Convert rasters to approximate corresponding h3geo.org hex index

raster_to_hex.R

unlink("mobi.xyz")

remotes::install_github("cboettig/duckdbfs")

library(duckdbfs)
library(dplyr)
library(sf)

tracts_z8 = open_dataset("https://minio.carlboettiger.info/public-social-vulnerability/2022-tracts-h3-z8.parquet", recursive = FALSE) |>
            mutate(h8 = tolower(h8))

# ~ 30 sec.  We could speed up by downscaling with the warper at the same time
sf::gdal_utils("warp", 
               "/vsicurl/https://minio.carlboettiger.info/public-mobi/mobi-species-richness.tif",
               "mobi.xyz",
               options = c("-t_srs", "EPSG:4326", "-of", "XYZ"))

# ~ fast
combined <- open_dataset("mobi.xyz", 
             recursive = FALSE, 
             format = "csv",
             parser_options = 
               c(delim = "' '", 
                 columns = "{'X': 'FLOAT', 'Y': 'FLOAT', 'Z': 'INTEGER'}")
        ) |>
        mutate(h8 = h3_latlng_to_cell_string(Y, X, 8L)) |>
        inner_join(tracts_z8)

# Average the species richness to the county level
combined |> group_by(COUNTY) |> summarize(mean_richness = mean(Z))