From 8c443fb50814bf518e51cbcb6e066d0592c976fd Mon Sep 17 00:00:00 2001 From: David Blodgett Date: Tue, 19 Nov 2024 13:12:03 -0600 Subject: [PATCH] version update and pkgdown --- DESCRIPTION | 2 +- docs/404.html | 6 +- docs/DISCLAIMER.html | 6 +- docs/articles/geometry.html | 13 +- docs/articles/index.html | 6 +- docs/articles/ncdfgeom.html | 9 +- .../ncdfgeom_files/figure-html/plot-1.png | Bin 314943 -> 313536 bytes docs/articles/polygon_intersection.html | 233 ++++++++++--- docs/articles/timeseries.html | 15 +- docs/authors.html | 8 +- docs/index.html | 8 +- docs/news/index.html | 6 +- docs/pkgdown.yml | 6 +- .../calculate_area_intersection_weights-1.png | Bin 7240 -> 9742 bytes .../calculate_area_intersection_weights.html | 314 +++++------------- docs/reference/create_cell_geometry.html | 17 +- docs/reference/index.html | 8 +- docs/reference/read_attribute_data.html | 9 +- docs/reference/read_geometry.html | 11 +- docs/reference/read_timeseries_dsg.html | 9 +- docs/reference/write_attribute_data.html | 11 +- docs/reference/write_geometry.html | 11 +- docs/reference/write_timeseries_dsg.html | 9 +- 23 files changed, 372 insertions(+), 345 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 11181aa..195865f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: ncdfgeom Type: Package Title: 'NetCDF' Geometry and Time Series -Version: 1.2.0 +Version: 1.3.0 Authors@R: c(person("David", "Blodgett", role = c("aut", "cre"), email = "dblodgett@usgs.gov"), person("Luke", "Winslow", role = "ctb")) diff --git a/docs/404.html b/docs/404.html index 16a73ca..21aaa2c 100644 --- a/docs/404.html +++ b/docs/404.html @@ -6,7 +6,7 @@ Page not found (404) • ncdfgeom - + @@ -32,7 +32,7 @@ ncdfgeom - 1.2.0 + 1.3.0 @@ -109,7 +109,7 @@

Page not found (404)

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/docs/DISCLAIMER.html b/docs/DISCLAIMER.html index 424a601..db4cf1d 100644 --- a/docs/DISCLAIMER.html +++ b/docs/DISCLAIMER.html @@ -1,5 +1,5 @@ -Disclaimer • ncdfgeomDisclaimer • ncdfgeom @@ -17,7 +17,7 @@ ncdfgeom - 1.2.0 + 1.3.0 @@ -87,7 +87,7 @@

Disclaimer

-

Site built with pkgdown 2.0.7.

+

Site built with pkgdown 2.0.9.

diff --git a/docs/articles/geometry.html b/docs/articles/geometry.html index 05a7031..414736f 100644 --- a/docs/articles/geometry.html +++ b/docs/articles/geometry.html @@ -6,7 +6,7 @@ Reading and Writing NetCDF-CF Geometry • ncdfgeom - + @@ -33,7 +33,7 @@ ncdfgeom - 1.2.0 + 1.3.0 @@ -55,6 +55,9 @@
  • Reading and Writing NetCDF-CF Geometry
    • +
  • + Area Weight Generation for Polygon Intersections +
  • Reading and Writing Timeseries
    • @@ -131,7 +134,7 @@

    Load Spatial Data
    -(vars <- ncmeta::nc_vars(example_file))
+(vars <- ncmeta::nc_vars(example_file))
 #> # A tibble: 5 × 5
 #>      id name         type      ndims natts
 #>   <int> <chr>        <chr>     <int> <int>
@@ -146,7 +149,7 @@ 
    Load Spatial Data= "station", 
                          variables = vars$name) -> example_file

    Now the NetCDF file looks like:

    -
    #> netcdf file69986203fbb {
+
    #> netcdf file63806c813359 {
 #> dimensions:
 #>  maxStrlen64 = 64 ;
 #>  station = 2 ;
@@ -681,7 +684,7 @@ 
    Multiple MultiPolygons wi
 
 
    
   
    
-
    Site built with pkgdown 2.0.7.
    
