Author: Gilles Colling
Date: 2026-02-28

# H3 Grid Support

#### Gilles Colling

#### 2026-02-28

hexify v0.5.0 adds H3 as a first-class grid type alongside ISEA. Every core function — `hexify()`, `grid_rect()`, `grid_clip()`, `get_parent()`, `get_children()` — works with both grid systems through the same interface. This vignette covers what H3 is, how to use it in hexify, and when to prefer it over ISEA.

## What is H3?

H3 is a hierarchical hexagonal grid system developed by Uber. It partitions Earth’s surface into hexagonal cells at 16 resolutions (0–15), each roughly 7\(\times\) finer than the last. Cell IDs are 64-bit integers encoded as hexadecimal strings (e.g., `"8528342bfffffff"`).

H3 has become an industry standard adopted by the FCC, Foursquare, and numerous geospatial platforms.

Key difference from ISEA: H3 cells are not equal-area. Cell area varies by ~1.6\(\times\) between the largest and smallest hexagons at any given resolution, depending on latitude. For rigorous equal-area analysis, use ISEA. For interoperability with H3 ecosystems, use `type = "h3"`.

## Getting Started

Create an H3 grid by passing `type = "h3"` to `hex_grid()`:

    library(sf)
    #> Linking to GEOS 3.13.1, GDAL 3.11.4, PROJ 9.7.0; sf_use_s2() is TRUE
    library(ggplot2)

    # Create an H3 grid specification
    grid_h3 <- hex_grid(resolution = 5, type = "h3")
    #> H3 cells are not exactly equal-area; area varies ~3-5% by latitude.
    #> This message is displayed once per session.
    grid_h3
    #> HexGridInfo Specification [H3]
    #> -------------------------------
    #> Grid Type:   H3 (Uber)
    #> Resolution:  5
    #> Avg Area:    252.9040 km^2 (varies by location)
    #> Avg Diagonal:17.09 km
    #> CRS:         EPSG:4326
    #> Total Cells: 2016842
    #> Note: H3 cells are NOT exactly equal-area

Then use `hexify()` with the grid object, just like ISEA:

    # Sample cities
    cities <- data.frame(
      name = c("Vienna", "Paris", "Madrid", "Berlin", "Rome",
               "London", "Prague", "Warsaw", "Budapest", "Amsterdam"),
      lon = c(16.37, 2.35, -3.70, 13.40, 12.50,
              -0.12, 14.42, 21.01, 19.04, 4.90),
      lat = c(48.21, 48.86, 40.42, 52.52, 41.90,
              51.51, 50.08, 52.23, 47.50, 52.37)
    )

    result <- hexify(cities, lon = "lon", lat = "lat", grid = grid_h3)
    result
    #> HexData Object
    #> --------------
    #> Rows:    10
    #> Columns: 3
    #> Cells:   10 unique
    #> Type:    data.frame
    #>
    #> Grid:
    #>   H3 Resolution 5 (~252.9040 km^2 avg)
    #>
    #> Columns: name, lon, lat
    #>
    #> Data preview (with cell assignments):
    #>    name   lon   lat         cell_id
    #>  Vienna 16.37 48.21 851e15b7fffffff
    #>   Paris  2.35 48.86 851fb467fffffff
    #>  Madrid -3.70 40.42 85390ca3fffffff
    #> ... with 7 more rows

H3 cell IDs are character strings, unlike ISEA’s numeric IDs:

    # Cell IDs are hexadecimal strings
    result@cell_id
    #>  [1] "851e15b7fffffff" "851fb467fffffff" "85390ca3fffffff" "851f1d4bfffffff"
    #>  [5] "851e8053fffffff" "85194ad3fffffff" "851e3543fffffff" "851f53cbfffffff"
    #>  [9] "851e037bfffffff" "85196953fffffff"

