wip

.github/workflows/deploy.yml

@@ -17,4 +17,4 @@ jobs:
       - name: Push to hub
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: git push -f https://cboettig:[email protected]/spaces/boettiger-lab/geo-llm-r main
+        run: git push -f https://cboettig:[email protected]/spaces/boettiger-lab/inat-ranges

.gitignore

@@ -47,3 +47,5 @@ po/*~
 
 # RStudio Connect folder
 rsconnect/
+
+.tmp/*

app.R

@@ -1,272 +1,58 @@
 library(shiny)
-library(bslib)
-library(htmltools)
-library(fontawesome)
-library(bsicons)
-library(gt)
-library(colourpicker)
-library(glue)
-
-library(ggplot2)
-library(readr)
-library(dplyr)
 library(mapgl)
-library(duckdbfs)
-duckdbfs::load_spatial()
-
-css <-
-  HTML(paste0("<link rel='stylesheet' type='text/css' ",
-              "href='https://demos.creative-tim.com/",
-              "material-dashboard/assets/css/",
-              "material-dashboard.min.css?v=3.2.0'>"))
-
-
-# Define the UI
-ui <- page_sidebar(
-  fillable = FALSE, # do not squeeze to vertical screen space
-  tags$head(css),
-  titlePanel("Demo App"),
-
-  "
-  This is a proof-of-principle for a simple chat-driven interface
-  to dynamically explore geospatial data.
-  ",
 
-  card(
-    layout_columns(
-      textInput("chat",
-        label = NULL,
-        "Which four counties in California have the highest average social vulnerability?",
-        width = "100%"),
-      div(
-      actionButton("user_msg", "", icon = icon("paper-plane"),
-                   class = "btn-primary btn-sm align-bottom"),
-      class = "align-text-bottom"),
-      col_widths = c(11, 1)),
-      fill = FALSE
-  ),
-  
-  textOutput("agent"),
+source("utils.R")
+source("inat-ranges.R")
 
+load_h3()
+load_spatial()
 
-  layout_columns(
-    card(maplibreOutput("map")),
-    card(includeMarkdown("## Plot"),
-         plotOutput("chart1"),
-         plotOutput("chart2"),
-         ),
-    col_widths = c(8, 4),
-    row_heights = c("500px"),
-    max_height = "600px"
-  ),
+duckdbfs::duckdb_secrets()
+inat <- open_dataset("s3://public-inat/hex")
 
-  gt_output("table"),
+# dummy case
+aoi <- spData::us_states
 
-  card(fill = TRUE,
-    card_header(fa("robot"),  textOutput("model", inline = TRUE)),
-    accordion(
-      open = FALSE,
-      accordion_panel(
-        title = "show sql",
-        icon = fa("terminal"),
-        verbatimTextOutput("sql_code"),
-      ),
-      accordion_panel(
-        title = "explain",
-        icon = fa("user", prefer_type="solid"),
-        textOutput("explanation"),
-      )
-    ),
-  ),
-  card(
-      card_header("Errata"),
-      shiny::markdown(readr::read_file("footer.md")),
-  ),
-  sidebar = sidebar(
-    selectInput(
-    "select",
-    "Select an LLM:", 
-    list("LLama3" = "llama3",
-         #"OLMO2 (AllenAI)" = "olmo",
-         "Gorilla (UC Berkeley)" = "gorilla" 
-        )
-  ),
+taxa <- open_dataset("https://minio.carlboettiger.info/public-inat/taxonomy/taxa.csv", format = "csv", recursive = FALSE)
 
-    input_switch("redlines", "Redlined Areas", value = FALSE),
-    input_switch("svi", "Social Vulnerability", value = TRUE),
-    input_switch("richness", "Biodiversity Richness", value = FALSE),
-    input_switch("rsr", "Biodiversity Range Size Rarity", value = FALSE),
 
+# publish richness at the aoi (bbox or poly)
+m = maplibre(center = c(-110.5, 37), zoom = 3)
 
-    card(
-      card_header(bs_icon("github"), "Source code:"),
-      a(href = "https://github.com/boettiger-lab/geo-llm-r",
-        "https://github.com/boettiger-lab/geo-llm-r"))
-  ),
 
-  theme = bs_theme(version = "5")
+ui <- fluidPage(
+    maplibreOutput("map"),
+    actionButton("get_features", "Get Drawn Features"),
+    verbatimTextOutput("feature_output")
 )
 