+
    Site built with pkgdown 2.0.9.
    
 
    
 
       
diff --git a/docs/articles/index.html b/docs/articles/index.html
index a3104bb..05de0fe 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -1,5 +1,5 @@
 
-Articles • ncdfgeomArticles • ncdfgeom
@@ -17,7 +17,7 @@
       
       
         ncdfgeom
-        1.2.0
+        1.3.0
       
     
 
@@ -87,7 +87,7 @@ 
    All vignettes
    
 
 
 
    
-  
    Site built with pkgdown 2.0.7.
    
+  
    Site built with pkgdown 2.0.9.
    
 
    
 
       
diff --git a/docs/articles/ncdfgeom.html b/docs/articles/ncdfgeom.html
index 34de8e3..e5586d1 100644
--- a/docs/articles/ncdfgeom.html
+++ b/docs/articles/ncdfgeom.html
@@ -6,7 +6,7 @@
 
 
 NetCDF-CF Geometry and Timeseries Tools for R • ncdfgeom
-
+
 
 
 
@@ -33,7 +33,7 @@
       
       
         ncdfgeom
-        1.2.0
+        1.3.0
       
     
 
@@ -55,6 +55,9 @@
 
  • 
       Reading and Writing NetCDF-CF Geometry
     
    • 
+    
  • 
+      Area Weight Generation for Polygon Intersections
+    
    • 
     
  • 
       Reading and Writing Timeseries
     
    • 
@@ -538,7 +541,7 @@ 
    Read Timeseries and Geometry f
 
 
    
   
    
-
    Site built with pkgdown 2.0.7.
    
+
    Site built with pkgdown 2.0.9.
    
 
    
 
       
diff --git a/docs/articles/ncdfgeom_files/figure-html/plot-1.png b/docs/articles/ncdfgeom_files/figure-html/plot-1.png
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z_fAskR;R6AxuKO7#
    
 
    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
    
+weights[is.na(weights$gdptools_wght) & !is.na(weights$w),]
    
+
    The following example illustrates the nuances between normalized and
+non-normalized area weights and shows more specifically how area weight
+intersection calculations can be accomplished.
    
+
    The set of polygons are a contrived but useful for the sake of
+demonstration.
    
+
    +
+library(dplyr)
+library(sf)
+library(ncdfgeom)
+
+g <- list(rbind(c(-1,-1), c(1,-1), c(1,1), c(-1,1), c(-1,-1)))
+
+blue1 = sf::st_polygon(g) * 0.8
+blue2 = blue1 + c(1, 2)
+blue3 = blue1 * 1.2 + c(-1, 2)
+
+pink1 = sf::st_polygon(g)
+pink2 = pink1 + 2
+pink3 = pink1 + c(-0.2, 2)
+pink4 = pink1 + c(2.2, 0)
+
+blue = sf::st_sfc(blue1,blue2,blue3)
+
+pink = sf::st_sfc(pink1, pink2, pink3, pink4)
+
+plot(c(blue,pink), border = NA)
+plot(blue, border = "#648fff", add = TRUE)
+plot(pink, border = "#dc267f", add = TRUE)
+
+blue <- sf::st_sf(blue, data.frame(idblue = c(1, 2, 3)))
+pink <- sf::st_sf(pink, data.frame(idpink = c(7, 8, 9, 10)))
+
+text(sapply(sf::st_geometry(blue), \(x) mean(x[[1]][,1]) + 0.4),
+     sapply(sf::st_geometry(blue), \(x) mean(x[[1]][,2]) + 0.3),
+     blue$idblue, col = "#648fff")
+
+text(sapply(sf::st_geometry(pink), \(x) mean(x[[1]][,1]) + 0.4),
+     sapply(sf::st_geometry(pink), \(x) mean(x[[1]][,2])),
+     pink$idpink, col = "#dc267f")
+
+sf::st_agr(blue) <- sf::st_agr(pink) <- "constant"
+sf::st_crs(pink) <- sf::st_crs(blue) <- sf::st_crs(5070)
    
