terra::rasterize() Issues: Why Are Polygon Values Missing?

• 🧭 Misaligned CRS or extent nearly always causes terra rasterize() to produce NA outputs.
• 🧱 Large raster cells can miss small polygons entirely, leaving blank raster results.
• ⚙️ Not specifying a field or using the wrong aggregation function can silently fail the process.
• 🧪 Visualizing with plot() and testing with single polygons helps isolate errors early.
• 🚀 Simplifying geometries and writing to disk vastly improve performance with large datasets.

Overview: Why Polygon Values Go Missing in terra::rasterize() in R

Turning polygons into raster format with terra::rasterize() in R is a key step in many spatial jobs. It helps with analysis, modeling, and combining data in fields like environmental science, city planning, and resource management. But if your output has only NA values or looks unfinished, the problem usually comes from wrong settings or data that don't match. This guide shows you each problem that can come up and how to find and fix common issues when rasterizing in R using the terra package.

Understanding How terra::rasterize() Works

The terra::rasterize() function in R takes a polygon layer and turns it into a raster grid. It does this by giving values to cells based on a chosen attribute. The main job of this function is to change polygons into rasters. This is important for putting vector data into raster-based models or display processes.

Function Syntax:

rasterize(x, y, field=NULL, fun=NULL)

• x: A SpatVector object (usually polygons).
• y: A SpatRaster template—this grid sets the resolution, extent, and CRS.
• field: The attribute from the vector that gives cells their values.
• fun: A function used when many vector features cross over a raster cell (for example, “sum”, “first”, “mean”).

If your raster result is blank or full of NAs, the common reasons are in how you set these parameters—especially y, field, and fun.

Raster Template Alignment: A Hidden Reason For Missing Data

A poorly set up raster template (y) is one of the most common causes of rasterization problems. Keep in mind that terra::rasterize() doesn’t change your raster to fit the polygon on its own. It will only show values where there is overlap.

Common Alignment Issues:

• ❌ Raster extent does not cover the entire polygon layer
• ❌ Resolution is too rough to show small features
• ❌ Coordinate Reference Systems (CRS) are not lined up

To stop these issues, create the raster template right from the polygon layer:

r <- rast(ext(poly), resolution=100, crs=crs(poly))

This makes sure:

1. The extent is just big enough to hold all polygons.
2. The resolution fits the size of the polygon features.
3. The raster and vector have the same CRS.

Skipping this step is a quick way to get a raster full of NA values.

CRS Mismatch: An Invisible But Important Error

Coordinate Reference System mismatches between the vector (SpatVector) and raster (SpatRaster) layers won't cause errors, but they will lead to empty outputs.

Example:

• Polygon in EPSG:4326 (WGS 84)
• Raster in EPSG:32633 (UTM Zone 33N)

They might look fine when plotted, but on the inside, cells don't match features. Always check:

crs(poly)
crs(r_template)

Use this method to line them up:

if (!compareGeom(poly, r_template, stopOnError=FALSE)) {
    poly <- project(poly, crs(r_template))
}

Not projecting your vector data to match the raster template will definitely cause problems in R rasterization.

Choosing and Correctly Setting the field

The field argument tells which attribute from your polygon should be written to the raster. Leaving it out or pointing to a missing column can lead to empty rasters.

Steps to Stop This:

1. Look at available fields:

   names(poly)
   

2. Set an existing attribute:

   r <- rasterize(poly, r_template, field="zone_id")
   

Some processes skip field, thinking cell values will default to 1. But this only happens with certain fun settings, like length() or sum(). Be clear with both field and fun to prevent unexpected results.

The Power and Problems of the fun Parameter

When many polygons overlap in the same raster cell, the fun parameter decides how to combine their values. If you don't set it and overlap happens, what occurs is hard to guess—sometimes cells become NA.

Common Functions for Combining Values:

• "first": Takes the first value, good for group data
• "last": Takes the last value found
• "mean": Averages values (for number fields)
• "sum": Adds up values from polygons
• "max" / "min": Highest or lowest value when many polygons cover a cell

Here's how to use it:

r <- rasterize(poly, r_template, field="land_value", fun="mean")

Using the wrong kind of function for your attribute (for example, "mean" on text fields) causes errors or missing data.

Resolution, Scale, and Finding Small Polygons

Even layers that are perfectly lined up will give back empty rasters if your cell size is too big. Raster cells that cover kilometers cannot find small features like tiny conservation plots or building outlines. They just "skip" them.

Example:

• Raster resolution: 1000 meters
• Polygon area: 200 square meters
• Result: Cell does not cross polygon geometry much

Fix:

Make the resolution lower to get more detail:

r <- rast(ext(poly), resolution=10, crs=crs(poly))

Having a good balance is important—smaller cells mean more detail but also use more memory.