-
-repo <- "https://data.source.coop/cboettig/social-vulnerability"
-pmtiles <- glue("{repo}/2022/SVI2022_US_tract.pmtiles")
-parquet <- glue("{repo}/2022/SVI2022_US_tract.parquet")
-con <- duckdbfs::cached_connection()
-svi <- open_dataset(parquet, tblname = "svi") |> filter(RPL_THEMES > 0)
-
-safe_parse <- function(txt) {
-  gsub("[\r\n]", " ", txt) |> gsub("\\s+", " ", x = _)
-}
-
-
-# helper utilities
-# faster/more scalable to pass maplibre the ids to refilter pmtiles,
-# than to pass it the full geospatial/sf object
-filter_column <- function(full_data, filtered_data, id_col = "FIPS") {
-  if (nrow(filtered_data) < 1) return(NULL)
-  values <- full_data |>
-    inner_join(filtered_data, copy = TRUE) |>
-    pull(id_col)
-  # maplibre syntax for the filter of PMTiles  
-  list("in", list("get", id_col), list("literal", values))
-}
-
-
-
-# Define the server
 server <- function(input, output, session) {
+    output$map <- renderMaplibre({
+        m
+    })
 
-  chart1_data <- svi |>
-    group_by(COUNTY) |>
-    summarise(mean_svi = mean(RPL_THEMES)) |>
-    collect()
-
-  chart1 <- chart1_data |>
-    ggplot(aes(mean_svi)) + geom_density(fill="darkred") +
-    ggtitle("County-level vulnerability nation-wide")
-  
-  data <- reactiveValues(df = tibble())
-  output$chart1 <- renderPlot(chart1)
-
-  model <- reactive(input$select)
-  output$model <- renderText(input$select)
-  observe({
-    schema <- read_file("schema.yml")
-    system_prompt <- glue::glue(readr::read_file("system-prompt.md"),
-                                .open = "<", .close = ">")
-    chat <- ellmer::chat_vllm(
-      base_url = "https://llm.nrp-nautilus.io/",
-      model =  model(),
-      api_key = Sys.getenv("NRP_API_KEY"),
-      system_prompt = system_prompt,
-      api_args = list(temperature = 0)
-    )
-
-  observeEvent(input$user_msg, {
-    stream <- chat$chat(input$chat)
-
-    # Parse response
-    response <- jsonlite::fromJSON(safe_parse(stream))
-    #response <- jsonlite::fromJSON(stream)
-
-    if ("query" %in% names(response)) {
-      output$sql_code <- renderText(stringr::str_wrap(response$query, width = 60))
-      output$explanation <- renderText(response$explanation)
-
-      # Actually execute the SQL query generated:
-      df <- DBI::dbGetQuery(con, response$query)
-
-      # don't display shape column in render
-      df <- df |> select(-any_of("Shape"))
-      output$table <- render_gt(df, height = 300)
-
-
-      y_axis <- colnames(df)[!colnames(df) %in% colnames(svi)]
-      chart2 <- df |>
-        rename(social_vulnerability = y_axis) |>
-        ggplot(aes(social_vulnerability)) +
-        geom_density(fill = "darkred")  +
-        xlim(c(0, 1)) +
-        ggtitle("Vulnerability of selected areas")
-
-      output$chart2 <- renderPlot(chart2)
-
-      # We need to somehow trigger this df to update the map.
-      data$df <- df
-
-    # Note: ellmer will preserve full chat history automatically.
-    # this can confuse the agent and mess up behavior, so we reset:
-    chat$set_turns(NULL)
-
-    } else {
-      output$agent <- renderText(response$agent)
-
-    }
-
-  })
-  })
-
-
-  output$map <- renderMaplibre({
-
-    m <- maplibre(center = c(-104.9, 40.3), zoom = 3, height = "400")
-    if (input$redlines) {
-      m <- m |>
-        add_fill_layer(
-          id = "redlines",
-          source = list(type = "vector",
-                        url = paste0("pmtiles://", "https://data.source.coop/cboettig/us-boundaries/mappinginequality.pmtiles")),
-          source_layer = "mappinginequality",
-          fill_color = list("get", "fill")
-        )
-    }
-    if (input$richness) {
-      m <- m |>
-        add_raster_source(id = "richness",
-                          tiles = "https://data.source.coop/cboettig/mobi/tiles/red/species-richness-all/{z}/{x}/{y}.png",
-                          maxzoom = 11
-                          ) |>
-        add_raster_layer(id = "richness-layer",
-                         source = "richness")
-
-    }
-
-     if (input$rsr) {
-      m <- m |>
-        add_raster_source(id = "rsr",
-                          tiles = "https://data.source.coop/cboettig/mobi/tiles/green/range-size-rarity-all/{z}/{x}/{y}.png",
-                          maxzoom = 11
-                          ) |>
-        add_raster_layer(id = "richness-layer",
-                         source = "rsr")
-
-    }
-    if (input$svi) {
-      m <- m |>
-        add_fill_layer(
-          id = "svi_layer",
-          source = list(type = "vector",
-                        url = paste0("pmtiles://", pmtiles)),
-          source_layer = "svi",
-          tooltip = "RPL_THEMES",
-          filter = filter_column(svi, data$df, "FIPS"),
-          fill_opacity = 0.5,
-          fill_color = interpolate(column = "RPL_THEMES",
-                                  values = c(0, 1),
-                                  stops = c("lightpink", "darkred"),
-                                  na_color = "lightgrey")
-        )
-    }
-  m |>
-    add_draw_control() |>
-    add_geocoder_control()
+    output$feature_output <- renderPrint({
+            print(input$map_bbox)
+    })
 
