Vorbias

Vorbias is a term used in discussions of bias within Voronoi-based methods applied to spatial analysis, machine learning, and related fields. It denotes systematic distortions that arise when the geometric partitioning created by a Voronoi diagram interacts with uneven data distributions.

In practice, Voronoi partitions are defined by a set of seed points. If seeds are sparse in some regions and dense in others, the resulting cells vary in area. Predictions or decisions tied to cells—such as assigning labels, estimating values, or guiding resource allocation—can therefore reflect the sampling pattern rather than the underlying phenomenon.

Vorbias is distinct from computational bias intrinsic to algorithms; it is a representation bias that emerges from the geometry of the partitioning and the data collection process.

Mitigation strategies include spatially aware sampling, weighting predictions by cell area or sampling density, using weighted Voronoi diagrams, and cross-validation schemes that preserve spatial structure.

In literature, vorbias is discussed as one of multiple biases affecting spatial models; some authors argue for careful dataset design and explicit bias assessments during model validation.

See also: Voronoi diagram, spatial bias, spatial cross-validation, bias in machine learning.