    # All standard accessors work
    cells(result)
    #>  [1] "851e15b7fffffff" "851fb467fffffff" "85390ca3fffffff" "851f1d4bfffffff"
    #>  [5] "851e8053fffffff" "85194ad3fffffff" "851e3543fffffff" "851f53cbfffffff"
    #>  [9] "851e037bfffffff" "85196953fffffff"
    n_cells(result)
    #> [1] 10

### Choosing Resolution by Area

If you think in terms of cell area rather than resolution numbers, pass `area_km2` instead of `resolution`. hexify picks the closest H3 resolution:

    grid_area <- hex_grid(area_km2 = 500, type = "h3")
    #> Warning in hex_grid(area_km2 = 500, type = "h3"): H3 cells are not exactly
    #> equal-area. Closest resolution 5 has average area ~252.904 km^2 (requested
    #> 500.000 km^2)
    grid_area
    #> HexGridInfo Specification [H3]
    #> -------------------------------
    #> Grid Type:   H3 (Uber)
    #> Resolution:  5
    #> Avg Area:    252.9040 km^2 (varies by location)
    #> Avg Diagonal:17.09 km
    #> CRS:         EPSG:4326
    #> Total Cells: 2016842
    #> Note: H3 cells are NOT exactly equal-area

## Grid Generation

All grid generation functions work with H3 grids.

### Rectangular Region

    # Generate H3 hexagons over Western Europe
    grid_h3 <- hex_grid(resolution = 3, type = "h3")
    europe_h3 <- grid_rect(c(-10, 35, 25, 60), grid_h3)

    # Basemap
    europe <- hexify_world[hexify_world$continent == "Europe", ]

    ggplot() +
      geom_sf(data = europe, fill = "gray95", color = "gray60") +
      geom_sf(data = europe_h3, fill = NA, color = "#E6550D", linewidth = 0.4) +
      coord_sf(xlim = c(-10, 25), ylim = c(35, 60)) +
      labs(title = sprintf("H3 Resolution %d Grid (~%.0f km² avg cells)",
                           grid_h3@resolution, grid_h3@area_km2)) +
      theme_minimal()

### Clipping to a Boundary

    # Clip H3 grid to France
    france <- hexify_world[hexify_world$name == "France", ]
    grid_h3 <- hex_grid(resolution = 4, type = "h3")
    france_h3 <- grid_clip(france, grid_h3)
    #> Spherical geometry (s2) switched off
    #> although coordinates are longitude/latitude, st_intersection assumes that they
    #> are planar
    #> Spherical geometry (s2) switched on

    ggplot() +
      geom_sf(data = france, fill = "gray95", color = "gray40", linewidth = 0.5) +
      geom_sf(data = france_h3, fill = alpha("#E6550D", 0.3),
              color = "#E6550D", linewidth = 0.3) +
      coord_sf(xlim = c(-5, 10), ylim = c(41, 52)) +
      labs(title = sprintf("H3 Grid Clipped to France (res %d)", grid_h3@resolution)) +
      theme_minimal()

## Hierarchical Navigation

H3’s killer feature is its clean hierarchical structure: every cell has exactly one parent and seven children. hexify exposes this with `get_parent()` and `get_children()`.

### Parents

    # Get parent cells (one resolution coarser)
    grid_h3 <- hex_grid(resolution = 5, type = "h3")
    child_ids <- lonlat_to_cell(
      lon = c(16.37, 2.35, 13.40),
      lat = c(48.21, 48.86, 52.52),
      grid = grid_h3
    )

    parent_ids <- get_parent(child_ids, grid_h3, levels = 1)
    data.frame(child = child_ids, parent = parent_ids)
    #>             child          parent
    #> 1 851e15b7fffffff 841e15bffffffff
    #> 2 851fb467fffffff 841fb47ffffffff
    #> 3 851f1d4bfffffff 841f1d5ffffffff

