Spatial Computing - Laura Kurgan, Adam Vosburgh, and e-flux Architecture - Editorial


Laura Kurgan, Adam Vosburgh, and e-flux Architecture

Aram Bartholl, Map, 2019. Courtesy of the artist. Photo: Kulturbüro Arnsberg.

Spatial Computing
June 2024

Spatial Computing is a collaboration between e-flux Architecture and the M.S. in Computational Design Practices Program (MSCDP) at the Graduate School of Architecture, Planning, and Preservation (GSAPP) at Columbia University, featuring contributions by Dare Brawley, Catherine Griffiths, Laura Kurgan, Sam Lavigne and Tega Brain, Erin McElroy, Lai Yi Ohlsen, Mimi Ọnụọha, and Lucia Rebolino.

The distinction between the digital and the physical is only becoming increasingly blurry. It’s difficult to imagine getting through a day without the assistance of a data-rich device, and not just in the major population centers of the Global North. These “extensions of man” feel fully grafted onto us, pretty much everywhere and at all times.1 No wonder: we live in a world in which so much depends on and is designed for computability. Sensors are overhead, underfoot, on and even in our bodies, feeding data from our work and play to machines that process it, send it back to us, and then on to others. This flow, and the equipment that generates it, is part of the contemporary design environment. Furthermore, it has effectively rewritten the meaning of computation, which is no longer just the ability to calculate, but has also become a process that integrates data with space and remakes the world.

“Spatial computing” is a term that was only added to Wikipedia in January 2024, in response to Apple’s introduction of a new mixed reality device, which they refer to as a “spatial computer.”2 The accompanying “spatial operating system” is said to usher in “the era of spatial computing,” and “seamlessly blends digital content to your physical space.”3 But do we not already live in an era of spatial computing? There are already so many devices and practices engaged in rendering data spatial. Geographic Information System (GIS) software allows geo-coded data visualization, generally on a map. The Global Positioning System’s (GPS) constellation of satellites enables real time navigation and location services for moving objects, from missiles to cars to stolen phones. Remote sensing uses sensors and cameras on aircraft and satellites to collect visual and other data about the earth at multiple and increasingly high resolutions.4 Even old-fashioned surveys (like the census) still generate mountains of spatial data, as do sensors embedded in everything from refrigerators to roads, not to mention the prodigious outputs of tracking software encoded into digital platforms. Overlaid onto and utilizing these systems, a plethora of spatial practices and industries have grown up to produce (and predict) more and more data about places on the planet—what’s happening where, when, and with whom—and to automate its generation and processing.

The politics, boundaries, and control systems built into spatial technologies are computationally designed to be invisible and are very difficult to exit. Approaching spatial computing critically might thus help us understand where and how things, people, animals, plants, and movements are tied to these bigger, less visible systems—ones that are perhaps not “seen” on the map but are guided by it. A central irony of spatial computing is that there is now effectively no outside to computation. In the late 1990s, Gayatri Spivak drew a distinction between the planet and the globe and argued that “The globe is on our computers. […] No one lives there.”5 Today, however, we might indeed live there, so it has become crucial to decode and recode the world that computation has enabled.

If there is no outside, conscious computation must move beyond technological determinism, the black box, and the dream of “liberation” from data and the map. The task at hand is to introduce the unknowable, uncertain, serendipitous, diverse—which is to say, wisdom, rather than data—into computational design.6 Taking on this difficult task, counter-computational spatial practices engage with the methods of spatial computing to challenge and propose alternatives to what is typically created by the very tools, infrastructures, or media they are using.


Marshall McLuhan, Understanding Media: The Extensions of Man (1964; Cambridge: MIT Press, 1994).


Wikipedia, s.v. “Spatial computing,” .


Apple Vision Pro, .


The term “spatial computing” has been used by Shashi Shekhar and Pamela Vold in their book Spatial Computing (Cambridge: MIT Press, 2020), xi. For a critical evaluation of the same spatial technologies, see Laura Kurgan’s Close Up at a Distance (New York: Zone Books, 2013), 38-54.


Gayatri Chakravorty Spivak, Imperatives to Re-Imagine the Planet (Vienna: Passagen Verlag, 1999), 44.


See Toni Morrison, The Source of Self-Regard: Selected Essays, Speeches, and Meditations (New York: Alfred A. Knopf, 2019), 34.

Spatial Computing is a collaboration with the M.S. in Computational Design Practices Program (MSCDP) at the Graduate School of Architecture, Planning, and Preservation (GSAPP) at Columbia University.

Architecture, Urbanism, Technology, Data & Information, Design
Infrastructure, Maps
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