+
    +
+(blue_pink_norm_false <- 
+calculate_area_intersection_weights(blue, pink, normalize = FALSE))
    
+
    NOTE: normalize = FALSE so weights sum to 1 per source polygon only
+when a source polygon is fully covered by the target. The
+non-intersecting portion is not included.
    
+
    The following breaks down how to use these weights for one source
+polygon.
    
+
    +blue$val = c(30, 10, 20)
+blue$area <- as.numeric(sf::st_area(blue))
+
+(result <- st_drop_geometry(blue) |>
+  left_join(blue_pink_norm_false, by = "idblue"))
    
+
    To calculate the value for pink-9, we would do:
    
+
    +
+((10 * 0.375 * 2.56) + (20 * 0.604167 * 3.6864)) / ((0.375 * 2.56) + (0.604167 * 3.6864))
    
+
    This is saying that 0.375 of blue-3 covers pink-9 and 0.6 of blue-2
+covers pink-9. Since we are using area as the weighting method, we
+multiply the fraction of each source polygon by its area and the value
+we want to create an area weight for. We sum the contributions from
+blue-2 and blue-3 to pink-9 and divide by the sum of the combined area
+weights.
    
+
    Note that because there is no contribution to 9 over some parts of
+the polygon, that missing area does not appear. The intersecting areas
+are 0.96 and 2.23 meaning that we are missing
    
+
    4 - 0.96 - 2.23 = 0.81
    
+
    and could rewrite the value for pink-9 as:
    
+
    +((10 * 0.375 * 2.56) + (20 * 0.604167 * 3.6864)) + (NA * 1 * 0.81) / 
+  ((1 * 0.81) + (0.375 * 2.56) + (0.604167 * 3.6864))
    
+
    Which evaluates to NA. This is why for this operation we usually drop
+NA terms!
    
+
    In practice, the above can be accomplished with:
    
+
    +(result <- result |>
+  group_by(idpink) |> # group so we get one row per target
+  # now we calculate the value for each `pink` with fraction of the area of each
+  # polygon in `blue` per polygon in `pink` with an equation like this:
+  summarize(
+    new_val = sum( (val * w * area) ) / sum(w * area)))
    
+
    Now let’s do the same thing but with
+normalize = FALSE.
    
+
    +
+(blue_pink_norm_true <-
+calculate_area_intersection_weights(select(blue, idblue), pink, normalize = TRUE))
    
+
    NOTE: normalize = TRUE so weights sum to 1 per target polygon.
+Non-overlap is ignored as if it does not exist.
    
+
    The following breaks down how to use these weights for one source
+polygon.
    
+
    +(result <- st_drop_geometry(blue) |>
+    left_join(blue_pink_norm_true, by = "idblue"))
    
+
    To calculate the value for pink-9, we would do:
    
+
    +((10 * 0.24) + (20 * 0.5568)) / (0.24 + (0.5568))
    
+
    This is saying that the portion of pink-9 that should get the value
+from blue-2 is 0.3 and the portion of pink-9 that should get the value
+from blue-3 is 0.7. In this form, our weights are transformed to
+includethe relative area of the source polygons.
    
+
    As shown above as well, the calculation can be accomplished with:
    
+
    +(result <- result |>
+    group_by(idpink) |> # group so we get one row per target
+    # now we calculate the value for each `pink` with fraction of the area of each
+    # polygon in `blue` per polygon in `pink` with an equation like this:
+    summarize(
+      new_val = sum( (val * w) ) / sum(w)))
    
+
    We can look at a more typical arrangement of polygons and look at
+this a different way.
    