Crop, Mask, and Align: Handling Spatial Coverage

You can make rasterization more exact by making sure your template only looks at areas that matter. Use crop() to cut the raster and mask() to leave out data you don't need.

Cropping:

Cuts the raster to the polygon's edges.

r_template <- crop(r_template, poly)

Masking:

Leaves out areas outside polygon borders.

r_rasterized <- mask(r_template, poly)

Aligning:

When putting together rasters from different places, align() matches their extents and rows/columns.

r_aligned <- align(r_raster_1, r_raster_2)

Using these makes things more exact and stops things from not matching during more analysis.

A Sure-Fire Way to Debug

You can find rasterization issues in an organized way. Follow these steps:

1. ✅ Check if CRSs work together:

   crs(poly)
   crs(template)
   

2. 👀 Look at areas where they might overlap:

   plot(template); lines(poly)
   

3. 🔍 Compare extents with:

   ext(template); ext(poly)
   

4. 🧾 Check if fields are there:

   names(poly)
   

5. 🧪 Rasterize just one polygon to test:

   r_test <- rasterize(poly[1,], template, field="id")
   plot(r_test)
   

6. 🧂 Use extract() to check values:

   extract(r_test, poly)
   

This step-by-step method shows exactly where the process breaks. This happens before you spend time and effort running the whole thing.

Common Mistakes People Make with R rasterization

These mistakes often cause problems for even experienced R users:

• ❌ Not setting a raster template—this causes default actions that are hard to guess.
• ⚠️ Not checking CRS causes layers to not line up right.
• 🤷 Wrong or missing field values cause errors you don't see.
• 🐘 Raster scale that doesn't fit well misses small polygons.
• 🎨 Not plotting steps along the way leads to “guess debugging.”

Avoiding these makes sure you get clear, easy-to-understand polygon to raster conversion.

Making Performance Better For Large Datasets

When you work with complicated shapes or data for a whole country, speed is very important.

Making It Faster:

• 🧩 Simplify vectors using aggregate() or combine features
• 💾 Always save output to disk to use less RAM:

  rasterize(poly, r_template, field="id", filename="output.tif", overwrite=TRUE)
  

• 🎯 Filter polygons first using location-based searches (subset())
• ⚙️ Run many tasks at once across tiles or GCP/cluster environments

Good processing stops crashes and lets you grow your spatial processes.

Beyond terra: When Other Ways Work Better

Sometimes terra::rasterize() isn’t enough. R is strong but not always perfect.

Other Tools:

• raster::rasterize() — older but deals with some tricky situations better.
• sf::st_cast() and sf::st_union() — get geometry ready by changing how complex it is.
• QGIS or Python (rasterio, gdal_rasterize) — better for unusual shapes.
• Save vector file, rasterize in an outside tool, and bring it back into R.

Using tools together makes sure the output is correct across complicated situations.

End-To-End Example: A Clean and Lined Up Polygon to Raster Process

library(terra)

# Step 1: Load polygon layer
poly <- vect("zoning.shp")

# Step 2: Create a lined up raster grid
template <- rast(ext(poly), resolution=100, crs=crs(poly))

# Step 3: Project polygon to raster CRS if needed
# poly <- project(poly, crs(template))

# Step 4: Rasterize using a good field and aggregation func
r <- rasterize(poly, template, field="zone_id", fun="first")

# Step 5: Mask to get clear outlines
r <- mask(r, poly)

# Step 6: Plot to check results
plot(r)

Following this clean pattern gets rid of most common errors and makes it easier to repeat your work.

Final Recap: Getting Good At terra rasterize

To turn vector polygons to raster in R correctly using terra::rasterize(), always keep in mind:

• 📏 Make resolution and scale fit polygon detail.
• 🌍 Line up CRS, raster extent, and pixel grid closely.
• 🎯 Check and set the right field values.
• 🛠 Use fun to handle overlaps smartly.
• 🧪 Debug with one polygon and look at results often.
• 🚀 Make performance smoother for big jobs.

Getting good at polygon-to-raster conversion takes time. But it gives you the power of raster-based geospatial analysis. This helps with better modeling and more understanding.

Looking Ahead: Adding To Your Raster Tools

Once you're good with R rasterization:

• Try out raster math and if-then logic using terra.
• Learn tiled processing and masking in regional processes.
• Look at tmap, stars, and exactextractr for more complex raster processes.
• Combine remote sensing data and large geospatial databases.

Keep asking questions. Keep working with spatial data.

Citations

Hijmans, R. J. (2023). terra: Spatial Data Analysis. R package version 1.7-29. https://cran.r-project.org/package=terra

Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. CRC Press. https://geocompr.robinlovelace.net

Pebesma, E. (2018). Simple features for R: Standardized support for spatial vector data. The R Journal, 10(1), 439–446. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html