The coronavirus disease 2019 (COVID-19) crisis resulted in unprecedented changes in the spatial mobility of people across societies due to the restrictions imposed. This also resulted in unexpected mobility and population dynamics that created a challenge for crisis preparedness, including the mobility from cities during the crisis due to the underlying phenomenon of multi-local living. People changing their residences can spread the virus between regions and create situations in which health and emergency services are not prepared for the population increase. Here, our focus is on urban–rural mobility and the influence of multi-local living on population dynamics in Finland during the COVID-19 crisis in 2020. Results, based on three mobile phone datasets, showed a significant drop in inter-municipal mobility and a shift in the presence of people—a population decline in urban centres and an increase in rural areas, which is strongly correlated to secondary housing. This study highlights the need to improve crisis preparedness by: (1) acknowledging the growing importance of multi-local living, and (2) improving the use of novel data sources for monitoring population dynamics and mobility. Mobile phone data products have enormous potential, but attention should be paid to the varying methodologies and their possible impact on analysis.

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user‐generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state‐of‐the‐art computer‐vision methods to studying human–nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human–nature interactions can be extracted from user‐generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer‐vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.

The cross-border practices of people beyond migration and tourism are often overlooked. However, the increase of frequent social interactions and mobilities of people crossing country borders for work, shopping, services and leisure contribute to (re)shaping functional cross-border regions. We propose a conceptual framework using a big data approach to provide new insights from the individual-level cross-border mobility of people. We used Twitter data as a promising transnational data source to empirically examine who, when and where the borders are crossed in the case of Luxembourg – one of the busiest cross-border regions in Europe. Special attention has been given to cross-border commuters. We evaluated our findings to demonstrate the feasibility of the proposed approach as the proof-of-concept for cross-border research. We mapped prospects and challenges to improve the operationalization of the proposed approach relevant to cross-border research and shared our source code to encourage further method development. Finally, we have highlighted how this information from social media data can benefit research and practice for policy and planning in cross-border regions.

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP_0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model’s ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP_0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP_0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R² of 0.98 for variable cattle herd densities. These results highlight that a model with high AP_0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing.

This article explores the spatiotemporal and affective qualities of linguistic landscapes at three linguistically diverse neighborhoods in Helsinki, Finland. The three sites were selected for qualitative fieldwork using a method that combines social media and population registry data with quantitative measures of diversity and spatial analytics. The article demonstrates how each site is characterized by its own distinct affective atmosphere, which is discursively construed and made sense of through references to other places and times. The timespace analysis of affects provides conceptual and methodological tools that allow analyzing the change and circulation of social meanings in the landscape, as well as differences in these aspects across settings.

Twitter is a popular social media platform for scholarly research, because the user-generated content on the platform can also include geographic and temporal information. We collect a corpus of 38 million Twitter messages with two million geographical coordinates to map the languages used across Finland at the level of regions and municipalities. To cope with the high volume of social media data, we use automatic language identification and place of residence detection. We estimate the linguistic richness and diversity of users and locations using measures developed within ecology and information sciences. The analyses reveal a rich, multilingual environment that varies geographically and temporally, particularly between coastal, rural and urban areas. The results, which underline the mutual benefits of collaboration between linguists and geographers, provide a more fine-grained, accurate and comprehensive view of the languages used on Twitter in Finland than previously available.