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


Jakub Nowosad


February 4, 2021

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.


Discovering and describing spatial patterns is an important element of many geographical studies with spatial patterns being related to ecological and sociological processes. While spatial patterns are often clearly visible on maps, it is not easy to unequivocally decide if two areas are much alike or delineate regions with similar patterns. In this talk, Jakub Nowosad will present a set of consistent ideas on how spatial patterns can be described and analyzed, with a focus on categorical raster data. The core idea is to divide raster data consisting of cells having simple content (a single value) into a large number of smaller areas, and then characterize each area using a statistical description of a pattern - a spatial signature. Spatial signatures are multi-values representations of spatial composition and configuration, and therefore can be compared using a large number of existing distance or dissimilarity measures. This enables spatial analysis such as search, change detection, clustering, and segmentation. During this talk, a number of real-life examples of finding similar spatial patterns, detecting changes over time, and grouping areas with homogeneous patterns for regional, continental, and global scales will be shown.


You can find the slides for the talk at




BibTeX citation:
  author = {Jakub Nowosad},
  editor = {},
  title = {Pattern-Based Spatial Analysis: An Approach for Discovering,
    Describing and Studying Geographical Patterns},
  date = {2021-02-04},
  url = {},
  langid = {en}
For attribution, please cite this work as:
Jakub Nowosad. 2021. “Pattern-Based Spatial Analysis: An Approach for Discovering, Describing and Studying Geographical Patterns.” February 4, 2021.