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+Area Weight Generation for Polygon +Intersections
+ + + Source:vignettes/polygon_intersection.Rmd
+ polygon_intersection.RmdThis 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$w
+
+# make sure nothing is way out of whack
+max(weights$diff, na.rm = TRUE)
+#> [1] 0
+
+# ensure the weights generally sum as we would expect.
+sum(weights$gdptools_wght, na.rm = TRUE)
+#> [1] 24.99911
+sum(weights$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] 1
+sum(is.na(weights$gdptools_wght))
+#> [1] 14
+
+# look at cases where gptools has NA and ncdfgeom does not
+weights[is.na(weights$gdptools_wght),]
+#> # A tibble: 14 × 5
+#> huc12 huc8 w gdptools_wght diff
+#> <chr> <chr> <dbl> <dbl> <dbl>
+#> 1 020402040000 02040302 0 NA 0
+#> 2 020403010800 02040302 0 NA 0
+#> 3 020402050801 02040204 0 NA 0
+#> 4 020402050802 02040204 0 NA 0
+#> 5 020402050803 02040204 0 NA 0
+#> 6 020402060106 02040204 0 NA 0
+#> 7 020402060603 02040204 0 NA 0
+#> 8 020402060604 02040204 0 NA 0
+#> 9 020402060605 02040204 0 NA 0
+#> 10 020402060702 02040204 0 NA 0
+#> 11 020402060703 02040204 0 NA 0
+#> 12 020402070103 02040204 0 NA 0
+#> 13 020600010000 02060002 0 NA 0
+#> 14 020600030104 NA NA NA NA