The spatial signatures of categorical rasters are a set of numbers that describe the spatial patterns of the provided variables. Next, they allow for further operations such as searching, comparing, or clustering. Less known is that they can also be used to extract information about the composition and configuration of spatial patterns. This blog post shows how to do it using the motif R package.

This is a second blog post in a series about the supercells package. You can read the first one at “supercells: universal superpixels algorithm for applications to geospatial data”. The main idea of supercells is to create groupings of adjacent cells that share common characteristics.

Segmentation is a process of partitioning space into smaller segments. For example, imagine looking at your family photo and trying to distinct individual people. Similarly, we can look at a satellite image (in RGB colors) with the goal of delineating where are the buildings, fields, roads, etc. In geography, segmentation can also be associated with regionalization.

In the past, I wrote a post on how to calculate landscape-level metrics for local landscapes. There, I showed how to divide the categorical input map into a number of smaller areas and calculate selected landscape metrics for each area.

TLTR: This is a last blog post in a series about motif - an R package aimed for pattern-based spatial analysis. It sums up previous posts, but also underlines potential considerations when working with spatial patterns. Finally, it lists underexplored topics and future ideas related to pattern-based spatial analysis.

TLTR: Clustering similar spatial patterns requires one or more raster datasets for the same area. Input data is divided into many sub-areas, and spatial signatures are derived for each sub-area. Next, distances between signatures for each sub-area are calculated and stored in a distance matrix. The distance matrix can be used to create clusters of similar spatial patterns.

TLTR: Quantifying changes of spatial patterns requires two datasets for the same variable in the same area. Both datasets are divided into many sub-areas, and spatial signatures are derived for each sub-area for each dataset. Next, distances for each pair of areas are calculated. Sub-areas with the largest distances represent the largest change.

TLTR: Finding similar spatial patterns requires data for a query region and a search space. Spatial signatures are derived for the query region and many sub-areas of the search space, and distances between them are calculated.

TLTR: Spatial signatures are multi-value representations of the patterns that compress information about spatial composition and configuration. Spatial signatures can be directly compared using various distance measures. Describing categorical rasters A categorical raster shown below represents land cover data for some area. This area is mainly covered by forest, with some small patches of agriculture, grasslands, and water.

I gave the overview of what is the pattern-based spatial analysis and how it can be applied for the RGS-IBG GIScience Webinar Series. You can find the workshop abstract, slides, and recording below. Abstract Discovering and describing spatial patterns is an important element of many geographical studies with spatial patterns being related to ecological and sociological processes.

TLTR: motif is an R package aimed for pattern-based spatial analysis. It allows for spatial analysis such as search, change detection, and clustering to be performed on spatial patterns. This blog post introduces basic ideas behind the pattern-based spatial analysis, and shows the types of problems to which it can be applied.