <- par(mar = c(0, 0, 0, 0))
plot(sf::st_as_sfc(sf::st_bbox(huc12)))
plot(sf::st_geometry(basin), lwd = 4, add = TRUE)
plot(sf::st_simplify(sf::st_geometry(huc08), dTolerance = 500), add = TRUE, lwd = 2)
plot(sf::st_simplify(sf::st_geometry(huc12), dTolerance = 500), add = TRUE, lwd = 0.2, border = "grey")

par(org_par)

weights <- ncdfgeom::calculate_area_intersection_weights(
  x = sf::st_transform(dplyr::select(huc12, huc12), 6931),
  y = sf::st_transform(dplyr::select(huc08, huc8), 6931),
  normalize = TRUE
)
#> Loading required namespace: areal

weights <- dplyr::left_join(weights, gdptools_weights, by = c("huc8", "huc12"))

With weights calculated, we can do a little investigation into the differences.

weights$diff <- weights$w - weights$gdptools_wght

# make sure nothing is way out of whack
max(weights$diff, na.rm = TRUE)
#> [1] 0.0000009205585

# ensure the weights generally sum as we would expect.
sum(weights$gdptools_wght, na.rm = TRUE)
#> [1] 25
sum(weights$w, na.rm = TRUE)
#> [1] 25
length(unique(na.omit(weights$huc8)))
#> [1] 25

# see how many NA values we have in each.
sum(is.na(weights$w))
#> [1] 183
sum(is.na(weights$gdptools_wght))
#> [1] 183

# look at cases where gptools has NA and ncdfgeom does not
weights[is.na(weights$gdptools_wght),]
#> # A tibble: 183 × 5
#>    huc12        huc8      w gdptools_wght  diff
#>    <chr>        <chr> <dbl>         <dbl> <dbl>
#>  1 011000050101 NA       NA            NA    NA
#>  2 011000050201 NA       NA            NA    NA
#>  3 011000050202 NA       NA            NA    NA
#>  4 011000050203 NA       NA            NA    NA
#>  5 011000050305 NA       NA            NA    NA
#>  6 011000050501 NA       NA            NA    NA
#>  7 011000050504 NA       NA            NA    NA
#>  8 020200030604 NA       NA            NA    NA
#>  9 020200030801 NA       NA            NA    NA
#> 10 020200030802 NA       NA            NA    NA
#> # ℹ 173 more rows