-  })
 
+    observeEvent(input$map_bbox, {
 
+        bbox =sf::st_bbox(unlist(input$map_bbox), crs = 4326)
+        print(bbox)
 
+        output$map <- renderMaplibre({
+            richness(inat, bbox)
+            richness_map(m)
+     })
+    })
 
+#    observeEvent(input$get_features, {
+#        drawn_features <- get_drawn_features(mapboxgl_proxy("map"))
+#        output$feature_output <- renderPrint({
+#            print(drawn_features)
+#        })
+#    })
 }
 
-# Run the app
-shinyApp(ui = ui, server = server)
+shinyApp(ui, server)

inat-ranges.R

@@ -0,0 +1,50 @@
+library(dplyr)
+library(duckdbfs)
+library(mapgl)
+
+# Also requires get_h3_aoi() from utils.R
+
+richness <- function(inat, aoi) {
+
+  h3_aoi  <- get_h3_aoi(aoi, precision = 4) |> select(h3id)
+
+  bench::bench_time({
+  inat |> 
+    rename(h3id = h4) |>
+    inner_join(h3_aoi) |>
+    distinct(taxon_id, h3id) |> 
+    group_by(h3id) |>
+    summarise(n = n()) |>
+    mutate(height = n / max(n)) |> 
+    duckdbfs::to_h3j("s3://public-data/inat-tmp-ranges.h3j")
+  #  write_dataset("s3://public-data/inat-tmp-ranges.parquet")
+  })
+}
+
+richness_map <- function(
+  m = maplibre(center = c(-110.5, 37), zoom = 3),
+  url = "https://minio.carlboettiger.info/public-data/inat-tmp-ranges.h3j"
+  ) {
+  
+  m <- m |>
+    add_h3j_source("h3j_source",
+                  url = url
+    ) |>
+    add_fill_extrusion_layer(
+      id = "h3j_layer",
+      source = "h3j_source",
+      tooltip = "n",
+      fill_extrusion_color = viridis_pal("height"),
+      fill_extrusion_height = list(
+        "interpolate",
+        list("linear"),
+        list("zoom"),
+        0,
+        0,
+        1,
+        list("*", 100000, list("get", "height"))
+      ),
+      fill_extrusion_opacity = 0.7
+    )
+  m
+}

geo-llm-r.Rproj → inat-ranges.Rproj

@@ -1,5 +1,5 @@
 Version: 1.0
-ProjectId: 337131c5-7aa5-4963-bc4e-d8a156e206a0
+ProjectId: 6e13bbf8-d79c-409c-a98f-e51f6bdd7e7a
 
 RestoreWorkspace: Default
 SaveWorkspace: Default
@@ -12,6 +12,3 @@ Encoding: UTF-8
 
