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$species))){
-  
-  kde_samples <- dismo::randomPoints(tgb_kde, 
-                                     n = 10000, 
-                                     prob = TRUE)
-  bg_df <- kde_samples %>% 
-    as.data.frame() %>%
-    mutate(species = unique(sp_all$species)[i]) %>% 
-    bind_rows(bg_df)
-  
-}
-
-nrow(bg_df)
-
-
-
-
-
-
-
-
diff --git a/shiny-resources/aus_SA2.gpkg b/shiny-resources/aus_SA2.gpkg
index d07035d..d463db7 100644
Binary files a/shiny-resources/aus_SA2.gpkg and b/shiny-resources/aus_SA2.gpkg differ