R packages


supercells: Creates superpixels based on input spatial data; this package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters)

sabre: Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure

motif: Enables spatial analysis such as search, change detection, and clustering to be performed on spatial patterns

landscapemetrics: Calculates landscape metrics of categorical map patterns

landscapetools: Provides utility functions for some of the less-glamorous tasks involved in landscape analysis

belg: Calculates the Boltzmann entropy of a landscape gradient

bespatial: Calculates several entropy metrics for spatial data inspired by Boltzmann’s entropy formula

comat: Creates co-occurrence matrices based on spatial data

regional: Calculates intra-regional and inter-regional similarities based on user-provided spatial vector objects (regions) and spatial raster objects (cells with values)

spatialising: Performs simulations of binary spatial raster data using the Ising model

patternogram: Quantifies the spatial autocorrelation of values from a set of points or a raster object

raceland: Pattern-based, zoneless method for analysis and visualization of racial topography

waterquality: Detects and quantifies water quality and cyanobacterial harmful algal bloom (CHABs) from remotely sensed imagery

reedsolomon: Applying the Reed-Solomon algorithm on remote sensing data

rgeopat2: Additional functions for ‘GeoPAT’ 2


spData: Datasets for spatial analysis

climate: An interface for downloading in-situ meteorological (and hydrological) dataset

rgugik: An interface for downloading datasets from Polish Head Office of Geodesy and Cartography

popgrids: Global dataset of gridded population and GDP (1980-2010 estimations and 2020-2100 scenarios)

geostatbook: Datasets used in the Geostatistics with R book


rcartocolor: An implementation of the CARTOcolor palettes in R

colorblindcheck: Tools helping to decide if a color palette is colorblind friendly


pollen: Tools for working with aerobiological data



The geocompx website: geocompx hosts free resources on reproducible geographic data analysis, modelling and visualization with open source software

CRAN Task View: Analysis of Spatial Data: a guidance on which packages on CRAN are relevant for spatial data analysis


Country-level Land Cover: Country-level Land Cover is a database containing land cover categories, transitions, and gross changes on a country level for years between 1992 and 2015


GeoPAT 2: GeoPAT 2 - a suite of modules dedicated to analysis of large datasets in their entirety using spatial and/or temporal patterns


OpenGeoHub Summer School Poznan 2023: Summer School 2023: “Processing and visualizing large geospatial data using R, Python and Julia

GIScience 2021: GIScience 2021 - 11th International Conference on Geographic Information Science. Poznań, Poland


The distortion of the Mercator projection: A repository containing an R code and animations showing area distortions of the Mercator projection

Alternative layout for maps of the United States: A repository containing an R code and spatial data to create an inset map of the USA, which shows all the states and ensures relative sizes are preserved

Alternative layout for maps of Spain: A repository containing an R code and spatial data to create an inset map of Spain, which includes the Canary Islands and ensures that relative sizes are preserved

The 2020 Environmental Performance Index (EPI): The 2020 Environmental Performance Index (EPI) provides a data-driven summary of the state of sustainability around the world. You can compare countries and get a summary of each of them

Template for writing a PhD thesis in Markdown: Template for writing a PhD thesis in Markdown, a super-friendly plain text format. Using Pandoc, the Markdown can be easily converted to popular formats such as LATEX, PDF, MS Word, HTML, etc.

Discovering good data packages: The state Of data on CRAN: discovering good data packages