Extracting positive descriptions and exploring landscape value using text analysis in the Cairngorms National Park

Being in natural places is known to influence well-being. Therefore, increasing attention is being paid to the diverse ways in which our emotions can be influenced by places, and this paper investigates this notion in a protected area -- the Cairngorms National Park in Scotland. We assume that positive sentiment correlates with pleasant (hedonic) experiences, and extract positive emotions and objects associated with them, from textual descriptions written about places in the park. To do so, we first apply sentiment analysis and select descriptions with positive sentiment. Second, we filter the initial dataset to reduce the impact of prolific users and ambiguously positive words. From an initial set of 33790 descriptions we are left with 3031 positive descriptions. Third, we apply transducers to find semantic sequences for patterns detection. Finally, these sequences are annotated and assigned to one of five classes -- activities, aesthetics, features, places and times. Features are by far the most common class, making up more than 50% of all unique nouns associated with positive emotions

Capturing perceived everyday lived landscapes through gamification and active crowdsourcing

Summary Landscapes are distinguishable areas of the earth with distinct characters comprised of tangible and intangible dimensions and entities. Interactions between humans and landscapes influence social, physical and mental well-being as well as guide behaviour. Understanding how landscapes are perceived has thus gained traction in sustainable and inclusive policy and decision making processes and public participation is called for. The recognised importance of understanding landscapes from an experiential and perceptual perspective and incorporating public participation in data generation efforts is reflected in overarching conventions, policy guidelines and frameworks including the European Landscape Convention (ELC), the Millennium Ecosystem Assessment (MEA), Natures Contributions to People (NCP) and the Landscape Character Assessment (LCA) framework. Major challenges for these conventions and frameworks are 1) how to collect data on landscape experiences and perceptions from a diverse group of individuals, 2) how to integrate and link physical entities, sensory experiences and intangible dimensions of landscapes and 3) how to identify other potential sources of landscape relevant information. The abundance of storage space and the accessibility of broadband internet have led to a burgeoning of user generated natural language content. In parallel, various paradigms of exploiting ubiquitous internet access for research purposes have emerged, including crowdsourcing, citizen science, volunteered geographic information and public participation geographic information systems. These low cost approaches have shown great potential in generating large amounts of data, however, they struggle with motivating and retaining participants. Gamification - broadly defined as adding entertaining or playful elements to applications or processes - has been found to increase user motivation and has explicitly been called for in landscape perception and preference research to diversify participant demographics. Meanwhile, natural language has been found to be deeply intertwined with thought and emotion and has been identified as a rich source of semantic data on how landscapes are perceived and experienced. Written texts and the ways in which these can be analysed have gained particular interest. Therefore, the overall goal of this thesis is to develop and implement a gamified crowdsourcing application to collect natural language landscape descriptions and to analyse and explore the contributions in terms of how landscapes are perceived through sensory experiences and how additional landscape relevant natural language can be identified. To approach this goal, I first elicit key data and feature requirements to collect landscape relevant information from a heterogeneous audience. Guided by the identified requirements, I develop and implement Window Expeditions, a gamified active crowdsourcing platform geared towards collecting natural language descriptions of everyday lived landscapes. The generated corpus of natural language is explored using computational methods and I present and discuss the results in light of who the contributors are, the locations from which participants contribute and salient terms found in English and German. In a further step I annotate a subset of English contributions according to the contained biophysical elements, sensory experiences and cultural ecosystem (dis)services and explore these in terms of how they are linked. Finally, I present a novel approach of using a curated high quality landscape specific dataset to computationally identify similar documents in other corpora using sentence-transformers. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in reporting not being at home (n = 172) who were more likely to contribute from areas of herbaceous vegetation. Terms describing salient elements of everyday lived environments such as "tree", "house", "garden" and "street", as well as weather related phenomena and colours were found frequently in both English and German contributions in the generated corpus. Further, terms related to space, time and people were found significantly more frequently in the generated corpus compared to general natural language and representative landscape image descriptions highlighting the importance of spatial features as well as people and the times at which these were observed. Notably, descriptions referring to trees and birds were frequently found in the contributed texts, underlining their saliency in everyday lived landscapes. The results show biophyiscal terms related to vegetation (n = 556) and the built environment (n = 468) as well as weather related terms (n = 452) to be most prominent. Further, contributions referencing visual (n = 186) and auditory (n = 96) sensory experiences were found most often with positive sensory experiences being most common (n = 168) followed by neutral (n = 86) and negative (n = 68). In regards to the intangible dimensions captured in the contributed landscape descriptions, recreation (n = 68) was found most often followed by heritage (n = 36), identity (n = 26) and tranquillity (n = 23). Through linking biophysical elements, sensory experiences and cultural ecosystem (dis)services, the results show that the biophysical category of animals appears often with the sensory experience of smell/taste and the biophysical category of moving objects appears more than expected with the sensory experience of sound. Further, the results show the cultural ecosystem service of inspiration to often appear with the biophysical category of natural features and tranquillity with weather. Using a curated subcorpus of English natural language landscape descriptions (n = 428) collected with Window Expeditions, similar documents in other collections were identified. Through translating documents to vectors by means of sentence-transformers and calculating cosine similarity scores, a total of 6075 to 8172 documents were identified to be similar to contributions to Window Expeditions, depending on if the initial dataset was prefiltered for biophysical noun lemmas (a list of biophysical landscape elements derived from the Window Expeditions corpus) and Craik’s list adjectives (a list of common adjectives used to describe landscapes). Latent Dirichlet allocation topic modelling, a clustering approach which is commonly used to identify overarching topics or themes in collections of natural language, shows four distinct clusters in both Window Expeditions as well as in the corpus of identified similar documents, namely urban and residential, rural and natural, autumn and colours and snow and weather. Overall, the results presented in this thesis provide further evidence to work that natural language is a rich source of landscape specific information, capturing underlying semantics of a multitude of referenced landscape dimensions. In particular, this thesis demonstrates that computationally aided approaches to analysing and exploring landscape relevant textual data can give detailed insights into salient features of landscapes and how individuals perceive and experience these. Especially when complemented by human annotation, natural language landscape descriptions are a welcome source of data about a landscape’s biophysical elements, individual sensory experiences in landscapes and the perceived cultural ecosystem (dis)services. The findings of this thesis are accompanied by various limitations, chief amongst which are the possibilities of users to falsify their locations, the rather small amount of data that was collected through Window Expeditions and the Eurocentric definitions and approaches common in landscape perception research. The former two limitations can be addressed through implementational reiterations and promotional efforts, whereas the latter limitation calls for further consideration of the socio-culturally induced construction of landscape perception research and a rethinking of holistic approaches, especially in multicultural participatory contexts. The work presented in this thesis shows great potential in complementing landscape perception research with gamified methods of data generation. Active crowdsourcing can be a cost efficient and scalable approach of generating much needed data from a diverse audience. Exploring landscape relevant natural language with both quantitative and qualitative methods from various disciplines including geographic information science, linguistics and machine learning can lead to new insights into landscape perception, sensory landscape experiences and how these are expressed

Fictive motion extraction and classification

Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a staticand a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse