Author: Gilles Colling
Date: 2026-02-28

# Quick Start

#### Gilles Colling

#### 2026-02-28

# Spatial Analysis Done Right

You want to do spatial statistics, and it involves binning points into grid cells.

The problem with rectangular grids: A rectangular lat-lon grid introduces severe distortions. At the equator, a 1° cell covers ~12,300 km². Near the poles, the same 1° cell covers a tiny fraction of that area. This breaks any analysis that assumes equal sampling effort or comparable cell sizes.

The solution: Discrete global grids partition Earth’s surface into cells of equal area, regardless of latitude. hexify implements the ISEA (Icosahedral Snyder Equal Area) projection, providing hexagonal cells that are all the same size from the equator to the Arctic.

## Basic Usage

    # Sample data: European cities
    cities <- data.frame(
      name = c("Vienna", "Paris", "Madrid", "Berlin", "Rome"),
      lon = c(16.37, 2.35, -3.70, 13.40, 12.50),
      lat = c(48.21, 48.86, 40.42, 52.52, 41.90)
    )

    # Create a grid specification
    grid <- hex_grid(area_km2 = 10000)
    grid
    #> HexGridInfo Specification
    #> -------------------------
    #> Aperture:    3
    #> Resolution:  8
    #> Area:        7773.97 km^2
    #> Diagonal:    94.74 km
    #> CRS:         EPSG:4326
    #> Total Cells: 65612

    # Assign cities to hexagonal cells
    result <- hexify(cities, lon = "lon", lat = "lat", grid = grid)
    result
    #> HexData Object
    #> --------------
    #> Rows:    5
    #> Columns: 3
    #> Cells:   5 unique
    #> Type:    data.frame
    #>
    #> Grid:
    #>   Aperture 3, Resolution 8 (~7774.0 km^2)
    #>
    #> Columns: name, lon, lat
    #>
    #> Data preview (with cell assignments):
    #>    name   lon   lat cell_id
    #>  Vienna 16.37 48.21   14092
    #>   Paris  2.35 48.86   13272
    #>  Madrid -3.70 40.42   13260
    #> ... with 2 more rows

### Accessing HexData

    # Get the grid specification
    grid_info(result)
    #> HexGridInfo Specification
    #> -------------------------
    #> Aperture:    3
    #> Resolution:  8
    #> Area:        7773.97 km^2
    #> Diagonal:    94.74 km
    #> CRS:         EPSG:4326
    #> Total Cells: 65612

    # Get unique cell IDs
    cells(result)
    #> [1] 14092 13272 13260 13688 14247

    # Count unique cells
    n_cells(result)
    #> [1] 5

    # Access all cell IDs (one per row)
    result@cell_id
    #> [1] 14092 13272 13260 13688 14247

    # Access cell centers
    head(result@cell_center)
    #>            lon      lat
    #> [1,] 15.968349 48.25028
    #> [2,]  2.460284 48.49334
    #> [3,] -3.482737 40.05509
    #> [4,] 13.428088 52.18073
    #> [5,] 12.466432 41.61442

    # Extract original data as data.frame
    head(as.data.frame(result))
    #>     name   lon   lat cell_id cell_cen_lon cell_cen_lat cell_area_km2
    #> 1 Vienna 16.37 48.21   14092    15.968349     48.25028      7773.969
    #> 2  Paris  2.35 48.86   13272     2.460284     48.49334      7773.969
    #> 3 Madrid -3.70 40.42   13260    -3.482737     40.05509      7773.969
    #> 4 Berlin 13.40 52.52   13688    13.428088     52.18073      7773.969
    #> 5   Rome 12.50 41.90   14247    12.466432     41.61442      7773.969
    #>   cell_diag_km
    #> 1     94.74495
    #> 2     94.74495
    #> 3     94.74495
    #> 4     94.74495
    #> 5     94.74495

### With sf Objects

    library(sf)

    # Create sf object (any CRS works - hexify transforms automatically)
    pts <- st_as_sf(cities, coords = c("lon", "lat"), crs = 4326)

    # hexify handles CRS transformation automatically
    result_sf <- hexify(pts, area_km2 = 10000)
    class(result_sf)
    #> [1] "HexData"
    #> attr(,"package")
    #> [1] "hexify"

## Real-World Example: Species Occurrence Data

This example demonstrates the typical workflow: loading point data, assigning to grid cells, aggregating, and visualizing.

    library(sf)
    library(ggplot2)

    # Simulate bird observation data (reduced for faster vignette build)
    set.seed(123)
    n_obs <- 500

    # Generate observations with realistic spatial clustering
    birds <- data.frame(
      lon = c(
        rnorm(150, mean = 10, sd = 15),    # Western Europe
        rnorm(100, mean = 25, sd = 10),    # Eastern Europe
        rnorm(100, mean = 20, sd = 20),    # Mediterranean
        rnorm(80, mean = 0, sd = 15),      # West Africa
        rnorm(70, mean = 35, sd = 10)      # East Africa
      ),
      lat = c(
        rnorm(150, mean = 50, sd = 8),     # Western Europe
        rnorm(100, mean = 55, sd = 6),     # Eastern Europe
        rnorm(100, mean = 42, sd = 5),     # Mediterranean
        rnorm(80, mean = 10, sd = 10),     # West Africa
        rnorm(70, mean = -5, sd = 12)      # East Africa
      ),
      species = sample(c("Passer domesticus", "Turdus merula", "Parus major",
                         "Columba palumbus", "Sturnus vulgaris"), n_obs, replace = TRUE)
    )

