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Assessing experienced tranquillity through natural language processing and landscape ecology measures.

Flurina M Wartmann1,2, Olga Koblet3, Ross S Purves3

  • 1School of Geosciences, University of Aberdeen, St Mary's, Elphinstone Road, Aberdeen, AB24 3UF UK.

Landscape Ecology
|November 1, 2021
PubMed
Summary

User-generated text data can identify experienced tranquillity in landscapes. This study links landscape features like elevation and naturalness to perceived tranquillity, finding improved grasslands and arable land most tranquil.

Keywords:
Landscape perceptionLandscape qualityNatural language processingTranquillity mappingUser-generated content

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Area of Science:

  • Environmental Science
  • Landscape Ecology
  • Computational Social Science

Background:

  • Identifying tranquil areas is crucial for landscape planning and policy.
  • Discrepancies exist between modelled potential tranquil areas and those perceived by people.
  • User-generated text data offers a scalable alternative to resource-intensive field surveys for mapping experienced tranquillity.

Purpose of the Study:

  • To explore and model the relationship between landscape ecological measures and experienced tranquillity.
  • To extract perceived tranquillity from user-generated text data using natural language processing.
  • To quantify the influence of landscape characteristics on experienced tranquillity.

Main Methods:

  • Georeferenced user-generated landscape descriptions from Geograph.UK were filtered for tranquillity keywords.
  • Tranquil locations were stratified by dominant land cover.
  • Landscape characteristics (diversity, naturalness) and their influence on tranquillity were quantified using logistic regression.
  • Natural language processing identified terms linked to tranquillity and compared their similarity across land cover classes.

Main Results:

  • Six keywords yielded 15,350 tranquillity descriptions.
  • Arable and horticulture, and improved grassland were the most common land covers associated with tranquillity.
  • Freshwater, elevation, and naturalness positively predicted tranquillity, while built-up areas negatively predicted it.
  • Tranquillity descriptions were most similar between improved grassland and arable land, and most dissimilar between arable land and urban areas.

Conclusions:

  • Natural language processing effectively extracts experienced tranquillity from text data.
  • Links between landscape ecological measures and perceived tranquillity as a landscape quality were demonstrated.
  • This approach provides a valuable tool for understanding human perception of landscape tranquillity.