+
    Let’s also look at the values.
    
+
    +# say we have data from `blue` that we want sampled to `pink`.
+# this gives the percent of each `blue` that intersects each `pink`
+
+(blue_pink <- calculate_area_intersection_weights(
+  select(blue, idblue), select(pink, idpink), normalize = FALSE))
+
+# NOTE: `w` sums to 1 per `blue` in all cases
+
+summarize(group_by(blue_pink, idblue), w = sum(w))
+
+# Since normalize is false, we apply weights like:
+st_drop_geometry(blue) |>
+  left_join(blue_pink, by = "idblue") |>
+  mutate(blue_areasqkm = 1.5 ^ 2) |> # add area of each polygon in `blue`
+  group_by(idpink) |> # group so we get one row per `pink`
+  # now we calculate the value for each b with fraction of the area of each
+  # polygon in `blue` per polygon in `pink` with an equation like this:
+  summarize(
+    new_val = sum( (val * w * blue_areasqkm) ) / sum(w * blue_areasqkm))
+
+# NOTE: `w` is the fraction of the polygon in `blue`. We need to multiply `w` by the
+# unique area of the polygon it is associated with to get the weighted mean weight.
+
+# we can go in reverse if we had data from `pink` that we want sampled to `blue`
+
+(pink_blue <- calculate_area_intersection_weights(
+  select(pink, idpink), select(blue, idblue), normalize = FALSE))
+
+# NOTE: `w` sums to 1 per `pink` (source) only where `pink` is fully covered by `blue` (target).
+
+summarize(group_by(pink_blue, idpink), w = sum(w))
+
+# Now let's look at what happens if we set normalize = TRUE. Here we
+# get `blue` as source and `pink` as target but normalize the weights so
+# the area of `blue` is built into `w`.
+
+(blue_pink <- calculate_area_intersection_weights(
+  select(blue, idblue), select(pink, idpink), normalize = TRUE))
+
+# NOTE: if we summarize by `pink` (target) `w` sums to 1 only where there is full overlap.
+
+summarize(group_by(blue_pink, idpink), w = sum(w))
+
+# Since normalize is false, we apply weights like:
+st_drop_geometry(blue) |>
+  left_join(blue_pink, by = "idblue") |>
+  group_by(idpink) |> # group so we get one row per `pink`
+  # now we weight by the percent of each polygon in `pink` per polygon in `blue`
+  summarize(new_val = sum( (val * w) ) / sum( w ))
+
+# NOTE: `w` is the fraction of the polygon from `blue` overlapping the polygon from `pink`.
+# The area of `blue` is built into the weight so we just sum the weith times value oer polygon.
    
   
 
   
 
@@ -55,6 +55,9 @@
 
  • 
       Reading and Writing NetCDF-CF Geometry
     
    • 
+    
  • 
+      Area Weight Generation for Polygon Intersections
+    
    • 
     
  • 
       Reading and Writing Timeseries
     
    • 
@@ -163,7 +166,7 @@ 
    Reading and Writing Timeseries
    
 timeseries ID (which is stored as a string).
 
 
    -ncmeta::nc_dims(nc_file)
+ncmeta::nc_dims(nc_file)
 #> # A tibble: 3 × 4
 #>      id name               length unlim
 #>   <int> <chr>               <dbl> <lgl>
@@ -173,7 +176,7 @@ 
    Reading and Writing Timeseries
    
 
    The file has variables for latitude, longitude, altitude, timeseries
 IDs, and a data variable.
    
 
    -ncmeta::nc_vars(nc_file)
+ncmeta::nc_vars(nc_file)
 #> # A tibble: 6 × 5
 #>      id name                                        type      ndims natts
 #>   <int> <chr>                                       <chr>     <int> <int>
@@ -186,7 +189,7 @@ 
    Reading and Writing Timeseries
    
 
    The primary dimensions in the file are of length, number of time
 steps and number of time series.
    
