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‎.gitignore‎

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@@ -61,3 +61,4 @@ rsconnect/
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# pixi environments
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.pixi/*
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!.pixi/config.toml
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/RT

‎R/filter_save.R‎

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@@ -13,7 +13,7 @@
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#'
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#' @param filt `[matrix]` \cr Filter or weight matrix, such as one created by
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#' [oneimpact::filter_create()] or [terra::focalMat()].
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#' @param radius `[numeric(1)]` \cr Radius of the Zone of Influence (ZoI) of
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#' @param radius `[numeric(1)]` \cr Radius of the Zone of Influence (ZOI) of
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#' the matrix, in meters.
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#' @param type `[character(1)]` Function for the kernel or filter matrix
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#' (see `type` parameter for [oneimpact::filter_create()]).

‎examples/calc_zoi_cumulative_example.R‎

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@@ -39,6 +39,6 @@ density_exp <- calc_zoi_cumulative(cabins, type = "exp_decay", radius = 1000,
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# compare
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# note the difference in the color scales
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plot(c(cumzoi_exp[[3]], density_exp),
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main = c("Cumulative ZoI 1000m", "Density 1000m"))
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main = c("Cumulative ZOI 1000m", "Density 1000m"))
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#--------------------

‎examples/calc_zoi_cumulative_grass_example.R‎

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# Running calc_zoi_cumulative through GRASS GIS
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library(rgrass)
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library(terra)
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# Load raster data
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f <- system.file("raster/sample_area_cabins.tif", package = "oneimpact")
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cabins <- terra::rast(f)
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# connect to grass gis and create grass location
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# For linux or within OSGeo4W shell
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grassdir <- system("grass --config path", intern = TRUE)
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# grassdir <- system("grass78 --config path", intern = TRUE) # for GRASS 7.8
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# If you used the standalone installer in Windows
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# grassdir <- "C:\\Programs\\GRASS GIS 7.8" # Correct if the path GRASS version or path is different
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gisDB <- "." # create location and mapset in the working directory
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loc <- "ETRS_33N/" # name of the location
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ms <- "PERMANENT" # name of the mapset
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rgrass::initGRASS(gisBase = grassdir,
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SG = cabins, # use map to define location projection
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home = tempdir(),
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override = TRUE,
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gisDbase = gisDB,
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location = loc,
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mapset = ms)
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# define map name within GRASS GIS
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cabins_g <- "cabins_example"
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# add file to GRASS GIS mapset
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rgrass::write_RAST(cabins, cabins_g, flags = c("o", "overwrite"))
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# check
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terra::plot(cabins, col = "black",
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main = "Map of tourist cabins")
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#---
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# define region in GRASS GIS
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rgrass::execGRASS("g.region", raster = cabins_g,
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flags = "p")
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#---
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# Guarantee input map is binary (zeros as background)
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# Input map name within GRASS GIS - binary map
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cabins_bin_g <- grass_binarize(cabins_g, breaks = 1, output = "cabins_example_bin",
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null = 0, overwrite = TRUE)
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# check input
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cabins_bin <- rgrass::read_RAST("cabins_example_bin", return_format = "terra", NODATA = 255)
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plot(cabins_bin, col = c("lightyellow", "black"),
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main = "Binarized map of cabins")
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#---
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# Using 'r.mfilter' algorithm (default)
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# Exponential decay
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exp_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "exp_decay",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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# Bartlett decay
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barlett_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "bartlett",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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# Gaussian decay
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gauss_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "Gauss",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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# Threshold decay (circle, step)
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threshold_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "threshold",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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(all_names <- c(exp_name, barlett_name, gauss_name, threshold_name))
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# visualize
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cabins_zoi_cumulative <- rgrass::read_RAST(all_names, return_format = "terra")
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title_plot <- c("Exponential decay 1000m", "Bartlett decay 1000m",
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"Gaussian decay 1000m", "Threshold decay 1000m")
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terra::plot(cabins_zoi_cumulative, main = title_plot)
