EvaVGI
https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5790
"Geovisual analysis of VGI for understanding people’s behaviour in relation to multi-faceted context"
Volunteered Geographic Information (VGI) in the form of actively and passively generated spatial content offers extensive potential for a wide range of applications. Realising this potential however requires methods which take account of the specific properties of such data, for example its heterogeneity, quality, subjectivity, spatial resolution and temporal relevance. The creation and production of such content through social media platforms is an expression of human behaviour, and as such influenced strongly by events and external context. In this project we will develop geovisual analysis methods which show how actors interact in LBSM, and how their interactions influence, and are influenced by, their physical and social environment and relations.2024-03-29T09:29:32ZSupplementary materials for the publication "Generative text-to-image diffusion for map creation based on geosocial media data."
https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5992
Supplementary materials for the publication "Generative text-to-image diffusion for map creation based on geosocial media data."
Dunkel, Alexander; Dunkel, Alexander; Gugulica, Madalina
Supporting Information for the publication "Generative text-to-image diffusion for map creation based on geosocial media data"(Release v1.0.0). Updates from the peer review process may not be reflected in this submission. The original git repository with the latest versions can be found at https://gitlab.hrz.tu-chemnitz.de/ad/mapnik_stablediffusion
The base data is from Stability AI, Stable Diffusion 1.5, which was trained on LAION-5B (Schuhmann et al. 2022), a dataset consisting of 5.85 billion images scraped from the web.
2023-01-01T00:00:00ZSupplementary materials for the publication "Event inventories: Estimating the temporality of landscape scenic resources from user-contributed geodata."
https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5930
Supplementary materials for the publication "Event inventories: Estimating the temporality of landscape scenic resources from user-contributed geodata."
Dunkel, Alexander; Burghardt, Dirk
Supporting Information for the conference publication
> Event inventories: Estimating the temporality of landscape scenic resources from user-contributed geodata.
Updates may have been made since submission to Opara. The original git repository can be found here:
https://gitlab.hrz.tu-chemnitz.de/ad/ephemeral_events
2023-01-01T00:00:00ZSupplementary materials for the publication "From sunrise to sunset: Exploring landscape preference through global reactions to ephemeral events captured in georeferenced social media"
https://opara.zih.tu-dresden.de/xmlui/handle/123456789/5793
Supplementary materials for the publication "From sunrise to sunset: Exploring landscape preference through global reactions to ephemeral events captured in georeferenced social media"
Dunkel, Alexander; Burghardt, Dirk; Hartmann, Maximilian; Ross, Purves; Eva, Hauthal
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.
The data encompasses both code (jupyter notebooks) and data (abstracted using hyperloglog).
Please see the git repository for any further information:
https://gitlab.vgiscience.de/ad/sunset-sunrise-paper
The workflow to load and process the data provided is available in Jupyter Notebooks or the respective HTML conversions of notebooks.
2023-01-01T00:00:00Z