prepare_raster_data.R

# preparing data 1

library(tidyverse)
library(terra)
library(geodata)


# all these data can be downloaded from: <https://doi.org/10.26188/21630146.v1>


# get extent map for our basic analyses - using outline of Kenya

kenya_vector <- gadm(
  country = "KEN",
  level = 0,
  path = "data/downloads"
) # slow to run though not large

kenya_vector

plot(kenya_vector)

# let's download some data

# travel time from city to cell
# you can explore the sizes of city using ?travel time, and change size if you wish
travel <- travel_time(
  to = "city",
  size = 2,
  path = "data/downloads"
) # 421 MB - may be slow to download

# it's a good idea to have a look at your objects and even plot them


# bioclimactic variables from worldclim
# https://worldclim.org/data/bioclim.html
bioclim_kenya <- worldclim_country(
  country = "KEN",
  var = "bio",
  res = 0.5,
  path = "data/downloads"
)

# have a look at this object first
# Do you think you don't want to plot all layers at once?

# process these data to match our extent

# generate mask of our area of interest so we can use it to process other data into this shape
kenya_mask <- bioclim_kenya[[1]] %>%
  mask(kenya_vector) * 0 + 1
names(kenya_mask) <- "Kenya"
plot(kenya_mask)


# process bioclim into shape
bc_kenya <- mask(
  x = bioclim_kenya,
  mask = kenya_mask
)

# make a low resolution version (to deal with bandwidth issues)
bc_kenya_lores <- aggregate(bc_kenya,
                            fact = 5,
                            fun = "mean",
                            na.rm = TRUE)

# change up the names
old_names <- names(bc_kenya_lores)
new_names <- str_remove(old_names, "wc2.1_30s_")
names(bc_kenya_lores) <- new_names

# process travel data
# this is a very large raster, we speed up the operation by cropping then masking
# try looking at the result of only cropping to understand why both steps are needed
travel_kenya <- crop(
  x = travel,
  y = kenya_mask
) %>%
  mask(
    mask = kenya_mask
  )

# let's have a look at it




# now let's create a bias layer out of our travel data
# it should scale from 0-1 where 0 is hard to get to and 1 is easy

plot(travel_kenya)

rescale_travel <- travel_kenya

rescale_travel[] <- rescale_travel[]/ max(rescale_travel[], na.rm = TRUE)

rescale_travel[] <- 1 - rescale_travel[]

plot(rescale_travel)



# now let's write our key rasters to disk
# NB the above download functions will only need to download once;
# once those objects are saved in that location, they will be re-read from disk

terra::writeRaster(
  x = kenya_mask,
  filename = "data/rasters/kenya_mask.tif",
  overwrite = TRUE
)

terra::writeRaster(
  x = bc_kenya,
  filename = "data/rasters/bc_kenya.tif",
  overwrite = TRUE
)

terra::writeRaster(
  x = bc_kenya_lores,
  filename = "data/rasters/bc_kenya_lores.tif",
  overwrite = TRUE
)

terra::writeRaster(
  x = rescale_travel,
  filename = "data/rasters/rescale_travel.tif",
  overwrite = TRUE
)


# BIO1 = Annual Mean Temperature
#
# BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
#
# BIO3 = Isothermality (BIO2/BIO7) (×100)
#
# BIO4 = Temperature Seasonality (standard deviation ×100)
#
# BIO5 = Max Temperature of Warmest Month
#
# BIO6 = Min Temperature of Coldest Month
#
# BIO7 = Temperature Annual Range (BIO5-BIO6)
#
# BIO8 = Mean Temperature of Wettest Quarter
#
# BIO9 = Mean Temperature of Driest Quarter
#
# BIO10 = Mean Temperature of Warmest Quarter
#
# BIO11 = Mean Temperature of Coldest Quarter
#
# BIO12 = Annual Precipitation
#
# BIO13 = Precipitation of Wettest Month
#
# BIO14 = Precipitation of Driest Month
#
# BIO15 = Precipitation Seasonality (Coefficient of Variation)
#
# BIO16 = Precipitation of Wettest Quarter
#
# BIO17 = Precipitation of Driest Quarter
#
# BIO18 = Precipitation of Warmest Quarter
#
# BIO19 = Precipitation of Coldest Quarter


#plot(bc_kenya[[1:10]])
#plot(bc_kenya[[11:19]])
# 1, 5, 6, 8, 9, 10, 11 + 1; 12, 13, 14, 16, 17, 18, 19 -1
# 2, 7, + 1
# 3, 4, -1


bc_kenya_123 <- bioclim_kenya[[1:3]]
writeRaster(
  bc_kenya_123,
  filename = "data/rasters/bc_kenya_123.tif"
)