 
    -ncmeta::nc_dims(nc_file)
+ncmeta::nc_dims(nc_file)
 #> # A tibble: 3 × 4
 #>      id name               length unlim
 #>   <int> <chr>               <dbl> <lgl>
@@ -289,7 +292,7 @@ 
    Reading and Writing Timeseries
    
 
 
    
   
    
-
    Site built with pkgdown 2.0.7.
    
+
    Site built with pkgdown 2.0.9.
    
 
    
 
       
diff --git a/docs/authors.html b/docs/authors.html
index eda525b..97b12c3 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -1,5 +1,5 @@
 
-Authors and Citation • ncdfgeomAuthors and Citation • ncdfgeom
@@ -17,7 +17,7 @@
       
       
         ncdfgeom
-        1.2.0
+        1.3.0
       
     
    
 
@@ -63,7 +63,7 @@
   
    
     
    
       
    
-        
    Authors
    
+        
    Authors and Citation
    
       
    
 
       
@@ -107,7 +107,7 @@ 
    Citation
    
 
    
 
 
    
-  
    Site built with pkgdown 2.0.7.
    
+  
    Site built with pkgdown 2.0.9.
    
 
    
 
       
    
diff --git a/docs/index.html b/docs/index.html
index 88a2c6d..b03fa50 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -6,7 +6,7 @@
 
 
 NetCDF Geometry and Time Series • ncdfgeom
-
+
 
 
 
@@ -33,7 +33,7 @@
       
       
         ncdfgeom
-        1.2.0
+        1.3.0
       
     
    
 
@@ -104,7 +104,7 @@ 
    Installationinstall.packages("ncdfgeom")
    
 
    For the latest development version:
    
 
    install.packages("remotes")
-remotes::install_github("DOI-USGS/ncdfgeom")
    
+remotes::install_github("DOI-USGS/ncdfgeom")

    Contributing @@ -174,7 +174,7 @@

    Developers

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.0.9.

    diff --git a/docs/news/index.html b/docs/news/index.html index 3d55034..7e90730 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -1,5 +1,5 @@ -Changelog • ncdfgeomChangelog • ncdfgeom @@ -17,7 +17,7 @@ ncdfgeom - 1.2.0 + 1.3.0
    @@ -90,7 +90,7 @@

    ncdfgeom 1.1.0
    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.0.9.

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Weights -will sum to 1 per SOURCE polygon if the target polygons fully cover that feature. -`normalize = TRUE`, weights are divided by the target polygon area such that weights +

    `normalize = FALSE`, if a polygon from x (source) is entirely within a polygon in y +(target), w will be 1. If a polygon from x (source) is 50 +and 50 +in each. Weights will sum to 1 per **SOURCE** polygon if the target polygons fully cover that +feature.

    +

    For `normalize = FALSE` the area weighted mean calculation must include the area of each +x (source) polygon as in:

    +

    > *in this case, `area` is the area of source polygons and you would do this operation grouped +by target polygon id.*

    +

    > `sum( (val * w * area), na.rm = TRUE ) / sum(w * area)`

    +

    If `normalize = TRUE`, weights are divided by the target polygon area such that weights sum to 1 per TARGET polygon if the target polygon is fully covered by source polygons.

    +

    For `normalize = FALSE` the area weighted mean calculation no area is required +as in:

    +

    > `sum( (val * w), na.rm = TRUE ) / sum(w)`

    +

    See examples for illustration of these two modes.

