Spatial Autocorrelation

# Adapted from "An Introduction to R for Spatial Analysis and Mapping" by Chris
# Brusdon and Lex Comber

## load required libraries: sf, GISTools, haffutils

## read hazard data as an sf object
wi.hazards <- st_read("https://gitlab.com/mhaffner/data/-/raw/master/wi_hazards.geojson")

## create function
rand_maps <- function(variable, colors) {
  ## this function will draw six maps - one will contain the actual data, the
  ## other five will be randomizations

  ## account for three different variable types
  if (variable == 'Flood') {
    plot.var <- 'flod_evt'
  } else if (variable == 'Tornado') {
    plot.var <- 'torn_evt'
  } else if (variable == 'Hail') {
    plot.var <- 'hail_evt'
  } else {
    plot.var <- variable
  }

  ## convert from sf to sp
  wi.hazards <- as(wi.hazards, 'Spatial')

  ## set up the plotting area (3 rows, 2 columns)
  par(mfrow=c(3,2))

  # set the page margins
  par(mar=c(1,1,1,1))

  ## select a random value between 1 and 6 for the actual data
  real.data.i <- sample(1:6,1)

  ## create color scheme
  shades <- auto.shading(wi.hazards[[plot.var]], n = 9, cols = brewer.pal(9, colors))

  ## create maps
  for (i in 1:6) {
    ## case where i is equal to the value corresponding to the real data, plot
    ## the actual data
    if (i == real.data.i) {
      choropleth(wi.hazards, wi.hazards[[plot.var]], shades)
    } else {
      ## if i does not correspond to the real data, plot a randomization using the sample function
      choropleth(wi.hazards, sample(wi.hazards[[plot.var]]), shades)
    }
    title(i)
  }
  return(real.data.i)
}

## use the function here, set the result equal to a variable
val <- rand_maps("Flood", "Blues")

## use the function with different variables and colors

##### challenge 1
## use the rand_maps function with different variables and colors with
## schemes that can be found here:
## http://colorbrewer2.org/#type=sequential&scheme=PuBuGn&n=8
##
## display color palettes first
display.brewer.all()