Categorical data. These are non-numerical data. Grids that classify land use or land cover exemplify this category.
Categorical data - Data measured or observed by descriptive terms (e.g. very fertile, fertile, moderately fertile and infertile), rather than by numerical values.
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Discrete Data Categorical data such as types of vegetation, or class data such as speed zones. In geographical terms, discrete data can be represented by polygons. Sometimes referred to as integer data. In contrast, see continuous data.
Categorical data analysis - Data sets used in the book, An Introduction to Categorical Data Analysis, by Agresti are provided on-line by StatLib.
Confusion matrices (error matrices or contingency tables) for proportions of cover classes are used to better understand categorical data for the maps being compared.
It is primarily used for categorical data such as a land use classification, since it will not change the values of the data cells.
1 there will be support for categorical data in image services. This provides the ability to do better queries on raster datasets.
or just categorical data?) condition with no reference to the variance or its spatial distribution within a parcel. It is tough to generate a good spatial model from "generalized chunky" data.
Use this to display categorical data, such as soil type, county, or ecoregions. Be careful with unique values display, as a large number of unique class values can easily make classes indistinguishable.
 See also: Model, Information, Map, Class, Relation
  
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