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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
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.
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.
Keywords:
Landscape perceptionLandscape qualityNatural language processingTranquillity mappingUser-generated contentMore Related Videos
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