2. Summarizing categorical data
In exactextractr: Fast Extraction from Raster Datasets using Polygons

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7
)
knitr::opts_knit$set(
  global.par = TRUE
)

par(mar = c(1, 1, 1, 1))

Introduction

This vignette builds upon the sample data for São Miguel (introduced in the previous vignette) to demonstrate the use of exactextractr with categorical land cover data. A sample of the CORINE 2018 raster dataset is included in exactextractr.

As in the previous vignette, the following packages are used:

library(exactextractr)
library(dplyr)
library(sf)
library(raster)

Loading the sample data

First, we load the CORINE land cover data and the concelho boundaries.

clc <- raster(system.file('sao_miguel/clc2018_v2020_20u1.tif',
                     package = 'exactextractr'))
concelhos <- st_read(system.file('sao_miguel/concelhos.gpkg',
                                 package = 'exactextractr'),
                     quiet = TRUE)

corine_palette <- c(
  "#e6004d", "#ff0000", "#cc4df2", "#cc0000", "#e6cccc", "#e6cce6", "#a600cc",
  "#a64d00", "#ff4dff", "#ffa6ff", "#ffe6ff", "#ffffa8", "#ffff00", "#e6e600",
  "#e68000", "#f2a64d", "#e6a600", "#e6e64d", "#ffe6a6", "#ffe64d", "#e6cc4d",
  "#f2cca6", "#80ff00", "#00a600", "#4dff00", "#ccf24d", "#a6ff80", "#a6e64d",
  "#a6f200", "#e6e6e6", "#cccccc", "#ccffcc", "#000000", "#a6e6cc", "#a6a6ff",
  "#4d4dff", "#ccccff", "#e6e6ff", "#a6a6e6", "#00ccf2", "#80f2e6", "#00ffa6",
  "#a6ffe6", "#e6f2ff", "#ffffff")

plot(clc, col = corine_palette,
     axes = FALSE, legend = FALSE)
plot(st_geometry(concelhos), add = TRUE)

The land cover class descriptions are provided in a separate DBF file. We read this in to a data frame, then use levels() to associate the class descriptions with the raster.

clc_classes <- foreign::read.dbf(system.file('sao_miguel/clc2018_v2020_20u1.tif.vat.dbf',
                                             package = 'exactextractr'),
                                 as.is = TRUE) %>%
  dplyr::select(value = Value,
                landcov = LABEL3)

levels(clc) <- list(data.frame(ID = clc_classes$value,
                               landcov = clc_classes$landcov))

This association provides us with a way to look up the description for a given ID. Alternatively, we can relate the values using merge or a `dplyr join.

factorValues(clc, c(2, 18, 24))

Summarizing land cover classifications

One of the most basic questions we might ask is which land cover classification is predominant in each concelho. We can do this with the built-in mode summary operation. The minority and variety operations are also applicable to categorical data and provide the least-common classification and number of distinct classifications, respectively.

landcov_mode <- exact_extract(clc, concelhos, 'mode', 
                              append_cols = 'name', progress = FALSE) %>%
  inner_join(clc_classes, by=c(mode = 'value'))

landcov_mode %>%
  dplyr::select(-mode) %>%
  knitr::kable()

Summary functions

While mode provides a handy way to see the most common land cover category, we need to write a custom summary function if we want to see the frequency of different land cover types in an area.

Summary functions are called once per feature from the input sf object. They can return either:

a scalar, in which case the return value of exact_extract will be a vector whose entries correspond with the rows of the input sf object, or
a data frame, in which case exact_extract will return a rowwise combination of the data frames for each feature. If the data frame returned by the summary function will have than a single row, it is useful for some identifying information to be included in the returned data frame.

If we are going to perform typical data frame operations on the raster values and coverage fractions, it can be more convenient for the summary function to accept a single data frame argument, instead of separate arguments for the cell values and coverage fractions. This behavior can be enabled with the summarize_df argument.

Using this method, we can calculate the fraction of each concelho that is covered by each land cover category:

landcov_fracs <- exact_extract(clc, concelhos, function(df) {
  df %>%
    mutate(frac_total = coverage_fraction / sum(coverage_fraction)) %>%
    group_by(name, value) %>%
    summarize(freq = sum(frac_total))
}, summarize_df = TRUE, include_cols = 'name', progress = FALSE)

Here we use the include_cols argument here to include the name column from concelhos in the data frame passed to the summary function. (Although the value of name will be the same for all rows in df, we include name in the group_by expression so that it is not removed by summarize.) Other similar arguments include include_xy to get the cell center coordinates, include_area to get the cell area, and include_cell to get the cell index used by the raster package.

This provides us with a correspondence between each numeric land cover category and its frequency in each concelho:

head(landcov_fracs)

Joining this table to clc_classes, we can associate the descriptions with the numeric types and view the three most common land cover classes in each concelho:

landcov_fracs %>%
  inner_join(clc_classes, by = 'value') %>%
  group_by(name) %>%
  arrange(desc(freq)) %>%
  slice_head(n = 3) %>%
  mutate(freq = sprintf('%0.1f%%', 100*freq)) %>%
  knitr::kable()

Similarly, we can find the top land covers by area:

landcov_areas <- exact_extract(clc, concelhos, function(df) {
  df %>%
    group_by(name, value) %>%
    summarize(area_km2 = sum(coverage_area) / 1e6)
}, summarize_df = TRUE, coverage_area = TRUE, include_cols = 'name', progress = FALSE)

landcov_areas %>%
  inner_join(clc_classes, by = 'value') %>%
  dplyr::select(-value) %>%
  group_by(name) %>%
  arrange(desc(area_km2)) %>%
  slice_head(n = 3) %>%
  knitr::kable()

Summarizing population land cover

One extension of the analysis above is to see which land covers are associated with human population in a given concelho. Is the population primary urban or rural?

As described in the previous vignette, the population density raster provides the most robust results in the presence of partially-covered pixels.

pop_density <- raster(system.file('sao_miguel/gpw_v411_2020_density_2020.tif',
                                  package = 'exactextractr'))

We are able to perform this analysis because the CORINE sample distributed with exactextractr has been reprojected from its native Lambert Equal Area projection into geographic coordinates consistent with GPW. Otherwise, working with multiple rasters in different projections requires transformation to a common grid using tools such as raster::projectRaster or the gdalwarp command-line utility.

Very little about the call to exact_extract requires changing to incorporate population. We set weights = pop_density and, since we are using the summarize_df option, a column called weight will be added to the data frame passed to the summary function. We also set coverage_area = TRUE so that we can multiply the density by the covered pixel area to get a population count.

landcov_pop_areas <- exact_extract(clc, concelhos, function(df) {
  df %>%
    group_by(name, value) %>%
    summarize(pop = sum(coverage_area * weight / 1e6)) %>%
    mutate(pop_frac = pop / sum(pop))
}, weights = pop_density, coverage_area = TRUE,
   summarize_df = TRUE, include_cols = 'name')

Looking at the highest-population land cover type in each concelho, we can can see that the western/central concelhos of Lagoa, Ponta Delgada, Ribeira Grande, and Vila Franca do Campo have a more urban population than Nordeste or Povoação to the east.

landcov_pop_areas %>%
  inner_join(clc_classes, by = 'value') %>%
  group_by(name) %>%
  arrange(desc(pop_frac)) %>%
  slice_head(n = 1) %>%
  dplyr::select(name, landcov, pop, pop_frac) %>%
  mutate(pop = round(pop),
         pop_frac = round(pop_frac, 3)) %>%
  knitr::kable()