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Scientific articles often contain relevant geographic information such as where field work was performed or where patients were treated. Most often, this information appears in the full-text article contents as a description in natural language including place names, with no accompanying machine-readable geographic metadata. Automatically extracting this geographic information could help conduct meta-analyses, find geographical research gaps, and retrieve articles using spatial search criteria. Research on this problem is still in its infancy, with many works manually processing corpora for locations and few cross-domain studies. In this paper, we develop a fully automatic pipeline to extract and represent relevant locations from scientific articles, applying it to two varied corpora. We obtain good performance, with full pipeline precision of 0.84 for an environmental corpus, and 0.78 for a biomedical corpus. Our results can be visualized as simple global maps, allowing human annotators to both explore corpus patterns in space and triage results for downstream analysis. Future work should not only focus on improving individual pipeline components, but also be informed by user needs derived from the potential spatial analysis and exploration of such corpora.

Events profoundly influence human-environment interactions. Through repetition, some events manifest and amplify collective behavioral traits, which significantly affects landscapes and their use, meaning, and value. However, the majority of research on reaction to events focuses on case studies, based on spatial subsets of data. This makes it difficult to put observations into context and to isolate sources of noise or bias found in data. As a result, inclusion of perceived aesthetic values, for example, in cultural ecosystem services, as a means to protect and develop landscapes, remains problematic. In this work, we focus on human behavior worldwide by exploring global reactions to sunset and sunrise using two datasets collected from Instagram and Flickr. By focusing on the consistency and reproducibility of results across these datasets, our goal is to contribute to the development of more robust methods for identifying landscape preference using geo-social media data, while also exploring motivations for photographing these particular events. Based on a four facet context model, reactions to sunset and sunrise are explored for Where, Who, What, and When. We further compare reactions across different groups, with the aim of quantifying differences in behavior and information spread. Our results suggest that a balanced assessment of landscape preference across different regions and datasets is possible, which strengthens representativity and exploring the How and Why in particular event contexts. The process of analysis is fully documented, allowing transparent replication and adoption to other events or datasets.

Volunteered geographical information (VGI) and citizen science have become important sources data for much scientific research. In the domain of land cover, crowdsourcing can provide a high temporal resolution data to support different analyses of landscape processes. However, the scientists may have little control over what gets recorded by the crowd, providing a potential source of error and uncertainty. This study compared analyses of crowdsourced land cover data that were contributed by different groups, based on nationality (labelled Gondor and Non-Gondor) and on domain experience (labelled Expert and Non-Expert). The analyses used a geographically weighted model to generate maps of land cover and compared the maps generated by the different groups. The results highlight the differences between the maps how specific land cover classes were under- and over-estimated. As crowdsourced data and citizen science are increasingly used to replace data collected under the designed experiment, this paper highlights the importance of considering between group variations and their impacts on the results of analyses. Critically, differences in the way that landscape features are conceptualised by different groups of contributors need to be considered when using crowdsourced data in formal scientific analyses. The discussion considers the potential for variation in crowdsourced data, the relativist nature of land cover and suggests a number of areas for future research. The key finding is that the veracity of citizen science data is not the critical issue per se. Rather, it is important to consider the impacts of differences in the semantics, affordances and functions associated with landscape features held by different groups of crowdsourced data contributors.

The United Kingdom is traditionally known for its wet climate, but droughts are also a recurring concern. Using newspapers as the medium of public communication, this study explores the timing and content of newspaper articles about droughts in England. We constructed a corpus of more than 800 newspaper articles related to droughts in England for the last 24 years (2000–2023) and analysed the temporal alignment of newspaper coverage with hydroclimatic anomalies and seasonality using a negative binomial regression model. Our results show that newspaper coverage of droughts coincides with the short-term shortage of precipitation (SPI-3) and groundwater (SGI-1) in spring/summer seasons, although temperature (CET-12) was not a significant factor. Using topic modelling, we explored narratives found in the texts such as “Drought and hosepipe ban”, a common measure used to restrict water usage in England during periods of hydrological drought. Comparing two major droughts (i.e. spring 2012 vs summer 2022), we found the summer drought of 2022 to contain more summer-related topics (i.e. “Heatwave”, “Temperature and hosepipe ban”), which underpins our statistical analysis, suggesting that warm seasons garner more media attention. Overall, our findings reveal that newspaper reporting on droughts is influenced by a combination of factors rather than precipitation alone, with a notable seasonality-based component that may reflect confirmation bias in reporting on droughts. This in turn implies a potential mismatch in how droughts are conceptualised by newspapers compared to scientists and may under-represent early signs of drought in cold seasons, potentially undermining public support for preventive measures at these times of year.

ContextIdentifying 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.ObjectivesWe explore and model the relationship between landscape ecological measures and experienced tranquillity extracted from user-generated text descriptions.MethodsGeoreferenced, 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.ResultsEvaluation 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.ConclusionsThis 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.

