Assessing experienced tranquillity through natural language processing and landscape ecology measures

Flurina M. Wartmann* (Corresponding Author), Olga Koblet, Ross S. Purves

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying tranquil areas is important for landscape planning and policy-making. Research demonstrated discrepancies between modelled potential tranquil areas and where people experience tranquillity based on field surveys. Because surveys are resource-intensive, user-generated text data offers potential for extracting where people experience tranquillity.
Objectives
We explore and model the relationship between landscape ecological measures and experienced tranquillity extracted from user-generated text descriptions.

Methods
Georeferenced, user-generated landscape descriptions from Geograph.UK were filtered using keywords related to tranquillity. We stratify resulting tranquil locations according to dominant land cover and quantify the influence of landscape characteristics including diversity and naturalness on explaining the presence of tranquillity. Finally, we apply natural language processing to identify terms linked to tranquillity keywords and compare the similarity of these terms across land cover classes.

Results
Evaluation of potential keywords yielded six keywords associated with experienced tranquillity, resulting in 15,350 extracted tranquillity descriptions. The two most common land cover classes associated with tranquillity were arable and horticulture, and improved grassland, followed by urban and suburban. In the logistic regression model across all land cover classes, freshwater, elevation and naturalness were positive predictors of tranquillity. Built-up area was a negative predictor. Descriptions of tranquillity were most similar between improved grassland and arable and horticulture, and most dissimilar between arable and horticulture and urban.

Conclusions
This study highlights the potential of applying natural language processing to extract experienced tranquillity from text, and demonstrates links between landscape ecological measures and tranquillity as a perceived landscape quality.
Original languageEnglish
Number of pages19
JournalLandscape Ecology
Early online date27 Jan 2021
DOIs
Publication statusE-pub ahead of print - 27 Jan 2021

Keywords

  • Tranquillity mapping
  • Landscape perception
  • Landscape quality
  • User-generated content
  • Natural Language Processing

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