Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/.

Quantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns.

Freely available and reliable meteorological datasets are highly demanded in many scientific and business applications. However, the structure of publicly available databases is often difficult to follow, especially for users who only deal with this kind of dataset on occasion. The “climate” R package aims to fill this gap with an easy-to-use interface for downloading global meteorological data in a fast and consistent way. The package provides access to different sources of in-situ meteorological data, including the Ogimet website, atmospheric vertical sounding gathered at the University of Wyoming’s webpage, and hydrological and meteorological measurements collected by the Institute of Meteorology and Water Management—National Research Institute (i.e., Polish Met Office). This article also provides a quick overview of the key functionalities available within the climate R package, and gives examples of an efficient and tidy workflow of meteorological data within the R based environment. The automation procedures included in the packages allow one to download data in a user-defined time resolution (from hourly to annual), for a user-defined time span, and for a specified group of stations or countries. The package also contains metadata, including a list of available stations, their geospatial information, and measurement descriptions with their units. Finally, the obtained datasets can be processed in R or exported to external tools (e.g., spreadsheets or GIS software).

There is a keen interest in calculating spatial associations between two variables spanning the same study area. Many methods for calculating such associations have been proposed, but the case when both variables are categorical is underdeveloped despite the fact that many datasets of interest are in the form of either regionalizations or thematic maps. In this paper, we advance this case by adapting the so-called -measure method from its original information-theoretical formulation to the analysis of variance formulation which provides more insight for spatial analysis. We present a step-by-step derivation of the -measure from the perspective of the analysis of variance. The method produces three indices of global association and two sets of local association indicators which could be mapped to indicate spatial distribution of association strength. The open-source software for calculating all indices from vector datasets accompanies the paper. To showcase the utility of the -measure, we identified three different application contexts: comparative, associative, and derivative, and present an example of each of them. The -measure method has several advantages over the widely used Mapcurves method, it has clear interpretations in terms of mutual information as well as in terms of analysis of variance, it provides more precise assessment of association, it is ready-to-use through the accompanying software, and the examples given in the paper serves as a guide to the gamut of its possible applications. Two specific contributions stemming from our re-analysis of the -measure are the finding of the conceptual flaw in the Geographical Detector—a method to quantify associations between numerical and categorical spatial variables, and a proposal for the new, cartographically based algorithm for finding an optimal number of regions in clustering-derived regionalizations.