    Examples

    - 
    b1 = sf::st_polygon(list(rbind(c(-1,-1), c(1,-1),
-                           c(1,1), c(-1,1),
-                           c(-1,-1))))
-b2 = b1 + 2
-b3 = b1 + c(-0.2, 2)
-b4 = b1 + c(2.2, 0)
-b = sf::st_sfc(b1, b2, b3, b4)
-a1 = b1 * 0.8
-a2 = a1 + c(1, 2)
-a3 = a1 + c(-1, 2)
-a = sf::st_sfc(a1,a2,a3)
-plot(b, border = 'red')
-plot(a, border = 'green', add = TRUE)
-
-
-sf::st_crs(b) <- sf::st_crs(a) <- sf::st_crs(5070)
-
-b <- sf::st_sf(b, data.frame(idb = c(1, 2, 3, 4)))
-a <- sf::st_sf(a, data.frame(ida = c(1, 2, 3)))
-
-sf::st_agr(a) <- sf::st_agr(b) <- "constant"
-
-calculate_area_intersection_weights(a, b, normalize = FALSE)
-#> # A tibble: 4 × 3
-#>     ida   idb     w
-#>   <dbl> <dbl> <dbl>
-#> 1     1     1 1    
-#> 2     2     2 0.5  
-#> 3     2     3 0.375
-#> 4     3     3 0.625
-calculate_area_intersection_weights(a, b, normalize = TRUE)
-#> # A tibble: 4 × 3
-#>     ida   idb     w
-#>   <dbl> <dbl> <dbl>
-#> 1     1     1 1    
-#> 2     2     2 1    
-#> 3     2     3 0.375
-#> 4     3     3 0.625
-calculate_area_intersection_weights(b, a, normalize = FALSE)
-#> # A tibble: 5 × 3
-#>     idb   ida     w
-#>   <dbl> <dbl> <dbl>
-#> 1     1     1  0.64
-#> 2     2     2  0.32
-#> 3     3     2  0.24
-#> 4     3     3  0.4 
-#> 5     4    NA NA   
-calculate_area_intersection_weights(b, a, normalize = TRUE)
-#> # A tibble: 5 × 3
-#>     idb   ida      w
-#>   <dbl> <dbl>  <dbl>
-#> 1     1     1  1    
-#> 2     2     2  0.571
-#> 3     3     2  0.429
-#> 4     3     3  1    
-#> 5     4    NA NA    
-
-#a more typical arrangement of polygons
-
-b1 = sf::st_polygon(list(rbind(c(-1,-1), c(1,-1),
-                           c(1,1), c(-1,1),
-                           c(-1,-1))))
-b2 = b1 + 2
-b3 = b1 + c(0, 2)
-b4 = b1 + c(2, 0)
-b = sf::st_sfc(b1, b2, b3, b4)
-a1 = b1 * 0.75 + c(-.25, -.25)
-a2 = a1 + 1.5
-a3 = a1 + c(0, 1.5)
-a4 = a1 + c(1.5, 0)
-a = sf::st_sfc(a1,a2,a3, a4)
-plot(b, border = 'red', lwd = 3)
-plot(a, border = 'green', add = TRUE)
-
-
-sf::st_crs(b) <- sf::st_crs(a) <- sf::st_crs(5070)
-
-b <- sf::st_sf(b, data.frame(idb = c(1, 2, 3, 4)))
-a <- sf::st_sf(a, data.frame(ida = c("a", "b", "c", "d")))
+    
    
+library(sf)
+#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
 
-sf::st_agr(a) <- sf::st_agr(b) <- "constant"
+source <- st_sf(source_id = c(1, 2), 
+                val = c(10, 20), 
+                geom = st_as_sfc(c(
+  "POLYGON ((0.2 1.2, 1.8 1.2, 1.8 2.8, 0.2 2.8, 0.2 1.2))", 
+  "POLYGON ((-1.96 1.04, -0.04 1.04, -0.04 2.96, -1.96 2.96, -1.96 1.04))")))
 
-# say we have data from `a` that we want sampled to `b`.
-# this gives the percent of each `a` that intersects each `b`
+source$area <- as.numeric(st_area(source))
 
