Recent Posts

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 allowing for reproducing these results and encouraging to try these metrics on different data. To learn more about this topic, read our open access article: Nowosad, J., and T.

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World population growth through time

A few months ago I have made an attempt to visualize the world population changes from 1800 to 2100: Inspired by @MaxCRoser and @jkottke, I've tried to visualize the world population changes from 1800 to 2100. My new blog post at describes how this animation was made using #rstats and #OpenData. — Jakub Nowosad (@jakub_nowosad) October 9, 2018 This way of visualization is good to show the ever-changing distribution of the population on a global scale.

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Recent Publications

  • Hasselbarth M., Sciaini M., With K., Wiegand K., Nowosad J. (2019). landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography.
    Abstract PDF Online DOI
  • Nowosad, J., Stepinski, T. F. (2019). Information Theory as a consistent framework for quantification and classification of landscape patterns. Landscape Ecology.
    Abstract PDF Online Preprint DOI
  • Nowosad, J., (2019). Elementarz programisty: wstęp do programowania używając R. Poznań: Space A. Online:
    Abstract PDF Online
  • Nowosad, J., Stepinski, T. F., Netzel, P. (2019). Global assessment and mapping of changes in mesoscale landscapes: 1992–2015. International Journal of Applied Earth Observation and Geoinformation, DOI: 10.1016/j.jag.2018.09.013
    Abstract Online Preprint DOI