Autosimulacrum

[2020]



Critical cartography is now commonplace in design education, illuminating the entrenched power dynamics embedded within maps. Accordingly, designers are embracing geospatial data as a means to visualize previously unexplored spatial dynamics. A noble intent, however, betrayed by stunted execution. Data is scattered, and when brought together produces a mostly static picture. Our discipline hungers to explore the speculative potential within data.

Autosimulacrum addresses this gap, computationally processing national-scale land cover and elevation datasets to create renderings of any site in the continental United States, from virtually any perspective.

The important component of this project is, paradoxically, obfuscated by the technique of representation. The potential of Autosimulacrum lies not in creating “realistic” images, but in the infrastructure to translate geospatial data into 3D objects visible in perspective. Textures, materials, vegetation, flood levels, air quality, and more can be visualized in any number of ways, creating new paradigms of representing the earth. This iteration is an early step.