!DOGMA
!DOGMA is the Spatial Forces Think Tank Unit.
!DOGMA is primarily focused on expanding the design discourse through search, experimentation, prototyping, and methodology development. The Think Tank unit engages in groundwork for all other units and operates independently, diving into an array of topics, while focusing on [0] Flows, spatial structures and dynamics, [1] Biosensing, neuroscience, and emotional state analysis within the built environment, [2] Generative AI models for creative production, [3] Citizen Science, Spatial Equity, and participatory processes, and [4] Biocities and ecological design.
[0] Flows, spatial structures, and dynamics
Analyzing flows and spatial dynamics through data at various scales, urban, territorial, and planetary. The work lays a foundation for establishing contexts and narratives.
[1] Biosensing, neuroscience, and emotional state analysis within the built environment
Human metrics and spatial metrics are crucial for a science-based analysis and understanding of the impact the built environment has on our perception and experience. We deploy state-of-the-art methodologies and equipment to create workflows that streamline this relationship.
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[2] Generative AI Applications for Creative Production
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Generative AI is key in most of our workflows and developments within research, conceptualization, workflow augmentation, and beyond. Besides using AI in that way, we deploy new workflows to speculate on how AI can be used as a co-pilot, and how it can impact design processes.
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[3] Citizen Science, Spatial Equity, and participatory processes
Spatial Equity is at the heart of citizen science research carried out for better cities and a more neurodiverse urban ecosystem. We focus on inclusive mapping, empathy-based methods, and learning methodologies.
[4] Biocities and ecological design.
Studying the nature of future cities as ecological entities, as ecosystems. We work at the scales of materials, buildings, cities, and territories to analyze new ways of creating cities, and learn from existing models and data.