 RnwWeave: Sweave
 LaTeX: pdfLaTeX
-
-AutoAppendNewline: Yes
-StripTrailingWhitespace: Yes

preprocess.R

@@ -0,0 +1,41 @@
+library(dplyr)
+library(duckdbfs)
+library(ggplot2)
+library(mapgl)
+library(glue)
+load_h3()
+load_spatial()
+
+duckdbfs::duckdb_secrets()
+inat = open_dataset("s3://public-inat/rangemaps")
+
+# Access SVI
+#svi = open_dataset("https://minio.carlboettiger.info/public-social-vulnerability/2022/SVI2022_US_tract.parquet")
+tracts = open_dataset("https://minio.carlboettiger.info/public-social-vulnerability/2022-tracts-h3-z8.parquet") # Access CalEnviroScreen
+# ces = open_dataset("https://minio.carlboettiger.info/public-calenviroscreen/ces_2021.parquet", format="parquet")
+
+# Filter GBIF to our area-of-interest (h-index) and species of interest
+
+ca = tracts |>
+  filter(STATE == "California") |>
+  mutate(h3 = h3_cell_to_parent(h8, 3L)) |>
+  mutate(h3 = tolower(as.character(h3)))
+
+ca |> inner_join(inat, by = "h3") |> count(STATE, COUNTY, FIPS, h8)
+
+
+bird_counts = sf_birds |>
+  count(FIPS, geom) |>
+  mutate(richness = n / {total})
+
+ces_poverty = ces |> select("Poverty", "FIPS")
+combined <- svi |>
+  select("RPL_THEMES", "FIPS") |> filter(RPL_THEMES > 0) |>
+  inner_join(bird_counts, "FIPS")  |>
+  inner_join(ces_poverty, "FIPS") |>
+  mutate(svi_bin = cut(RPL_THEMES, breaks = c(0, .25, .50, .75, 1), 
+                           labels = c("Q1", "Q2", "Q3", "Q4"))) |>
+  mutate(poverty_bin = cut(Poverty, breaks = c(0, 25, 50, 75, 100), 
+                           labels = c("0-25", "25-50", "50-75", "75-100")))
+ 
+  

test-app.R

@@ -0,0 +1,58 @@
+library(shiny)
+library(mapgl)
+
+source("utils.R")
+source("inat-ranges.R")
+
+load_h3()
+load_spatial()
+
+duckdbfs::duckdb_secrets()
+inat <- open_dataset("s3://public-inat/hex")
+
+# dummy case
+aoi <- spData::us_states
+
+taxa <- open_dataset("https://minio.carlboettiger.info/public-inat/taxonomy/taxa.csv", format = "csv", recursive = FALSE)
+
+
+# publish richness at the aoi (bbox or poly)
+m = maplibre(center = c(-110.5, 37), zoom = 3)
+
+
+ui <- fluidPage(
+    maplibreOutput("map"),
+    actionButton("get_features", "Get Drawn Features"),
+    verbatimTextOutput("feature_output")
+)
+
+server <- function(input, output, session) {
+    output$map <- renderMaplibre({
+        m
+    })
+
+    output$feature_output <- renderPrint({
+            print(input$map_bbox)
+    })
+
+
+    observeEvent(input$map_bbox, {
+
+        bbox =sf::st_bbox(unlist(input$map_bbox), crs = 4326)
+        print(bbox)
+
+        output$map <- renderMaplibre({
+            richness(inat, bbox)
+            richness_map(m)
+     })
+    })
+
+#    observeEvent(input$get_features, {
+#        drawn_features <- get_drawn_features(mapboxgl_proxy("map"))
+#        output$feature_output <- renderPrint({
+#            print(drawn_features)
+#        })
+#    })
+}
+
+shinyApp(ui, server)

