What type of data is best represented by differing shapes, colors, or textures?

The best choice for representing data through differing shapes, colors, or textures is nominal data. Nominal data is categorical data that can be used to label variables without any quantitative value. Examples include categories like types of land use (residential, commercial, agricultural) or different species of plants. Each category is distinct and non-numeric, making it ideal for visual differentiation using visual techniques such as colors, shapes, or textures.

In contrast, ordinal data involves a ranking or order but does not establish precise differences between the ranks, which limits the effective use of visual techniques for representation. Interval data includes numerical values where the difference between values is meaningful, but it does not have a true zero point, making color differentiation less effective for conveying relational data. Ratio data contains all the properties of interval data, but has a clear definition of zero, allowing for a true comparison of magnitudes; however, like interval data, it tends to be better suited to numerical representations rather than categorical visual differentiation. Thus, nominal data is the most appropriate type to represent through distinct visual elements.