In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

In recent years, physics-based snowpack models combined with machine-learning techniques have gained momentum in public avalanche forecasting. When integrated with spatial interpolation methods, these approaches enable fully model-driven predictions of snowpack stability or avalanche danger at any location. This raises a key question: are such spatially detailed model predictions sufficiently accurate for operational use? We evaluated the performance of three spatially interpolated model-driven forecasts of snowpack stability and avalanche danger in Switzerland over three winters. As a benchmark, we used the official public avalanche-danger forecasts, specifically focusing on the forecast danger level that includes the sub-levels. We assessed the ability of both model and human forecasts to discriminate between reference distributions of conditions – typically not associated with avalanche activity – and actual avalanche events that were either naturally released or triggered by humans by calculating event ratios as proxies for release probability. Our results show that event ratios clearly increased with higher predicted avalanche probability, lower snowpack stability, or higher forecast sub-level. Overall, both model predictions and human forecasts showed a comparable ability to discriminate between reference and event conditions, with the event ratio increasing exponentially with increasing model-predicted probabilities or forecast sub-levels. However, the human forecasts – which incorporate model output – achieved a small but statistically significant advantage in discriminatory skill. This indicates that while the models alone have not yet reached the full discriminatory power of human forecasters, their performance is already approaching operational usefulness in a setup such as that used in Switzerland. As model quality is expected to improve further in coming years, it is essential to ensure optimal integration into the operational forecasting workflow to realize the full potential of model-based support. Further research should explore how to implement this effectively, how to integrate real-time avalanche occurrence data into model prediction pipelines, and how to validate increasingly high-resolution avalanche forecasts.

Backcountry skiing is a popular form of recreation in Switzerland and worldwide, yet little is known about where and when people venture outside and methods to monitor skiing behaviour are limited by the vast and remote nature of backcountry terrain. With avalanche fatalities documented each year, there is a need for spatially and temporally explicit information on the persons exposed to avalanche danger for effective risk estimations. To do so, we explored over 6800 user-generated GPS tracks and over 8 million clicks on a ski touring website to model backcountry skiing base rates on a daily scale in 126 regions in the Swiss Alps. We linked the data to weather, snow, temporal and environmental variables to train two different spatio-temporal prediction models based on the two data sources. We found that GPS and click data describe different types of behaviour (planning and real world behaviour), yet we could demonstrate that they correlate well with a 1 d time lag (ρ = 0.63), suggesting that online activity precedes actual skiing activity. Our results show that online and real-world behaviour are driven by similar underlying factors, with temporal aspects – such as weekends and the progression of the season – playing the most important role in both datasets. However, we found differences in how certain variables influenced behaviour: people tended to click on more routes in areas of high avalanche danger during more extreme weather conditions than they actually visited, and time spent on trip planning decreased as the season progressed. Our study demonstrates the potential of user-generated data sources to model skiing activity on regional and daily temporal scales, but also sheds light on specific limitations of the different data sources in approximating backcountry skiing activity.

ABSTRACT What cultural ecosystem services (CES) do people perceive in their immediate surroundings, and what sensory experiences are linked to these ecosystem services? And how are these CES and experiences expressed in natural language? In this study, we used data generated through a gamified application called Window Expeditions, where people uploaded short descriptions of landscapes they were able to experience through their windows during the COVID-19 pandemic. We used a combination of annotation, close reading and distant reading using natural language processing and graph analysis to extract CES and sensory experiences and link these to biophysical landscape elements. In total, 272 users contributed 373 descriptions in English across more than 40 countries. Of the cultural ecosystem services, recreation was the most prominently described, followed by heritage, identity and tranquility. Descriptions of sensory experiences focused on the visual but also included auditory experiences and touch and feel. Sensory experiences and cultural ecosystem services varied according to biophysical landscape elements, with, for example, animals being more associated with sound and touch/feel and heritage being more associated with moving objects and the built environment. Sentiments also varied across the senses, with the visual being more strongly associated with positive experiences than other senses. This study showed how a hybrid approach combining manual analysis and natural language processing can be productively applied to landscape descriptions generated by members of the public, and how CES on everyday lived landscapes can be extracted from such data sources.

Effective and efficient communication of expected avalanche conditions and danger to the public is of great importance, especially where the primary audience of forecasts are recreational, non-expert users. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal levels of avalanche danger and progressing through avalanche-prone locations and avalanche problems to a danger description. In many forecast products, information relating to the trigger required to release an avalanche, the frequency or number of potential triggering spots, and the expected avalanche size is described exclusively in a textual danger description. These danger descriptions are, however, the least standardized part of avalanche forecasts. Taking the perspective of the avalanche forecaster and focusing particularly on terms describing these three characterizing elements of avalanche danger, we investigate first which meaning forecasters assign to the text characterizing these elements and second how these descriptions relate to the forecast danger level. We analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalogue of phrases with a limited number of words. Words and phrases representing information describing these three elements were labeled and assigned to ordinal classes by Swiss avalanche forecasters. These classes were then related to avalanche danger. Forecasters were relatively consistent in assigning labels to words and phrases with Cohen's kappa values ranging from 0.64 to 0.87. Avalanche danger levels were also described consistently using words and phrases, with for example avalanche size classes increasing monotonically with avalanche danger. However, especially for danger level 2 (moderate), information about key elements of avalanche danger, for instance the frequency or number of potential triggering spots, was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are described in more detail than intermediate values, highlighting the difficulty of communicating conditions that are neither rare nor frequent or neither small nor large. Our results provide data-driven insights that could be used to refine the ways in which avalanche danger could be communicated. Furthermore, through the perspective of the semiotic triangle, relating a referent (the avalanche situation) through thought (the processing process) to symbols (the textual danger description), we provide an alternative starting point for future studies of avalanche forecast consistency and communication.