test.R

@@ -1,69 +1,103 @@
 ## Illustrate/test core app functionality without shiny
-
-library(tidyverse)
+library(dplyr)
 library(duckdbfs)
+library(ggplot2)
 library(mapgl)
-library(ellmer)
 library(glue)
+load_h3()
+load_spatial()
+
+source("utils.R")
+source("inat-ranges.R")
+
+
+duckdbfs::duckdb_secrets()
+inat <- open_dataset("s3://public-inat/hex")
+
+aoi <- spData::us_states
+
+taxa <- open_dataset("https://minio.carlboettiger.info/public-inat/taxonomy/taxa.csv", format = "csv", recursive = FALSE)
+
+# publish richness at the aoi (bbox or poly)
+richness(inat, aoi)
+richness_map()
+
+
+
+## UGH can't deal with antimeridian
+# dropme <- antimeridian_hexes(3)
+# dropme <- antimeridian_hexes(4) |> rename(h4 = h3id)
+# inat |> anti_join(dropme) |> write_dataset("s3://public-inat/ranges.parquet")
+# inat <- open_dataset("s3://public-inat/ranges.parquet", recursive = FALSE)
+
+# mutate(h3 = h3_cell_to_parent(h4, 3L))
+
+
+
+m <- maplibre(center = c(-110.5, 34.8), zoom = 4) |> add_draw_control() 
+richness_map(m, "https://minio.carlboettiger.info/public-data/inat-tmp-ranges.h3j")
+
+
+
+
+
+
+
+
+
+
+
+library(htmlwidgets)
+htmlwidgets::saveWidget(m, "example.html")
+
+
+
+
+
+
+
+
+
+
+
+
+amphib = open_dataset("s3://public-inat/polygon/Amphibia.parquet", recursive = FALSE)
+
+gdf <- amphib |> 
+filter(name == "Ambystoma californiense") |>
+ to_sf(crs=4326)
+
+maplibre(center = c(-122.5, 37.8), zoom = 4) |>
+      add_source(id = "gdf", gdf) |>
+      add_layer("gdf-layer",
+                type = "fill",
+                source = "gdf",
+                paint = list(
+                  "fill-color" =  "darkgreen",
+                  "fill-opacity" = .9
+                )
+        )
+      
+
+
+
+
+# Access SVI
+#svi = open_dataset("https://minio.carlboettiger.info/public-social-vulnerability/2022/SVI2022_US_tract.parquet")
+#tracts = open_dataset("https://minio.carlboettiger.info/public-social-vulnerability/2022-tracts-h3-z5.parquet") # Access CalEnviroScreen
+# ces = open_dataset("https://minio.carlboettiger.info/public-calenviroscreen/ces_2021.parquet", format="parquet")
+
+# Filter GBIF to our area-of-interest (h-index) and species of interest
+
+ca <- tracts |>
+  filter(STATE == "California") |>
+  mutate(h4 = h3_cell_to_parent(h5, 4L)) |>
+  mutate(h4 = tolower(as.character(h4)))
+
+out <- ca |>
+ inner_join(inat, by = "h4") |>
+ count(STATE, COUNTY, FIPS, h5)
+
 
-repo <- "https://data.source.coop/cboettig/social-vulnerability"
-pmtiles <- glue("{repo}/svi2020_us_tract.pmtiles")
-parquet <- glue("{repo}/svi2020_us_tract.parquet")
-svi <- open_dataset(parquet, tblname = "svi") |> filter(RPL_THEMES > 0)
-
-schema <- read_file("schema.yml")
-system_prompt <- glue::glue(readr::read_file("system-prompt.md"),
-                            .open = "<", .close = ">")
-
-
-
-# Or optionally test with cirrus
-chat <- ellmer::chat_vllm(
-  base_url = "https://llm.cirrus.carlboettiger.info/v1/",
-  model = "kosbu/Llama-3.3-70B-Instruct-AWQ",
-  api_key = Sys.getenv("CIRRUS_LLM_KEY"),
-  system_prompt = system_prompt,
-  api_args = list(temperature = 0)
-)
-
-# or use the NRP model
-chat <- ellmer::chat_vllm(
-  base_url = "https://llm.nrp-nautilus.io/",
-  model = "llama3",
-  api_key = Sys.getenv("NRP_API_KEY"),
-  system_prompt = system_prompt,
-  api_args = list(temperature = 0)
-)
-
-
-# Test a chat-based response
-chat$chat("Which columns describes racial components of social vulnerability?")
-## A query-based response
-stream <- chat$chat("Which counties in California have the highest average social vulnerability?")
-response <- jsonlite::fromJSON(stream)
-
-con <- duckdbfs::cached_connection()
-filtered_data <- DBI::dbGetQuery(con, response$query)
-
-filter_column <- function(full_data, filtered_data, id_col) {
-  if (nrow(filtered_data) < 1) return(NULL)
-  values <- full_data |>
-    inner_join(filtered_data, copy = TRUE) |>
-    pull(id_col)
-  # maplibre syntax for the filter of PMTiles  
-  list("in", list("get", id_col), list("literal", values))
-}
-
-maplibre(center = c(-102.9, 41.3), zoom = 3) |>
-    add_fill_layer(
-        id = "svi_layer",
-        source = list(type = "vector", url  = paste0("pmtiles://", pmtiles)),
-        source_layer = "SVI2000_US_tract",
-        filter = filter_column(full_data, filtered_data, "FIPS"),
-        fill_opacity = 0.5,
-        fill_color = interpolate(column = "RPL_THEMES",
-                                values = c(0, 1),
-                                stops = c("#e19292c0", "darkblue"),
-                                na_color = "lightgrey")
-    )
+#  mutate(height = n / max(n)) |>
 

utils.R

@@ -1,4 +1,130 @@
 
-library(tidyverse)
+library(dplyr)
 library(duckdbfs)
+library(sf)
 