-(a_b <- calculate_area_intersection_weights(a, b, normalize = FALSE))
-#> # A tibble: 9 × 3
-#>   ida     idb     w
-#>   <chr> <dbl> <dbl>
-#> 1 a         1 1    
-#> 2 b         1 0.111
-#> 3 c         1 0.333
-#> 4 d         1 0.333
-#> 5 b         2 0.444
-#> 6 b         3 0.222
-#> 7 c         3 0.667
-#> 8 b         4 0.222
-#> 9 d         4 0.667
+target <- st_sf(target_id = "a", 
+                geom = st_as_sfc("POLYGON ((-1.2 1, 0.8 1, 0.8 3, -1.2 3, -1.2 1))"))
 
-# note that `w` sums to 1 where `b` completely covers `a`.
-
-dplyr::summarize(dplyr::group_by(a_b, ida), w = sum(w))
-#> # A tibble: 4 × 2
-#>   ida       w
-#>   <chr> <dbl>
-#> 1 a         1
-#> 2 b         1
-#> 3 c         1
-#> 4 d         1
-
-# We can apply these weights like...
-dplyr::tibble(ida = unique(a_b$ida), 
-                   val = c(1, 2, 3, 4)) |>
-  dplyr::left_join(a_b, by = "ida") |>
-  dplyr::mutate(val = ifelse(is.na(w), NA, val),
-                areasqkm = 1.5 ^ 2) |> # area of each polygon in `a`
-  dplyr::group_by(idb) |> # group so we get one row per `b`
-  # now we weight by the percent of the area from each polygon in `b` per polygon in `a`
-  dplyr::summarize(new_val = sum( (val * w * areasqkm), na.rm = TRUE ) / sum(w * areasqkm))
-#> # A tibble: 4 × 2
-#>     idb new_val
-#>   <dbl>   <dbl>
-#> 1     1    2   
-#> 2     2    2   
-#> 3     3    2.75
-#> 4     4    3.5 
-  
-# we can go in reverse if we had data from b that we want sampled to a
-
-(b_a <- calculate_area_intersection_weights(b, a, normalize = FALSE))
-#> # A tibble: 9 × 3
-#>     idb ida        w
-#>   <dbl> <chr>  <dbl>
-#> 1     1 a     0.562 
-#> 2     1 b     0.0625
-#> 3     2 b     0.25  
-#> 4     3 b     0.125 
-#> 5     4 b     0.125 
-#> 6     1 c     0.188 
-#> 7     3 c     0.375 
-#> 8     1 d     0.188 
-#> 9     4 d     0.375 
-
-# note that `w` sums to 1 only where `a` complete covers `b`
-
-dplyr::summarize(dplyr::group_by(b_a, idb), w = sum(w))
-#> # A tibble: 4 × 2
-#>     idb     w
-#>   <dbl> <dbl>
-#> 1     1  1   
-#> 2     2  0.25
-#> 3     3  0.5 
-#> 4     4  0.5 
-
-# with `normalize = TRUE`, `w` will sum to 1 when the target 
-# completely covers the source rather than when the source completely
-# covers the target. 
-
-(a_b <- calculate_area_intersection_weights(a, b, normalize = TRUE))
-#> # A tibble: 9 × 3
-#>   ida     idb      w
-#>   <chr> <dbl>  <dbl>
-#> 1 a         1 0.562 
-#> 2 b         1 0.0625
-#> 3 c         1 0.188 
-#> 4 d         1 0.188 
-#> 5 b         2 1     
-#> 6 b         3 0.25  
-#> 7 c         3 0.75  
-#> 8 b         4 0.25  
-#> 9 d         4 0.75  
-
-dplyr::summarize(dplyr::group_by(a_b, idb), w = sum(w))
-#> # A tibble: 4 × 2
-#>     idb     w
-#>   <dbl> <dbl>
-#> 1     1     1
-#> 2     2     1
-#> 3     3     1
-#> 4     4     1
-
-(b_a <- calculate_area_intersection_weights(b, a, normalize = TRUE))
-#> # A tibble: 9 × 3
-#>     idb ida       w
-#>   <dbl> <chr> <dbl>
-#> 1     1 a     1    
-#> 2     1 b     0.111
-#> 3     2 b     0.444
-#> 4     3 b     0.222
-#> 5     4 b     0.222
-#> 6     1 c     0.333
-#> 7     3 c     0.667
-#> 8     1 d     0.333
-#> 9     4 d     0.667
-
-dplyr::summarize(dplyr::group_by(b_a, ida), w = sum(w))
-#> # A tibble: 4 × 2
-#>   ida       w
-#>   <chr> <dbl>
-#> 1 a         1
-#> 2 b         1
-#> 3 c         1
-#> 4 d         1
+plot(source['val'], reset = FALSE)
+plot(st_geometry(target), add = TRUE)
+
 
