Considerations for the pattern-based spatial analysis

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 …

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Clustering similar spatial patterns

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 …

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Quantifing changes of spatial patterns

TLTR: Quantifing 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 …

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Finding similar spatial patterns

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. Sub-areas with the smallest distances from the query region are the most …

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Describing categorical rasters with spatial signatures

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 …

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Pattern-based spatial analysis: an approach for discovering, describing and studying geographical patterns

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 …

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Pattern-based spatial analysis in R: an introduction

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 …

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How to measure spatial diversity and segregation?

The raceland package implements a computational framework for a pattern-based, zoneless analysis and visualization of (ethno)racial topography. The main concept in this package is a racial landscape (RL). It consists of many large and small patches (racial enclaves) formed by adjacent raster grid …

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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 …

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