input('data/Buildings_SE_Upolu.shp', name: 'exposure')
  ->
select({
        *,
        bookmark('data/MaxEnv_All_Scenarios_50m.tif') as hazard_coverage
       })
  ->
select({
        *,
        sample_centroid(exposure, hazard_coverage) as hazard
       })
  ->
select({
        *,
        to_coverage(bookmark('data/Samoa_constituencies.shp')) as area_coverage
       })
  ->
select({
        *,
        # Make sure that every building gets matched to a region
        sample_closest(exposure, area_coverage, buffer-distance: 5000) as area
       })
  ->
select({
        *,
        # Change the `consequence` to either 'Exposed' or 'Not Exposed' in the results
        if(is_null(hazard), 'Not exposed', 'Exposed') as consequence
       })
  ->
select({
        exposure,
        hazard,
        area.Region,
        consequence
       }) as event_impact_table
  ->
# Add a group step so that the results are grouped by consequence and construction type
group(by: { consequence, exposure.Cons_Frame },
      select: {
          consequence,
          Cons_Frame,
          # Use `count(*)` to count the number of buildings in each group
          count(*) as "Number of buildings",
          # Include the median hazard depth for each group
          round(median(hazard) * 100) as Median_depth_cm
      })
  ->
# Add a sort step so that the 'Exposed' results are all grouped together
sort(by: consequence)
  ->
# change the filename that the results are saved in to be regional-exposure.csv
save('regional-exposure.csv')