-# We can apply these weights like...
-# Note that we don't need area in the normalized case
-dplyr::tibble(ida = unique(a_b$ida), 
-                   val = c(1, 2, 3, 4)) |>
-  dplyr::left_join(a_b, by = "ida") |>
-  dplyr::mutate(val = ifelse(is.na(w), NA, val)) |>
-  dplyr::group_by(idb) |> # group so we get one row per `b`
-  # now we weight by the percent of the area from each polygon in `b` per polygon in `a`
-  dplyr::summarize(new_val = sum( (val * w), na.rm = TRUE ))
-#> # A tibble: 4 × 2
-#>     idb new_val
-#>   <dbl>   <dbl>
-#> 1     1    2   
-#> 2     2    2   
-#> 3     3    2.75
-#> 4     4    3.5 
+(w <- 
+calculate_area_intersection_weights(source[c("source_id", "geom")], 
+                                    target[c("target_id", "geom")], 
+                                    normalize = FALSE, allow_lonlat = TRUE))
+#> # A tibble: 2 × 3
+#>   source_id target_id     w
+#>       <dbl> <chr>     <dbl>
+#> 1         1 a         0.375
+#> 2         2 a         0.604
+
+(res <-
+merge(st_drop_geometry(source), w, by = "source_id"))
+#>   source_id val   area target_id         w
+#> 1         1  10 2.5600         a 0.3750000
+#> 2         2  20 3.6864         a 0.6041667
+
+sum(res$val * res$w * res$area) / sum(res$w * res$area)
+#> [1] 16.98795
+
+(w <-
+calculate_area_intersection_weights(source[c("source_id", "geom")], 
+                                    target[c("target_id", "geom")], 
+                                    normalize = TRUE, allow_lonlat = TRUE))
+#> # A tibble: 2 × 3
+#>   source_id target_id     w
+#>       <dbl> <chr>     <dbl>
+#> 1         1 a         0.24 
+#> 2         2 a         0.557
+(res <-
+merge(st_drop_geometry(source), w, by = "source_id"))
+#>   source_id val   area target_id      w
+#> 1         1  10 2.5600         a 0.2400
+#> 2         2  20 3.6864         a 0.5568
+
+sum(res$val * res$w) / sum(res$w)
+#> [1] 16.98795
    @@ -360,7 +216,7 @@

    Examples

    -

    Site built with pkgdown 2.0.7.

    +

    Site built with pkgdown 2.0.9.

    diff --git a/docs/reference/create_cell_geometry.html b/docs/reference/create_cell_geometry.html index 76df750..8dc5527 100644 --- a/docs/reference/create_cell_geometry.html +++ b/docs/reference/create_cell_geometry.html @@ -1,5 +1,5 @@ -Create Cell Geometry — create_cell_geometry • ncdfgeomCreate Cell Geometry — create_cell_geometry • ncdfgeom @@ -17,7 +17,7 @@ ncdfgeom - 1.2.0 + 1.3.0 @@ -37,6 +37,9 @@