Geographic similarity analysis for Land System Science: opportunities and tools to facilitate knowledge integration and transfer

Vasco Diogo*, Matthias Bürgi, Niels Debonne, Julian Helfenstein, Christian Levers, Rebecca Swart, Tim G. Williams, Peter H. Verburg

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Advances in Land System Science (LSS) rely on the evidence generated by different types of research activities, including place-based case studies, landscape/land-system mapping and synthesis research. However, these activities are usually conducted in parallel, with a lack of integration often leading to important knowledge gaps and limitations. In this article, we provide tools for the application of geographic similarity analysis (GSA), a collection of spatially-explicit methods assessing the degree of similarity between geographic locations, and thereby help to address these limitations. We identify opportunities for employing GSA to support: 1) selecting geographically representative sets of case studies; 2) integrating empirical evidence generated at different scales and levels of abstraction; and 3) facilitating context-sensitive knowledge transfer. The resulting toolbox provides approaches for facilitating researchers to get an enhanced understanding of multi-scale land change processes, as well as supporting land governance in scaling up the knowledge and solutions generated by LSS research.

Original languageEnglish
Pages (from-to)227-248
Number of pages22
JournalJournal of Land Use Science
Volume18
Issue number1
DOIs
Publication statusPublished - 6 Jun 2023

Keywords

  • Archetype analysis
  • Case studies
  • Geographic representativeness
  • Land system analysis
  • Synthesis research
  • Toolbox

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