+as_dataset.sf <- function(sf, ...) {
+  # cludgy way to get polygon into duckdb as spatial data
+  tmp <- tempfile(fileext = ".fgb")
+  sf |> sf::st_transform(4326) |> sf::write_sf(tmp, append = FALSE, quiet = TRUE)
+  aoi <- duckdbfs::open_dataset(tmp, ...)
+
+  aoi
+}
+
+# promote bbox to polygon
+as_poly <- function(aoi) {
+
+  crs <- st_crs(aoi)
+  if (crs$input != "EPSG:4326" ) {
+    aoi <- aoi |> st_transform(4326)
+
+  } 
+  if (inherits(aoi, "bbox")) {
+    aoi <- aoi |> 
+      st_as_sfc() |> 
+      st_as_sf() |> 
+      rename(geom = x)
+  }
+aoi
+}
+
+
+
+get_h3_aoi <- function(aoi, zoom = 0L, precision = 6L, upper = FALSE) {
+  duckdbfs::load_h3()
+
+  zoom <- as.integer(zoom)
+  # consider auto-retry at higher precision if subset is empty.
+  precision <- as.integer(precision)
+  res <- paste0("h", precision)
+
+  if(inherits(aoi, "sf") || inherits(aoi, "bbox")) {
+    aoi <- as_poly(aoi)
+    aoi <- as_dataset.sf(aoi)
+  }
+
+  # multipolygon dump may not be needed for draw tools.
+  h3_aoi <- aoi |>
+    dplyr::mutate(poly = array_extract(unnest(st_dump(geom)),"geom"),
+          h3id = h3_polygon_wkt_to_cells(poly,{precision}),
+          h3id = unnest(h3id)
+          ) |>
+    dplyr::mutate(h0 = h3_h3_to_string( h3_cell_to_parent(h3id, {zoom})),
+           h3id = h3_h3_to_string (h3id) )
+
+  if(upper) {
+    h3_aoi <- h3_aoi |> 
+    dplyr::mutate(h0 = toupper(h0), h3id = toupper(h3id))
+  }
+
+  h3_aoi |>
+    dplyr::select(h0, h3id) |>
+    duckdbfs::as_view("h3_aoi")
+}
+
+hex_res <- function(x) {
+    x |>
+    utils::head(1) |>
+    dplyr::mutate(res = h3_get_resolution(h3id)) |>
+    dplyr::pull(res)
+}
+
+hex_join <- function(x,y) {
+  res_x <- hex_res(x)
+  res_y <- hex_res(y)
+
+  if (res_x > res_y) {
+    y <- y |> 
+      dplyr::mutate(h3id = unnest(h3_cell_to_children(h3id, {res_x})),
+                    h3id = toupper(h3id))
+  }
+    if (res_x < res_y) {
+    y <- y |> 
+      dplyr::mutate(h3id = h3_cell_to_parent(h3id, {res_x}))
+  }
+
+  dplyr::inner_join(x, y)
+}
+
+
+antimeridian_hexes <- function(zoom = 4, con = duckdbfs::cached_connection()) {
+  duckdbfs::load_h3(con)
+  duckdbfs::load_spatial(con)
+  DBI::dbExecute(con, 
+  "
+  CREATE OR REPLACE TABLE antimeridian AS (
+    SELECT ST_GeomFromText(
+    'POLYGON ((170 85, 190 85, 190 -85, 170 -85, 170 85))'
+    ) AS geometry
+  )
+  ")
+  zoom <- as.integer(zoom)
+  
+  am <- 
+    dplyr::tbl(con, "antimeridian") |>
+    dplyr::mutate(h3id = unnest(
+      h3_polygon_wkt_to_cells_string(geometry, {zoom})
+      )) |>
+    dplyr::select(h3id)
+
+am
+}
+
+
+viridis_pal <- 
+  function(column = "height",
+           n = 61, 
+           min_v = 0, 
+           max_v = 1) {
+  pal <- viridisLite::viridis(n)
+  fill_color = mapgl::step_expr(
+      column = column,
+      base = pal[1],
+      stops = pal[2:n],
+      values = seq(min_v, max_v, length.out = n-1),
+      na_color = "white"
+    )
+  
+  fill_color
+}