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#---
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# calculate density vs cumulative ZoI
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exp_name_d <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "exp_decay", output_type = "density",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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cabins_density <- rgrass::read_RAST(exp_name_d, return_format = "terra")
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terra::plot(c(cabins_zoi_cumulative[[1]], cabins_density),
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main = c("Cumulative ZoI", "Density"))
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#---
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# Using 'r.resamp.filter' algorithm
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# rectangle
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rectangle_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "box",
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output_type = "density",
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where = "GRASS",
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g_module = "r.resamp.filter",
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g_overwrite = TRUE,
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verbose = TRUE)
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rgrass::read_RAST(rectangle_resamp_filt, return_format = "terra") |>
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plot(main = "Rectangle ZoI 1000m")
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# bartlett
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bartlett_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "bartlett",
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output_type = "cumulative_zoi",
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where = "GRASS",
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g_module = "r.resamp.filter",
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g_overwrite = TRUE,
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verbose = TRUE)
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rgrass::read_RAST(bartlett_resamp_filt, return_format = "terra") |>
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plot(main = "Bartlett ZoI 1000m")
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# not run
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# Gaussian - to be implemented!
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\dontrun{
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gauss_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = "1000,3000",
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type = "gauss,box",
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output_type = "cumulative_zoi",
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where = "GRASS",
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module = "r.resamp.filter",
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overwrite = TRUE, quiet = FALSE)
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rgrass::read_RAST(bartlett_resamp_filt, return_format = "terra") |>
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plot()
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}
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# remove rasters created
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# to_remove_rast <- unique(c(all_names, exp_name_d,
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# rectangle_resamp_filt, bartlett_resamp_filt))
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# rgrass::execGRASS("g.remove", type = "vect", name = to_remove_vect, flags = "f")
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# rgrass::execGRASS("g.remove", type = "rast", name = to_remove_rast, flags = "f")
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# Running calc_zoi_cumulative through GRASS GIS
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library(rgrass)
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library(terra)
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# Load raster data
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f <- system.file("raster/sample_area_cabins.tif", package = "oneimpact")
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cabins <- terra::rast(f)
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9+
# connect to grass gis and create grass location
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# For linux or within OSGeo4W shell
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grassdir <- system("grass --config path", intern = TRUE)
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# grassdir <- system("grass78 --config path", intern = TRUE) # for GRASS 7.8
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# If you used the standalone installer in Windows
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# grassdir <- "C:\\Programs\\GRASS GIS 7.8" # Correct if the path GRASS version or path is different
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gisDB <- "." # create location and mapset in the working directory
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loc <- "ETRS_33N/" # name of the location
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ms <- "PERMANENT" # name of the mapset
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rgrass::initGRASS(gisBase = grassdir,
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SG = cabins, # use map to define location projection
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home = tempdir(),
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override = TRUE,
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gisDbase = gisDB,
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location = loc,
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mapset = ms)
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# define map name within GRASS GIS
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cabins_g <- "cabins_example"
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# add file to GRASS GIS mapset
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rgrass::write_RAST(cabins, cabins_g, flags = c("o", "overwrite"))
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# check
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terra::plot(cabins, col = "black",
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main = "Map of tourist cabins")
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#---
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# define region in GRASS GIS
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rgrass::execGRASS("g.region", raster = cabins_g,
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flags = "p")
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#---
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# Guarantee input map is binary (zeros as background)
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# Input map name within GRASS GIS - binary map
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cabins_bin_g <- grass_binarize(cabins_g, breaks = 1, output = "cabins_example_bin",
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null = 0, overwrite = TRUE)
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# check input
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cabins_bin <- rgrass::read_RAST("cabins_example_bin", return_format = "terra", NODATA = 255)
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plot(cabins_bin, col = c("lightyellow", "black"),