### Children

    # Get children of a single cell (one resolution finer)
    grid_coarse <- hex_grid(resolution = 3, type = "h3")
    coarse_id <- lonlat_to_cell(16.37, 48.21, grid_coarse)

    children <- get_children(coarse_id, grid_coarse, levels = 1)
    cat(length(children[[1]]), "children at resolution", grid_coarse@resolution + 1, "\n")
    #> 7 children at resolution 4
    head(children[[1]])
    #> [1] "841e151ffffffff" "841e153ffffffff" "841e155ffffffff" "841e157ffffffff"
    #> [5] "841e159ffffffff" "841e15bffffffff"

### Visualizing the Hierarchy

    # Parent cell polygon
    parent_poly <- cell_to_sf(coarse_id, grid_coarse)

    # Children cell polygons
    grid_fine <- hex_grid(resolution = 4, type = "h3")
    children_poly <- cell_to_sf(children[[1]], grid_fine)

    ggplot() +
      geom_sf(data = children_poly, fill = alpha("#E6550D", 0.3),
              color = "#E6550D", linewidth = 0.5) +
      geom_sf(data = parent_poly, fill = NA, color = "black", linewidth = 1.2) +
      labs(title = sprintf("H3 Hierarchy: 1 parent (res %d) → %d children (res %d)",
                           grid_coarse@resolution,
                           length(children[[1]]),
                           grid_fine@resolution)) +
      theme_minimal()

## Working with H3 Data

The standard hexify workflow applies to H3 grids. Here’s a complete example using simulated species observations:

    set.seed(42)

    # Simulate observations across Europe
    obs <- data.frame(
      lon = c(rnorm(200, 10, 12), rnorm(100, 25, 8)),
      lat = c(rnorm(200, 48, 6), rnorm(100, 55, 4)),
      species = sample(c("Sp. A", "Sp. B", "Sp. C"), 300, replace = TRUE)
    )
    obs$lon <- pmax(-10, pmin(40, obs$lon))
    obs$lat <- pmax(35, pmin(65, obs$lat))

    # Hexify with H3
    grid_h3 <- hex_grid(resolution = 3, type = "h3")
    obs_hex <- hexify(obs, lon = "lon", lat = "lat", grid = grid_h3)

    # Aggregate: species richness per cell
    obs_df <- as.data.frame(obs_hex)
    obs_df$cell_id <- obs_hex@cell_id

    richness <- aggregate(species ~ cell_id, data = obs_df,
                          FUN = function(x) length(unique(x)))
    names(richness)[2] <- "n_species"

    # Map it
    polys <- cell_to_sf(richness$cell_id, grid_h3)
    polys <- merge(polys, richness, by = "cell_id")

    europe <- hexify_world[hexify_world$continent == "Europe", ]

    ggplot() +
      geom_sf(data = europe, fill = "gray95", color = "gray70", linewidth = 0.2) +
      geom_sf(data = polys, aes(fill = n_species), color = "white", linewidth = 0.3) +
      scale_fill_viridis_c(option = "plasma", name = "Species\nRichness") +
      coord_sf(xlim = c(-10, 40), ylim = c(35, 65)) +
      labs(title = "Species Richness on H3 Grid",
           subtitle = sprintf("H3 resolution %d (~%.0f km² avg cells)",
                              grid_h3@resolution, grid_h3@area_km2)) +
      theme_minimal() +
      theme(axis.text = element_blank(), axis.ticks = element_blank())

## ISEA–H3 Crosswalk

hexify v0.6.0 added `h3_crosswalk()` for bidirectional mapping between ISEA and H3 cell IDs. This is useful when you work in ISEA for analysis but need to share results with H3 ecosystems (or vice versa).

