Getting Started with geomattR

In geomattR: Calculate Geometric Attributes of Spatial Polygons

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

Introduction

geomattR provides tools to compute geometric attributes of spatial polygons. By default, core spatial calculations use geodesic core measurements (method "geo", which is recommended), but they also support "haversine" and "cosine" methods. For studies covering large countries, continents or the world, geodesic calculations on unprojected polygons (WGS84) improve accuracy and minimize shape distortion.

Installation

Install geomattR from GitHub:

# Install if you haven't already
# install.packages("devtools")
# devtools::install_github("gortegasolis/geomattR")

library(geomattR)
library(terra)

Quick Start

The main function is calculate_geometric_attributes(), which takes a SpatVector of polygons and returns the same object with added geometric attribute columns:

# Create a simple polygon (1x1 degree square at equator)
coords <- cbind(c(0, 0, 1, 1, 0), c(0, 1, 1, 0, 0))
polygon <- vect(coords, type = "polygon", crs = "EPSG:4326")

# Calculate all available metrics
result <- calculate_geometric_attributes(polygon)

# View the results
print(result)

# Or view specific columns
print(result[, c("area", "perimeter", "compactness")])

Available Metrics

GeomattR calculates 20 different geometric metrics organized into several categories:

Size Metrics

# Calculate area and perimeter
polygon_with_size <- calculate_geometric_attributes(
  polygon,
  metrics = c("area", "perimeter", "hole_area", "hole_area_pct")
)

# Area in square meters (using geodesic calculations)
print(polygon_with_size$area)

# Perimeter in meters
print(polygon_with_size$perimeter)

Shape Metrics

# Calculate shape characteristics
polygon_with_shape <- calculate_geometric_attributes(
  polygon,
  metrics = c(
    "compactness", # Polsby-Popper (0-1, 1=perfect circle)
    "reock", # Reock compactness
    "shape_index", # Dimensionless shape complexity
    "circularity_ratio", # Based on max length
    "fractaldimension", # Boundary complexity
    "elongation_rectangle" # mean(long sides) / mean(short sides) from the minimum bounding rectangle
  )
)

# Perfect circle has compactness = 1
# Irregular shapes have lower values
print(polygon_with_shape[, c("compactness", "shape_index")])

Orientation Metrics

# Calculate orientation
polygon_with_orient <- calculate_geometric_attributes(
  polygon,
  metrics = c("bearing", "northerness", "ew_length", "ns_length", "maxlength")
)

# Bearing: axial orientation of the maximum length line, always measured
# south to north, so it lies within [-90, 90] (0 = N-S axis, -90/90 = E-W axis)
print(polygon_with_orient$bearing)

# Northerness: cosine of bearing (0 = E-W axis, 1 = N-S axis)
print(polygon_with_orient$northerness)

# Extents in meters: maximum distances from East to West and North to South
print(paste("EW Extent:", round(polygon_with_orient$ew_length, 0), "m"))
print(paste("NS Extent:", round(polygon_with_orient$ns_length, 0), "m"))

Complexity Metrics

polygon_with_complexity <- calculate_geometric_attributes(
  polygon,
  metrics = c("sinuosity", "fractaldimension")
)

# Sinuosity: perimeter / max_length (>1 indicates rougher boundary)
print(polygon_with_complexity$sinuosity)

# Fractal dimension (FRAGSTATS convention): boundary complexity, approaching
# 1 for simple square-like shapes and 2 for highly convoluted boundaries
print(polygon_with_complexity$fractaldimension)

Geometry Metrics

# Get centroid coordinates and structure info
polygon_with_geom <- calculate_geometric_attributes(
  polygon,
  metrics = c(
    "decimallongitude",
    "decimallatitude",
    "num_holes",
    "num_polygons"
  )
)

print(polygon_with_geom[, c("decimallongitude", "decimallatitude")])

Working with Multiple Polygons

For multiple polygons, calculate_geometric_attributes() processes them sequentially:

# Create multiple polygons
coords1 <- cbind(c(0, 0, 1, 1, 0), c(0, 1, 1, 0, 0))
coords2 <- cbind(c(1, 1, 2, 2, 1), c(1, 2, 2, 1, 1))

poly1 <- vect(coords1, type = "polygon", crs = "EPSG:4326")
poly2 <- vect(coords2, type = "polygon", crs = "EPSG:4326")

# Combine into single SpatVector
polygons <- rbind(poly1, poly2)

# Calculate metrics for all polygons at once
results <- calculate_geometric_attributes(
  polygons,
  metrics = c("area", "perimeter", "compactness")
)

# Results have same row count as input
print(nrow(results)) # 2

# All metrics calculated
print(results[, c("area", "perimeter", "compactness")])

Parallel Processing

For large datasets with many polygons, use the cl argument in calculate_geometric_attributes() to run calculations in parallel:

library(parallel)

# Create multiple polygons
polygons <- rbind(poly1, poly2) # From previous example

# Set up cluster (two workers here; adjust to your machine's resources)
cl <- makeCluster(2)

# Process in parallel using the main function
results <- calculate_geometric_attributes(
  polygons,
  metrics = "all",
  cl = cl
)

# Clean up
stopCluster(cl)

# Results should be equivalent to sequential processing
# (allowing for tiny floating-point differences)
print(head(results))

Handling Geographic vs. Projected CRS

GeomattR automatically handles different coordinate systems. By default, core spatial calculations prioritize geodesic core measurements (method "geo", which is recommended). However, for distance-based calculations, the helper functions (such as calc_extent() and calc_elongation()) accept a method parameter that can be changed to "haversine" or "cosine". For details on these distance methods and how they behave, refer to the method descriptions in the terra package.

Automatic CRS conversion handles the execution details:

# Create polygon in geographic CRS (WGS84/EPSG:4326)
poly_geographic <- vect(coords1, type = "polygon", crs = "EPSG:4326")

# Create same polygon in projected CRS (UTM Zone 30N)
poly_projected <- project(poly_geographic, "EPSG:32630")

# Calculate metrics on both
result_geo <- calculate_geometric_attributes(poly_geographic, metrics = "area")
result_proj <- calculate_geometric_attributes(poly_projected, metrics = "area")

# Both should give similar results (using geodesic calculations by default)
print(paste("Geographic CRS area:", round(result_geo$area, 0), "m²"))
print(paste("Projected CRS area:", round(result_proj$area, 0), "m²"))

# Difference should be minimal due to default geodesic calculations
print(paste(
  "Difference:",
  round(abs(result_geo$area - result_proj$area), 0),
  "m²"
))

See Also

• ?calculate_geometric_attributes - Main function documentation
• ?get_distant_points - Finding extreme points on polygons
• terra documentation - Spatial data handling
• R for Data Science - Data analysis workflow

Further Reading

• Polsby, D. D., & Popper, F. J. (1991). The third annual report of the political geography specialty group.
• Reock Jr, E. C. (1961). A note: Measuring compactness as a requirement for legislative districts.
• Mandelbrot, B. B. (1967). How long is the coast of Britain?

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For more information and updates, visit the geomattR GitHub repository.

geomattR documentation built on Oct. 18, 2026, 5:08 p.m.