fire_exp_extract_vis {fireexposuR} | R Documentation |
Visualize exposure to values in a summary table or map
Description
fire_exp_extract_vis()
standardizes the visualization of
outputs from fire_exp_extract()
as a summary table or a map by classifying
exposure into predetermined exposure classes.
Usage
fire_exp_extract_vis(
values_ext,
classify = c("local", "landscape", "custom"),
class_breaks,
method = c("max", "mean"),
map = FALSE,
zoom_level,
title = "Classified Exposure to Values"
)
Arguments
values_ext |
Spatvector of points or polygons from |
classify |
character, either |
class_breaks |
vector of numeric values between 0-1. Ignored unless
|
method |
character, either |
map |
Boolean. When |
zoom_level |
(Optional). Numeric. Ignored when
|
title |
(Optional) String. Ignored when |
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
Spatial reference
This function dynamically pulls map tiles for a base map when map = TRUE
.
The inputs are projected to WGS 84/Pseudo-Mercator
(EPSG:3857) to align them with the map tiles.
Zoom level
The map tile zoom level may need to be adjusted. If the base map is blurry, increase the zoom level. Higher zoom levels will slow down the function, so only increase if necessary. Reference the OpenStreetMap Wiki for more information on zoom levels.
Value
a summary table is returned as a data frame object, Unless:
map = TRUE
: a ggplot object
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 geometry
geom_file_path <- "extdata/polygon_geometry.csv"
geom <- read.csv(system.file(geom_file_path, package = "fireexposuR"))
# generate an area of interest polygon with the geometry
aoi <- terra::vect(as.matrix(geom), "polygons", crs = hazard)
# 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_vis(values_exp, classify = "local")
# visualize example points in standardized map
fire_exp_extract_vis(values_exp, map = TRUE)