I am extremely proud to announce that Geocomputation with R is complete. It took Robin, Jannes, and me almost 2 years of collaborative planning, writing, refinement, and deployment to make the book available for anyone interested in open source, command-line approaches for handling geographic data.
Last week Neil Kaye tweeted: This, of course, provoked me to ask: is it reproduclible? And more specifically, can it be reproduced in the open source statistical programming language, R? This post, building on experiences working on the open source and reproducible book project Geocomputation with R, answers both questions with a definitive YES.
Last month, Max Roser presented a cartogram of the Earth’s population in 2018. He also provided some perspectives on its spatial distribution in an article on the worldinourdata.org, which I recommend. Links to the article were shared in many places, including in the blog post A Map of the World Where the Sizes of Countries Are Determined by Population. The author, Jason Kottke, concluded with a wish: Why not from 1800 to 2100?
Creating or determination of regions is a useful way to describe the world. Regionalization does not only allow for a quicker understanding of spatial patterns but also can influence how regions are managed. Regions are created in various disciplines.
This is the sixth and the last blog post in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis. In the previous one we presented the pattern-based spatial segmentation - a method for creating regions of homogeneous patterns. Here, we will mention other pattern-based methods and show some examples of how you can use pieces of GeoPAT 2 in your own workflow.
Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 This is a fifth blog post in the series
Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 This is a fourth blog post in the series
Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 This is a third blog post in the series
Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 This is a second blog post in the series
Global socio-economic data is easily accessible nowadays. Just type the indicator of interest and the name of the country in your preferred search engine and you can find its value, sometimes also an additional plot or a map. But what about when you want to go further and (for example): Want to compare many countries? Get data just for a specific year? See changes in time? Just want to create a very specific plot or a map?
Blog posts in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis: GeoPAT 2: Software for Pattern-Based Spatial and Temporal Analysis Pattern-based Spatial Analysis - core ideas Finding similar local landscapes Quantifying temporal change of landscape pattern Pattern-based regionalization Moving beyond pattern-based analysis: Additional applications of GeoPAT 2 Take a look at the satellite image below -