Cloud Visions

Blurry photograph of people on beachfront

Cropped version of an image originally published in The Mail Online with the caption: Thousands of Britons ignored repeated warnings to stay home as part of ongoing efforts to clamp down on coronavirus by heading to DIY stores, parks and beaches on Sunday.

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In late March, some weeks into the pandemic, a new genre of local news story emerged: Local park Crowded with People Despite Social Distancing Orders. These stories usually struck a similar tone, describing how, while most people where behaving responsibly and staying indoors, a minority decided to flout the clear guidelines set out by the local government and gathered outdoors.

With these stories came a particular set of images, showing sunny paths and streets filled with seemingly oblivious people moving around in dangerous proximity. The real subject of these images is what isn’t there: distance between the bodies. Whatever empty space does remain visible in the image is being encroached from all sides by people walking, running, and cycling, bleeding in and out of focus.

Soon after these images appeared, people began to question how well they reflected the reality on the ground. Their composition seemed too similar (we’re always looking along the path from an elevated position, never across), and their optical artifacts (the condensed perspective and narrow depth of field of a long telephoto lens) too pronounced to be accidental.

Even as Twitter users advanced this point by comparing the media photographs with satellite images of the area1, the answer to the original question — are the people in the image keeping to the two metre distance? — remained elusive. All we have is a distorted slice of reality, blurred not only by the telephoto lens, but also the shimmering summer air, the JPEG algorithm, and the conflicting narratives surrounding them.

But suppose we had a some way of accurately measuring the distance between people in these images: What good would that information be, anyway? The two-metre line doesn’t represent a physical boundary (airborne particles don’t suddenly stop once they reach it) but a statistical one: at two meters, your risk of infection is low enough to improve public health. The real droplet-cloud has no boundary: you and me feed it everytime we exhale, it interacts with the built environment in complex ways, phasing in and out of existence. You’re always already enveloped by it. The cloud is not only physical but also also epistemological, spreading maddening uncertainty wherever the wind blows it.2

As an illustration of this condition, the blurred, distorted, compressed images of parks and seafront parks take on new meaning. In them, people, the heated athmosphere, and the built environment melt together into a single, ever-moving, amorphous body – this is the cloud, made visible.

The cloud is a terrifying entitity, escaping our attempts at classyfing and understanding it since the beginning of such efforts in the 18th century. But according to the architect Eyal Weizman, the ephemeral nature of the cloud also forms the basis of its civic potential. Because the cloud doesn’t stop at national borders or the threshold of buildings, it has the potential to create a political space that equally reaches across existing divisions. Everyone who is enveloped by the cloud becomes an inhabitant of this new space: a citizen of the cloud.

Read from this perspective, the images of crowded bodies in bright sunlight loose nothing of their subtle terror. But perhaps in their blurryness, a glimmer of hope for a new, more inclusive political community may be found. 3

  1. Joey D’Urso (2020), Here’s Why Some Pictures Of People Supposedly Breaking Coronavirus Social Distancing Rules Can Be Misleading. In Buzzfeed News, available at buzzfeed.com/joeydurso/coronavirus-social-distancing-lockdown-photos ↩︎

  2. Eyal Weizman (2017), Forensic Architecture: Violence at the Threshold of Detectability, p 193. Zone Books. ↩︎

  3. This text is also published on Medium. An extended version is forthcoming in Content Full ↩︎