Raster Algebra
Definition
Raster algebra, also called map algebra, performs cell-by-cell mathematical and logical operations on rasters to derive new surfaces. Operations include local (per-cell), focal (neighborhood), zonal (within polygons), and global functions. Examples: combine slope and land cover to compute erosion risk; apply conditional statements to mask unsuitable areas; calculate indices like NDVI; or run cost-distance accumulations. Units, nodata handling, and cell alignment are crucial for correct results. Raster algebra underpins suitability modeling, hydrology, visibility, and terrain analysis. Performance considerations—tiling, chunking, and lazy evaluation—enable processing of large datasets on modern hardware or cloud platforms.
Application
Planners create suitability maps; hydrologists compute flow accumulation; conservationists map connectivity costs; public-health teams derive exposure surfaces; agriculture computes vigor and stress indices. Education uses raster algebra to teach spatial reasoning.
FAQ
How to handle nodata?
Use explicit nodata values and functions that respect them; avoid silently treating nodata as zeros. Align rasters before operations.
What about differing resolutions?
Resample appropriately—nearest for categorical, bilinear/cubic for continuous—and document the effects on results.
Can we chain many operations safely?
Yes with clear workflows and tests; performance frameworks (Dask, Rasters on cloud) help scale while maintaining reproducibility.
How to validate outputs?
Spot-check against known sites, compare with independent datasets, and review unit consistency and ranges.