    # Clip to valid ranges
    birds$lon <- pmax(-30, pmin(60, birds$lon))
    birds$lat <- pmax(-35, pmin(70, birds$lat))

### Assign Points to Grid Cells

    # Create grid and assign observations (coarser grid for faster build)
    grid <- hex_grid(area_km2 = 50000)
    birds_hex <- hexify(birds, lon = "lon", lat = "lat", grid = grid)

    # Extract data with cell IDs
    birds_gridded <- as.data.frame(birds_hex)
    birds_gridded$cell_id <- birds_hex@cell_id

    # Count observations per cell
    obs_counts <- aggregate(
      species ~ cell_id,
      data = birds_gridded,
      FUN = length
    )
    names(obs_counts)[2] <- "n_observations"

    # Species richness per cell
    richness <- aggregate(
      species ~ cell_id,
      data = birds_gridded,
      FUN = function(x) length(unique(x))
    )
    names(richness)[2] <- "n_species"

    obs_counts <- merge(obs_counts, richness, by = "cell_id")
    head(obs_counts)
    #>   cell_id n_observations n_species
    #> 1       1              3         2
    #> 2      25              2         2
    #> 3      54              1         1
    #> 4     108              1         1
    #> 5     109              2         2
    #> 6     163              1         1

### Generate Cell Polygons and Visualize

    # Generate polygons for cells with data
    cell_polys <- cell_to_sf(obs_counts$cell_id, grid)
    cell_polys <- merge(cell_polys, obs_counts, by = "cell_id")

    # Get relevant countries
    region <- hexify_world[hexify_world$continent %in% c("Europe", "Africa"), ]

    ggplot() +
      geom_sf(data = region, fill = "gray95", color = "gray70", linewidth = 0.2) +
      geom_sf(data = cell_polys, aes(fill = n_observations), color = "white", linewidth = 0.3) +
      scale_fill_viridis_c(option = "plasma", name = "Observations", trans = "sqrt") +
      coord_sf(xlim = c(-30, 60), ylim = c(-35, 70)) +
      labs(
        title = "Bird Observations in Equal-Area Hexagonal Cells",
        subtitle = sprintf("ISEA3H grid at resolution %d (~%.0f km² cells)",
                           grid@resolution, grid@area_km2)
      ) +
      theme_minimal() +
      theme(
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_line(color = "gray90")
      )

### Species Richness Map

    ggplot() +
      geom_sf(data = region, fill = "gray95", color = "gray70", linewidth = 0.2) +
      geom_sf(data = cell_polys, aes(fill = n_species), color = "white", linewidth = 0.3) +
      scale_fill_viridis_c(option = "mako", name = "Species\nRichness", direction = -1) +
      coord_sf(xlim = c(-30, 60), ylim = c(-35, 70)) +
      labs(
        title = "Species Richness per Grid Cell",
        subtitle = "Number of unique species observed in each equal-area cell"
      ) +
      theme_minimal() +
      theme(
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_line(color = "gray90")
      )

## Core Concepts

hexify uses two S4 classes to make spatial workflows clean and error-free:

- `HexGridInfo`: A grid specification that stores all parameters (aperture, resolution, area). Define once, reuse everywhere.

- `HexData`: Your data + the grid that was used. Carries the grid reference so downstream operations “just work.”

### HexGridInfo Slots

Slot | Type | Description
--- | --- | ---
`aperture` | character | Grid aperture (“3”, “4”, “7”, or “4/3”)
`resolution` | integer | Resolution level (0-30)
`area_km2` | numeric | Cell area in km²
`diagonal_km` | numeric | Cell diagonal in km
`crs` | integer | Coordinate reference system (EPSG code)

### HexData Slots

Slot | Type | Description
--- | --- | ---
`data` | data.frame/sf | Your original data (unchanged)
`grid` | HexGridInfo | The grid specification used
`cell_id` | numeric | Cell ID for each row
`cell_center` | matrix | Cell center coordinates (lon, lat)

## Caveats

### Pentagon Cells

At every resolution, the ISEA grid contains 12 pentagonal cells with area 5/6 that of hexagons. These are located at the icosahedron vertices (poles + 10 evenly-spaced low-latitude points).

For most analyses, pentagons are not a concern:

1. They’re a tiny minority (12 out of millions of cells at high resolutions)

2. They’re in predictable locations

3. The area difference (5/6) is small and can be corrected if needed

### Multi-Scale Analysis

Hexagonal grids do not nest perfectly—cells at one resolution partially overlap cells at other resolutions. For hierarchical analysis, use grid-based aggregation rather than spatial containment.

### Integer Limits

Cell IDs are stored as integers. For resolutions above 15 (aperture 3), cell IDs may exceed R’s integer limit (2^31-1). Use appropriate numeric types for very high resolutions.

## See Also

- `vignette("visualization")` - Plotting options: `plot()`, `hexify_heatmap()`

- `vignette("workflows")` - Grid generation, multi-resolution analysis, spatial joins, choosing resolution

- `vignette("theory")` - Mathematical foundations (ISEA projection, apertures, space-filling curves)