    # Start with an ISEA grid and some cells
    grid_isea <- hex_grid(resolution = 9, aperture = 3)
    isea_ids <- lonlat_to_cell(
      lon = c(16.37, 2.35, 13.40, -3.70, 12.50),
      lat = c(48.21, 48.86, 52.52, 40.42, 41.90),
      grid = grid_isea
    )

    # Map ISEA cells to their closest H3 equivalents
    xw <- h3_crosswalk(isea_ids, grid_isea)
    xw[, c("isea_cell_id", "h3_cell_id", "isea_area_km2", "h3_area_km2")]
    #>   isea_cell_id      h3_cell_id isea_area_km2 h3_area_km2
    #> 1        42280 841e15bffffffff      2591.375    1753.173
    #> 2        39597 841fb43ffffffff      2591.375    1569.204
    #> 3        40823 841f1d5ffffffff      2591.375    1570.136
    #> 4        39585 84390cbffffffff      2591.375    1819.493
    #> 5        42516 841e80dffffffff      2591.375    1885.191

The `area_ratio` column shows how ISEA and H3 cell sizes compare — values close to 1 mean the resolutions are well-matched.

## ISEA vs H3: When to Use Which

| ISEA | H3
--- | --- | ---
Cell area | Exactly equal | ~1.6\(\times\) variation
Cell IDs | Numeric (integer) | Character (hex string)
Apertures | 3, 4, 7, 4/3 | Fixed (7)
Resolutions | 0–30 | 0–15
Hierarchy | Approximate (aperture-dependent) | Exact (7 children per parent)
Dependencies | None (built-in C++) | None (vendored H3 C library)
Industry adoption | Scientific / government | Tech industry / commercial

Use ISEA when:

- Equal-area cells are required (biodiversity surveys, density estimation, statistical sampling)

- You need fine control over aperture and resolution

- No external dependencies are acceptable

Use H3 when:

- Interoperability with H3 ecosystems (Uber, Foursquare, DuckDB, BigQuery)

- Clean hierarchical operations (parent/child traversal) are a priority

- Slight area variation across latitudes is acceptable for your analysis

## Resolution Reference

    h3_res <- hexify_compare_resolutions(type = "h3", res_range = 0:15)
    h3_res$n_cells_fmt <- ifelse(
      h3_res$n_cells > 1e9,
      sprintf("%.1fB", h3_res$n_cells / 1e9),
      ifelse(h3_res$n_cells > 1e6,
             sprintf("%.1fM", h3_res$n_cells / 1e6),
             ifelse(h3_res$n_cells > 1e3,
                    sprintf("%.1fK", h3_res$n_cells / 1e3),
                    as.character(h3_res$n_cells)))
    )
    knitr::kable(
      h3_res[, c("resolution", "n_cells_fmt", "cell_area_km2", "cell_spacing_km")],
      col.names = c("Resolution", "# Cells", "Avg Area (km²)", "Spacing (km)"),
      digits = 1
    )

Resolution | # Cells | Avg Area (km²) | Spacing (km)
--- | --- | --- | ---
0 | 122 | 4357449.4 | 2243.1
1 | 842 | 609788.4 | 839.1
2 | 5.9K | 86801.8 | 316.6
3 | 41.2K | 12393.4 | 119.6
4 | 288.1K | 1770.3 | 45.2
5 | 2.0M | 252.9 | 17.1
6 | 14.1M | 36.1 | 6.5
7 | 98.8M | 5.2 | 2.4
8 | 691.8M | 0.7 | 0.9
9 | 4.8B | 0.1 | 0.3
10 | 33.9B | 0.0 | 0.1
11 | 237.3B | 0.0 | 0.0
12 | 1661.0B | 0.0 | 0.0
13 | 11626.7B | 0.0 | 0.0
14 | 81386.8B | 0.0 | 0.0
15 | 569707.4B | 0.0 | 0.0

Areas are averages — actual cell area varies by latitude.

## See Also

- `vignette("quickstart")` - Getting started with hexify (ISEA-focused)

- `vignette("workflows")` - Grid generation, multi-resolution analysis, GIS export

- `vignette("visualization")` - Plotting with `plot()`, `hexify_heatmap()`

- `vignette("theory")` - Mathematical foundations of the ISEA projection