“What is OUV” revisited: A computational interpretation on the statements of outstanding universal value

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Abstract

The Statements of Outstanding Universal Value (OUV) concerns the core justification for nominating and inscribing cultural and natural heritage properties on the UNESCO World Heritage List, ever since 2007. Ten criteria are specified and measured independently for the selection process. The 2008 ICOMOS Report “What is OUV” has been a successful example to interpret OUV as an integral concept by inspecting the associations of the selection criteria in all inscribed properties. This paper presents a novel methodology for interpreting OUV using computational techniques of Natural Language Processing, Machine Learning, and Graph Visualization. Firstly, frequent phrases appearing in Statements of OUV are used to construct a lexicon for each selection criterion; Secondly, three similarity matrices are constructed as graphs to represent the pair-wise associations of the criteria; Lastly, the lexicon and graphs are visualized in 2D. The study shows that the lexicon derived from computational techniques can capture the essential concepts of OUV, and that the selection criteria are consistently associated with each other in different similarity metrics. This study provides a quantitative and qualitative interpretation of the Statements of OUV and the associations of selection criteria, which can be seen as an elaborated computational extension of the 2008 Report, useful for future inscription and evaluation process of World Heritage nominations.
Original languageEnglish
Pages (from-to)25-32
Number of pages8
JournalISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences
VolumeVIII-M-1-2021
DOIs
Publication statusPublished - 2021
Event28th CIPA Symposium “Great Learning & Digital Emotion” - Beijing, China
Duration: 28 Aug 20211 Sep 2021

Keywords

  • Outstanding Universal Value
  • UNESCO
  • World Heritage
  • Natural Language Processing
  • Machine Learning
  • Similarity Matrix
  • Graph Visualization
  • Statistics

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