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

CONTEXT: 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. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s10980-020-01181-8) contains supplementary material, which is available to authorized users.

Conceptualisations of landscape differ across European languages.

Policies aimed at sustainable landscape management recognise the importance of multiple cultural viewpoints, but the notion of landscape itself is implicitly assumed to be homogeneous across speech communities. We tested this assumption by collecting data about the concept of "landscape" from speakers of seven languages of European origin. Speakers were asked to freely list exemplars to "landscape" (a concrete concept for which the underlying conceptual structure is unclear), "animals" (a concrete and discrete concept) and "body parts" (a concrete concept characterised by segmentation). We found, across languages, participants considered listing landscape terms the hardest task, listed fewest exemplars, had the least number of shared exemplars, and had fewer common co-occurrence pairs (i.e., pairs of exemplars listed adjacently). We also found important differences between languages in the types of exemplars that were cognitively salient and, most importantly, in how the exemplars are connected to each other in semantic networks. Overall, this shows that "landscape" is more weakly structured than other domains, with high variability both within and between languages. This diversity suggests that for sustainable landscape policies to be effective, they need to be better tailored to local conceptualisations.

Modelling ranging behaviour of female orang-utans: a case study in Tuanan, Central Kalimantan, Indonesia.

Quantification of the spatial needs of individuals and populations is vitally important for management and conservation. Geographic information systems (GIS) have recently become important analytical tools in wildlife biology, improving our ability to understand animal movement patterns, especially when very large data sets are collected. This study aims at combining the field of GIS with primatology to model and analyse space-use patterns of wild orang-utans. Home ranges of female orang-utans in the Tuanan Mawas forest reserve in Central Kalimantan, Indonesia were modelled with kernel density estimation methods. Kernel results were compared with minimum convex polygon estimates, and were found to perform better, because they were less sensitive to sample size and produced more reliable estimates. Furthermore, daily travel paths were calculated from 970 complete follow days. Annual ranges for the resident females were approximately 200 ha and remained stable over several years; total home range size was estimated to be 275 ha. On average, each female shared a third of her home range with each neighbouring female. Orang-utan females in Tuanan built their night nest on average 414 m away from the morning nest, whereas average daily travel path length was 777 m. A significant effect of fruit availability on day path length was found. Sexually active females covered longer distances per day and may also temporarily expand their ranges.