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Visualize exposure to values in a summary table

Source: R/fire_exp_extract_summary.R
fire_exp_extract_summary.Rd

fire_exp_extract_summary() standardizes the visualization of outputs from fire_exp_extract() as a summary table by classifying exposure into predetermined exposure classes.

Usage

fire_exp_extract_summary(
  values_ext,
  classify = c("local", "landscape", "custom"),
  class_breaks,
  method = c("max", "mean")
)

Arguments

values_ext

Spatvector of points or polygons from fire_exp_extract()

classify

character, either "local", "landscape", or "custom", to specify classification scheme to use. The default is "local". If set to "custom": the parameter class_breaks must be used.

class_breaks

vector of numeric values between 0-1. Ignored unless classify = "custom". See details.

method

character, either "max" or "mean". If values_ext are polygons the default is "max".This parameter is ignored when values_ext are point features.

Value

a summary table is returned as a data frame

Details

This function visualizes the outputs from fire_exp_extract() with classes. Classes can be chosen from the pre-set "local" and "landscape" options, or customized. To use a custom classification scheme, it should be defined with a list of numeric vectors defining the upper limits of the breaks. A Nil class is added automatically for exposure values of exactly zero.

Local classification breaks are predefined as c(0.15, 0.3, 0.45, 1):

  • Nil (0)

  • 0 - 0.15

  • 0.15 - 0.3

  • 0.3 - 0.45

  • 0.45 - 1

#' Landscape classification breaks are predefined as c(0.2, 0.4, 0.6, 0.8, 1):

  • Nil (0)

  • 0 - 0.2

  • 0.2 - 0.4

  • 0.4 - 0.6

  • 0.6 - 0.8

  • 0.8 - 1

Examples

# read example hazard data
hazard_file_path <- "extdata/hazard.tif"
hazard <- terra::rast(system.file(hazard_file_path, package = "fireexposuR"))

# read example area of interest
polygon_path <- system.file("extdata", "polygon.shp", package ="fireexposuR")
aoi <- terra::vect(polygon_path)

# generate random points within the aoi polygon
points <- terra::spatSample(aoi, 100)

# compute exposure
exposure <- fire_exp(hazard)

values_exp <- fire_exp_extract(exposure, points)

# summarize example points in a table
fire_exp_extract_summary(values_exp, classify = "local")
#>   class_range  n prop method
#> 1    0 - 0.15  8 0.08     NA
#> 2  0.15 - 0.3 25 0.25     NA
#> 3  0.3 - 0.45 46 0.46     NA
#> 4    0.45 - 1 21 0.21     NA