Information theory provides a consistent framework for the analysis of spatial patterns

Quantitative assessment of spatial patterns has been a keen interest of generations of spatial scientists and practitioners using spatial data. This post describes Information Theory-based metrics allowing for numerical description of spatial patterns. Each example is accompanied by an R code allowing for reproducing these results and encouraging to try these metrics on different data. To learn more about this topic, read our open access article: Nowosad, J., and T.

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Efficient landscape metrics calculations for buffers around sampling points

Landscape metrics are algorithms that quantify physical characteristics of landscape mosaics (aka categorical raster) in order to connect them to some ecological processes. Many different landscape metrics exist and they can provide three main levels of information: (i) landscape level, (ii) class level, and (iii) patch level. A landscape level metric gives just one value describing a certain property of a landscape mosaic, such as its diversity. A class level metric returns one value for each class (category) present in a landscape.

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