From 0a85eec58e47da9915135d8522f319750098f983 Mon Sep 17 00:00:00 2001 From: Alex Slavenko Date: Tue, 9 Jan 2024 16:45:31 +1100 Subject: [PATCH] simplified polygons --- Rsource/data_prep.R | 209 ----------------------------------- shiny-resources/aus_SA2.gpkg | Bin 72646656 -> 72646656 bytes 2 files changed, 209 deletions(-) delete mode 100644 Rsource/data_prep.R diff --git a/Rsource/data_prep.R b/Rsource/data_prep.R deleted file mode 100644 index 7454050..0000000 --- a/Rsource/data_prep.R +++ /dev/null @@ -1,209 +0,0 @@ -# library(rgbif) -library(leaflet) -library(tidyverse) -library(sf) -library(terra) - -# get species data -------------------------------------------------------- -# my species for modelling -species <- c( - "Scirpophaga excerptalis", - "Sesamia grisescens", - "Chilo auricilius", - "Chilo infuscatellus", - "Eumetopina flavipes", - "Matsumuratettix hiroglyphicus", - "Yamatotettix flavovittatus", - "Perkinsiella saccharicida", - "Perkinsiella thompsoni" -) - -get_species <- function(x, genus = FALSE){ - out <- x %>% - str_split(" ") %>% - unlist() - if(genus){ - return(out[1]) - } else{ - out[2] - } -} - - -splist <- list() -for(i in seq_along(species)){ - - gbif_data <- geodata::sp_occurrence(genus = get_species(species[i], TRUE), - species = get_species(species[i], FALSE)) - - # take a look at the downloaded data: - # gbif_data %>% View() - - if(is.null(gbif_data)) next - - sp_coords <- gbif_data %>% - dplyr::select(lon, lat, status = occurrenceStatus, - country, species, genus, family) %>% - drop_na(lon, lat) - - if(nrow(sp_coords) < 2) next - - sp_points <- st_as_sf(sp_coords, coords = c("lon", "lat")) - # sp_points - - splist[[i]] <- sp_points -} - -# check the number of species -unlist(map(splist, nrow)) - -# combine all species data -sp_all <- splist %>% - do.call(bind_rows, .) %>% - st_set_crs(4326) - -head(sp_all) -nrow(sp_all) - - -species_palette <- colorFactor(palette = viridis::inferno(length(unique(sp_all$species))), - domain = unique(sp_all$species)) -leaflet() %>% - addTiles() %>% - # addMarkers(data = sp_points) - addCircleMarkers( - data = sp_all, - radius = 6, - stroke = FALSE, - label = ~species, - color = ~species_palette(species), - fillOpacity = 0.4 - ) - - - - -# raster data ------------------------------------------------------------- -# download bias layer -bias_layer <- geodata::travel_time(path = "data/travel_layer.tif", - size = 1, - up = TRUE) -plot(bias_layer) - -# download bioclim layers -bioclim <- geodata::worldclim_global(var = "bio", - res = 0.5, - path = "data/bioclim.tif") -plot(bioclim) - - -plot(bioclim[[1]]) -plot(sp_points$geometry, add = TRUE) - -leaflet() %>% - addTiles() %>% - addRasterImage(raster::raster(clim[[1]])) %>% - addMarkers(data = sp_points) - -r <- clim[[1]] %>% - terra::aggregate(fact = 5) %>% - raster::raster() - -library(mapview) - -mapview(r) - - -# Crop raster layers ------------------------------------------------------ -fls <- list.files("data/bioclim.tif/wc2.1_30s/", - pattern = ".tif$", - full.names = TRUE) -bioclim <- terra::rast(fls) -plot(bioclim) - -bg_mask <- st_read("data/background_mask.gpkg") -plot(st_geometry(bg_mask)) - -# mask raster layers one-by-one -for(i in 15:nlyr(bioclim)){ - masked <- terra::mask(bioclim[[i]], vect(bg_mask)) - terra::writeRaster(masked, paste0("data/bg_layers/", names(bioclim)[i], ".tif")) - print(i) -} - - -# KDE for background sampling --------------------------------------------- -# loading required libraries -library(spatialEco) -library(terra) -library(disdat) -library(dismo) -library(sf) - -# read TGB data -tgbs <- st_read("data/tgbs.gpkg") -# read a raster mask for the region -rs <- terra::rast("data/bg_layers/wc2.1_30s_bio_1.tif") - -plot(rs) -plot(st_geometry(tgbs), add = TRUE, pch = 16, cex = 0.3) - -# remove duplicated points in raster cells -samplecellID <- terra::cellFromXY(rs, st_coordinates(tgbs)) -dup <- duplicated(samplecellID) -tgbsp <- tgbs[!dup, ] - -nrow(tgbs) -nrow(tgbsp) -# st_write(tgbsp, "data/tgbs_reduced.gpkg") - -tgb_kde <- spatialEco::sp.kde(x = sf::as_Spatial(tgbsp), - bw = 5, # degree - newdata = raster::raster(rs), - standardize = TRUE, - scale.factor = 10000) -plot(tgb_kde) - - -# create background data with accessibility bias -------------------------- -# read species data -sp_all <- st_read("data/species_data.gpkg") - -# accessibility map -access <- terra::rast("data/accessibility_masked.tif") -plot(access) -setMinMax(access) - -# standardize the map -rmin <- terra::minmax(access)[1] -rmax <- terra::minmax(access)[2] -access_std <- (rmax - access) / (rmax - rmin) -plot(access_std) - -# calculate the distance map -distr <- terra::distance(access, vect(sp_all)) -plot - -# create sparate bg records for each species -bg_df <- data.frame() -for(i in seq_along(unique(sp_all$species))){ - - kde_samples <- dismo::randomPoints(tgb_kde, - n = 10000, - prob = TRUE) - bg_df <- kde_samples %>% - as.data.frame() %>% - 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