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main = "Binarized map of cabins")
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#---
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# Using 'r.mfilter' algorithm (default)
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# Exponential decay
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exp_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "exp_decay",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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# Bartlett decay
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barlett_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "bartlett",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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# Gaussian decay
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gauss_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "Gauss",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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79+
# Threshold decay (circle, step)
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threshold_name <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "threshold",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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(all_names <- c(exp_name, barlett_name, gauss_name, threshold_name))
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# visualize
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cabins_zoi_cumulative <- rgrass::read_RAST(all_names, return_format = "terra")
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title_plot <- c("Exponential decay 1000m", "Bartlett decay 1000m",
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"Gaussian decay 1000m", "Threshold decay 1000m")
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terra::plot(cabins_zoi_cumulative, main = title_plot)
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#---
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# calculate density vs cumulative ZOI
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exp_name_d <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000, zoi_limit = 0.01,
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type = "exp_decay", output_type = "density",
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where = "GRASS",
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g_overwrite = TRUE,
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verbose = TRUE)
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cabins_density <- rgrass::read_RAST(exp_name_d, return_format = "terra")
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terra::plot(c(cabins_zoi_cumulative[[1]], cabins_density),
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main = c("Cumulative ZOI", "Density"))
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#---
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# Using 'r.resamp.filter' algorithm
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# rectangle
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rectangle_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "box",
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output_type = "density",
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where = "GRASS",
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g_module = "r.resamp.filter",
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g_overwrite = TRUE,
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verbose = TRUE)
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rgrass::read_RAST(rectangle_resamp_filt, return_format = "terra") |>
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plot(main = "Rectangle ZOI 1000m")
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# bartlett
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bartlett_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = 1000,
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type = "bartlett",
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output_type = "cumulative_zoi",
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where = "GRASS",
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g_module = "r.resamp.filter",
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g_overwrite = TRUE,
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verbose = TRUE)
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rgrass::read_RAST(bartlett_resamp_filt, return_format = "terra") |>
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plot(main = "Bartlett ZOI 1000m")
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# not run
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# Gaussian - to be implemented!
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\dontrun{
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gauss_resamp_filt <- calc_zoi_cumulative(x = cabins_bin_g,
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radius = "1000,3000",
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type = "gauss,box",
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output_type = "cumulative_zoi",
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where = "GRASS",
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module = "r.resamp.filter",
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overwrite = TRUE, quiet = FALSE)
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rgrass::read_RAST(bartlett_resamp_filt, return_format = "terra") |>
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plot()
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}
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# remove rasters created
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# to_remove_rast <- unique(c(all_names, exp_name_d,
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# rectangle_resamp_filt, bartlett_resamp_filt))
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# rgrass::execGRASS("g.remove", type = "vect", name = to_remove_vect, flags = "f")
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# rgrass::execGRASS("g.remove", type = "rast", name = to_remove_rast, flags = "f")

‎examples/calc_zoi_example.R‎

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@@ -8,7 +8,7 @@ cabins <- terra::rast(f)
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# check background values
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terra::freq(cabins) ## No zeros, background is NA
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# compute both Zoi metrics with Gaussian decay, radius = 1000 m
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# compute both ZOI metrics with Gaussian decay, radius = 1000 m
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# since the background is NA, we use zeroAsNA = FALSE
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zoi_metrics <- calc_zoi(cabins,
1414
radius = 1000,
@@ -28,7 +28,7 @@ cabins_count <- terra::rast(f)
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# check background values
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terra::freq(cabins_count) ## Places with no infrastructure have value zero
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# compute both Zoi metrics with linear decay, varying radius from 1000 m to 3000 m
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# compute both ZOI metrics with linear decay, varying radius from 1000 m to 3000 m
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# since the background is zero, we use zeroAsNA = TRUE
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zoi_metrics2 <- calc_zoi(cabins_count,
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radius = c(1000, 2000, 3000),

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