The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or volunteered geographic information (VGI). Key challenges with such “opportunistic” data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

The exponential growth of user-contributed data provides a comprehensive basis for assessing collective perceptions of landscape change. A variety of possible public data sources exist, such as geospatial data from social media or Volunteered Geographic Information (VGI). Key challenges with such ‘opportunistic’ data sampling are variability in platform popularity and bias due to changing user groups and contribution rules. In this study, we use five case studies to demonstrate how intra- and inter-dataset comparisons can help to assess the temporality of landscape scenic resources, such as identifying seasonal characteristics for a given area, or testing hypotheses about shifting popularity trends observed in the field. By focusing on the consistency and reproducibility of temporal patterns for selected scenic resources and comparisons across different dimensions of data, we aim to contribute to the development of systematic methods for disentangling the perceived impact of events and trends from other technological and social phenomena included in the data. The proposed techniques may help to draw attention to overlooked or underestimated patterns of landscape change, fill in missing data between periodic surveys, or corroborate and support field observations. Despite limitations, the results already provide a comprehensive basis for developing indicators with a high degree of timeliness for monitoring perceived landscape change over time.

The state of generative AI has taken a leap forward with the availability of open source diffusion models. Here, we demonstrate an integrated workflow that uses text-to-image stable diffusion at its core to automatically generate icon maps such as for the area of the Großer Garten, a tourist hotspot in Dresden, Germany. The workflow is based on the aggregation of geosocial media data from Twitter, Flickr, Instagram and iNaturalist. This data are used to create diffusion prompts to account for the collective attribution of meaning and importance by the population in map generation. Specifically, we contribute methods for simplifying the variety of contexts communicated on social media through spatial clustering and semantic filtering for use in prompts, and then demonstrate how this human-contributed baseline data can be used in prompt engineering to automatically generate icon maps. Replacing labels on maps with expressive graphics has the general advantage of reaching a broader audience, such as children and other illiterate groups. For example, the resulting maps can be used to inform tourists of all backgrounds about important activities, points of interest, and landmarks without the need for translation. Several challenges are identified and possible future optimizations are described for different steps of the process. The code and data are fully provided and shared in several Jupyter notebooks, allowing for transparent replication of the workflow and adoption to other domains or datasets.

Volunteered Geographic Information in the form of actively and passively generated spatial content offers great potential to study people’s activities, emotional perceptions, and mobility behavior. Realizing this potential requires methods which take into account the specific properties of such data, for example, its heterogeneity, subjectivity, and spatial resolution but also temporal relevance and bias. The aim of the chapter is to show how insights into human behavior can be gained from location-based social media and movement data using visual analysis methods. A conceptual behavioral model is introduced that summarizes people’s reactions under the influence of one or more events. In addition, influencing factors are described using a context model, which makes it possible to analyze visitation and mobility patterns with regard to spatial, temporal, and thematic-attribute changes. Selected generic methods are presented, such as extended time curves and the co-bridge metaphor to perform comparative analysis along time axes. Furthermore, it is shown that emojis can be used as contextual indicants to analyze sentiment and emotions in relation to events and locations. Application-oriented workflows are presented for activity analysis in the field of urban and landscape planning. It is shown how location-based social media can be used to obtain information about landscape objects that are collectively perceived as valuable and worth preserving. The mobility behavior of people is analyzed using the example of multivariate time series from football data. Therefore, topic modeling and pattern analyzes were utilized to identify average positions and area of movements of the football teams.