![]() To validate a series of maps with two or more time points To validate a simulation against a reference mapĤ. To validate soft maps produced by the model against a reference mapģ. To validate a map against reference data/mapĢ. These help unleash the full potential of Cross-Tabulation analysis. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps”, we focus on specific analyses that can be carried out on the basis of Cross-Tabulation analyses, such as Land Use Cover Changes (LUCC) Budget or Quantity and Allocation disagreement. In the Chapters “ Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps” and “ Pontius Jr. In the example exercises, we used CORINE and SIOSE maps from the Asturias Central Area database, as well as maps from the modelling exercises carried out with this database. Examples of Cross-Tabulation analyses at one and multiple resolutions are presented for four different cases: to validate single LUC maps, to validate the soft maps produced by a model, to validate a simulation exercise and to validate and study land change in a series of LUC maps. ![]() As a reference we use LUC maps, although ground points could also be used as reference data for these analyses. In the exercises, raster data were obtained from vector data previously rasterized at different spatial resolutions. Basic Cross-Tabulation can compare raster and vector data, while Multiple-Resolution Cross-Tabulation only works with raster data, which is what we use in the exercises provided as examples. 2) compares two raster datasets at multiple spatial resolutions. Multiple-Resolution Cross-Tabulation (Sect. Results are usually displayed in the form of maps, tables and other statistical measures. It compares two datasets to observe their spatial relationship, i.e. 1) is usually the first step in the validation of Land Use Cover (LUC) data. In this chapter, we describe the fundamental principles and the normal procedure followed when cross-tabulating two datasets.
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