Building with Data: Bridging Architectural Design Practices and Information Visualization
Notice bibliographique
Résumé
Our work seeks to augment new information visualization research with strategies and workflows from the fields of design and architecture. To this end, this research explores how to adopt tools and methods that can integrate the best of physical and digital modalities to multiple contexts and scales in HCI and data visualization. Designing information visualization systems creates a need for a design approach that addresses and ties together two main threads – 1) how we as humans interact with and make sense of our environment and 2) how we as designers create meaning through geometry, form, and material encodings. While the research community within data visualization has primarily focused on screen-based data visualizations, there is now an opportunity to study how we can create insight with hybrid physical and digital representations of data through the lens of architectural practice. My colleagues and I have conducted this research at the intersection of model building, diagrams, and generative design, applying this knowledge to the design of multifaceted digital environments, from micro to macro scale, in two- and three- dimensional worlds. To develop this research, we first observe and characterize the architectural methods of model making and their potential to facilitate the design process of interactive systems. Next, we describe how physical hand-crafted and digitally fabricated models of different types assist in various stages of the design process. To illustrate how model building could support fluid exploration of multiple data sets, we built a 3D interactive campus model visualizing multiple layers of building-specific data. The system uses physical models as tangible tokens on an interactive touch surface, visualizing energy use and weather data daily over a two-year period. As an extension of our design, we developed a conceptual framework from this project to highlight the potential of physical models for supporting embodied exploration of spatial and non-spatial visualizations through fluid interaction. We then examine the use of diagrams in architecture and develop a conceptual framework based on the concept of data tectonics to organize and structure the design process of physical and immersive data systems. To further study the use of diagrams and generative design for data visualization, I collaborated with researchers at Tableau Software to develop a patented Tableau extension that self-generates and evolves up to thirty different design permutations at a time. The system randomly assigns a pre-specified palette of mark types to a chosen dataset giving designers the option of adding or deleting options that they deem promising. As a final project for this research, we brought the three principles of model making, diagramming, and generative design together to create a large-scale physical and immersive data visualization. In collaboration with the Department of Social Work at the University of Calgary, the project uses diagrams and generative design to prototype a series of three-dimensional encodings visualizing Global Gender Gap statistics from the World Economic Forum. The tent-like forms evoke sheltering structures that can be registered, experienced, and measured with the whole body. For this project, we applied the diagrammatic approach used in parametric design to traditional information visualization design principles and identified workflows that support rapid exploration and fabrication of multiple data design alternatives. There is no doubt that data and digital technologies, including machine learning and AI, will be part of our human fabric in the future, but what that looks like and how it is structured is still up to us. We need artists, and more diversity in general, in order to do this to the best of our potential as humans. In determining which practices encourage the creation of rich data-driven environments, this research underscores the fundamental need of humans to make sense of the world, inspiring designers to develop new spatial constructs that integrate both the art and science of the built environment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,007 | 0,031 |
| Communication savante | 0,028 | 0,032 |
| Science ouverte | 0,004 | 0,016 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».