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Christofer Meinecke

Publications and source records attributed to Christofer Meinecke.

8 recordsLinked to original sources
arXiv2026

Visualizing Colonial Regimes: A Multi-View Approach to (Historical) Political Transformation

This paper takes a critical approach to visualization of colonial regimes. Drawing on critical hermeneutics and postcolonial theory, we created interactive visu- alizations revealing the constructed nature of colonial categories and exposing the hierarchies between empires and territories. Using the Varieties of Political Regimes dataset, our approach combines temporal flow visualization and geo- graphic distribution mapping. This allows us to contextualize colonial rule within broader patterns of political change. Our visualization challenges conventional, static, and isolated representations of colonial data by making the interpre- tive frameworks underlying colonial categorization visible through coordinated temporal, spatial, and relational views.

cs.HC↗
arXiv2025

Cluster-Based Random Forest Visualization and Interpretation

Random forests are a machine learning method used to automatically classify datasets and consist of a multitude of decision trees. While these random forests often have higher performance and generalize better than a single decision tree, they are also harder to interpret. This paper presents a visualization method and system to increase interpretability of random forests. We cluster similar trees which enables users to interpret how the model performs in general without needing to analyze each individual decision tree in detail, or interpret an oversimplified summary of the full forest. To meaningfully cluster the decision trees, we introduce a new distance metric that takes into account both the decision rules as well as the predictions of a pair of decision trees. We also propose two new visualization methods that visualize both clustered and individual decision trees: (1) The Feature Plot, which visualizes the topological position of features in the decision trees, and (2) the Rule Plot, which visualizes the decision rules of the decision trees. We demonstrate the efficacy of our approach through a case study on the "Glass" dataset, which is a relatively complex standard machine learning dataset, as well as a small user study.

cs.LG↗
arXiv2025

Quantitative Analysis of Objects in Prisoner Artworks

Prisoners of Nazi concentration camps created paintings as a means to express their daily life experiences and feelings. Several thousand such paintings exist, but a quantitative analysis of them has not been carried out. We created an extensive dataset of 1,939 Holocaust prisoner artworks, and we employed an object detection framework that found 19,377 objects within these artworks. To support the quantitative and qualitative analysis of the art collection and its objects, we have developed an intuitive and interactive dashboard to promote a deeper engagement with these visual testimonies. The dashboard features various visual interfaces, e.g., a word cloud showing the detected objects and a map of artwork origins, and options for filtering. We presented the interface to domain experts, whose feedback highlights the dashboard's intuitiveness and potential for both quantitative and qualitative analysis while also providing relevant suggestions for improvement. Our project demonstrates the benefit of digital methods such as machine learning and visual analytics for Holocaust remembrance and educational purposes.

cs.HC↗
arXiv2024

Is Medieval Distant Viewing Possible? : Extending and Enriching Annotation of Legacy Image Collections using Visual Analytics

Distant viewing approaches have typically used image datasets close to the contemporary image data used to train machine learning models. To work with images from other historical periods requires expert annotated data, and the quality of labels is crucial for the quality of results. Especially when working with cultural heritage collections that contain myriad uncertainties, annotating data, or re-annotating, legacy data is an arduous task. In this paper, we describe working with two pre-annotated sets of medieval manuscript images that exhibit conflicting and overlapping metadata. Since a manual reconciliation of the two legacy ontologies would be very expensive, we aim (1) to create a more uniform set of descriptive labels to serve as a "bridge" in the combined dataset, and (2) to establish a high quality hierarchical classification that can be used as a valuable input for subsequent supervised machine learning. To achieve these goals, we developed visualization and interaction mechanisms, enabling medievalists to combine, regularize and extend the vocabulary used to describe these, and other cognate, image datasets. The visual interfaces provide experts an overview of relationships in the data going beyond the sum total of the metadata. Word and image embeddings as well as co-occurrences of labels across the datasets, enable batch re-annotation of images, recommendation of label candidates and support composing a hierarchical classification of labels.

cs.CV↗
arXiv2023

$n$ Walks in the Fictional Woods

This paper presents a novel exploration of the interaction between generative AI models, visualization, and narrative generation processes, using OpenAI's GPT as a case study. We look at the question "Where Does Generativeness Comes From", which has a simple answer at the intersection of many domains. Drawing on Umberto Eco's "Six Walks in the Fictional Woods", we engender a speculative, transdisciplinary scientific narrative using ChatGPT in different roles: as an information repository, a ghost writer, a scientific coach, among others. The paper is written as a piling of plateaus where the titling of each (sub-)section, the "teaser" images, the headers, and a biblock of text are strata forming a narrative about narratives. To enrich our exposition, we present a visualization prototype to analyze storyboarded narratives, and extensive conversations with ChatGPT. Each link to a ChatGPT conversation is an experiment on writing where we try to use different plugins and techniques to investigate the topics that, ultimately form the content of this portable document file. Our visualization uses a dataset of stories with scene descriptions, textual descriptions of scenes (both generated by ChatGPT), and images (generated by Stable Diffusion using scene descriptions as prompts). We employ a simple graph-node diagram to try to make a "forest of narratives" visible, an example of a vis4gen application that can be used to analyze the output of Large Languange + Image Models.

cs.HC↗
arXiv2023

An Interactive Decision Support System for Analyzing Time Related Restrictions in Renaturation and Redevelopment Planning Projects

The operation of open-cast lignite mines is a large intervention in nature, making the areas uninhabitable even after closing the mines without renaturation processes. Renaturation of these large areas requires a regional planning process which is tied to many conditions and restrictions, such as environmental protection laws. The related information is available only as unstructured text in a variety of documents. Associated temporal aspects and the geographical borders to these textual information have to be linked manually so far. This process is highly time-consuming, error-prone, and tedious. Therefore, the knowledge of experts is often used, but this does not necessarily include all the relevant information. In this paper, we present a system to support the experts in decision-making of urban planning, renaturation, and redevelopment projects. The system allows to plan new projects, while considering spatial and temporal restrictions extracted from text documents. With this, our presented system can also be used to verify compliance with certain legal regulations, such as nature conservation laws.

cs.HC↗
arXiv2022

Labeling of Cultural Heritage Collections on the Intersection of Visual Analytics and Digital Humanities

Engaging in interdisciplinary projects on the intersection between visualization and humanities research can be a challenging endeavor. Challenges can be finding valuable outcomes for both domains, or how to apply state-of-the-art visual analytics methods like supervised machine learning algorithms. We discuss these challenges when working with cultural heritage data. Further, there is a gap in applying these methods to intangible heritage. To give a reflection on some interdisciplinary projects, we present three case studies focusing on the labeling of cultural heritage collections, the problems and challenges with the data, the participatory design process, and takeaways for the visualization scholars from these collaborations.

cs.HC↗