H3 Grid Support

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.