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sanfrancisko:

(Salva López)
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Great work here: http://jorgetaboada.com/altadensidad.html
Also: Tumblr of note: http://architectureofdoom.tumblr.com/
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My work in the ‘O’ group show at Sarah Cottier Gallery, Paddington, Australia.
The data that defines the shape of the mountain range is the cyclic repetition of the peaks and troughs of Google searches for ‘draft’ from 2004-2010.
The show [ran] from July 6 – 30, 2011.


 (via O « Jonathan Zawada)

My work in the ‘O’ group show at Sarah Cottier Gallery, Paddington, Australia.

The data that defines the shape of the mountain range is the cyclic repetition of the peaks and troughs of Google searches for ‘draft’ from 2004-2010.

The show [ran] from July 6 – 30, 2011.

 (via O « Jonathan Zawada)

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Andrew Moore: His 2008-2009 “Detroit Disassembled” photo series is the subject of an exhibition on view through February 13, 2013 at the National Building Museum in Washington, D.C. (running concurrently is “Detroit Is No Dry Bones,” a show of photos Camilo José Vergara). In this photo, Moore captures the Surrealist afterlife of a clock that once measured the days of students at Detroit’s Cass Tech High School.

Ruins fiction??
(via Friday Photo: Dalí in Detroit? - UnBeige)

Andrew Moore: His 2008-2009 “Detroit Disassembled” photo series is the subject of an exhibition on view through February 13, 2013 at the National Building Museum in Washington, D.C. (running concurrently is “Detroit Is No Dry Bones,” a show of photos Camilo José Vergara). In this photo, Moore captures the Surrealist afterlife of a clock that once measured the days of students at Detroit’s Cass Tech High School.

Ruins fiction??

(via Friday Photo: Dalí in Detroit? - UnBeige)

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thingsmagazine:

Photography by Alex MacLean. See the new book, ‘Up on the Roof: New York’s Hidden Skyline Spaces’ (Princeton Architectural Press)
Photoset

prostheticknowledge:

Deconstructed Cities by Tim White Sobieski 

[More Here]

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The city has been dismembered, dissected block by block, the blocks then categorized, sorted and stacked by shape.

(via Krulwich Wonders: Odd Things Happen When You Chop Up Cities And Stack Them Sideways - Radiolab)

The city has been dismembered, dissected block by block, the blocks then categorized, sorted and stacked by shape.

(via Krulwich Wonders: Odd Things Happen When You Chop Up Cities And Stack Them Sideways - Radiolab)

Tags: Maps Visuals NYC
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Looking at hundreds of architecture photographs we noticed a strong bias regarding image composition. Perspective foreshortening and vanishing lines dominate the overall impression of the image. This realisation lead to experiments in automated extraction of said features in the image data. The medium used for the result – for now a printed image – is just one of the many possibilities. The method also bears the potential for further experimentation and can be considered a work in progress.

(via extracts of local distance)

Looking at hundreds of architecture photographs we noticed a strong bias regarding image composition. Perspective foreshortening and vanishing lines dominate the overall impression of the image. This realisation lead to experiments in automated extraction of said features in the image data. The medium used for the result – for now a printed image – is just one of the many possibilities. The method also bears the potential for further experimentation and can be considered a work in progress.

(via extracts of local distance)

Tags: Visuals
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I was Google Earth-ing, when I noticed that a striking number of buildings looked like they were upside down. I could tell there were two competing visual inputs here —the 3D model that formed the surface of the earth, and the mapping of the aerial photography; they didn’t match up. Depth cues in the aerial photographs, like shadows and lighting, were not aligning with the depth cues of the 3D model.The competing visual inputs I had noticed produced some exceptional imagery, and I began to find more and start a collection.  At first, I thought they were glitches, or errors in the algorithm, but looking closer, I realized the situation was actually more interesting — these images are not glitches. They are the absolute logical result of the system.
…
Google Earth’s textures however, are not shallow or flat. They are photographs that we look through into a space represented beyond—a space our brain interprets as having three dimensions and depth. We see space in the aerial photographs because of light and shadows and because of our prior knowledge of experienced space. When these photographs get distorted and stretched across the 3D topography of the earth, we are both looking at the distorted picture plane, and through the same picture plane at the space depicted in the texture. In other words, we are looking at two spaces simultaneously. Most of the time this doubling of spaces in Google Earth goes unnoticed, but sometimes the two spaces are so different, that things look strange, vertiginous, or plain wrong. But they’re not wrong.
…
Our mechanical processes for creating images have habituated us into thinking in terms of snapshots - discrete segments of time and space (no matter how close together those discrete segments get, we still count in frames per second and image aspect ratios). But Google is thinking in continuity. The images produced by Google Earth are quite unlike a photograph that bears an indexical relationship to a given space at a given time. Rather, they are hybrid images, a patchwork of two-dimensional photographic data and three-dimensional topographic data extracted from a slew of sources, data-mined, pre-processed, blended and merged in real-time. Google Earth is essentially a database disguised as a photographic representation.
…
it is precisely because humans did not directly create these images that they are so fascinating. They are created by an algorithm that finds nothing wrong in these moments.
…

Clement Valla:  Rhizome | The Universal Texture

I was Google Earth-ing, when I noticed that a striking number of buildings looked like they were upside down. I could tell there were two competing visual inputs here —the 3D model that formed the surface of the earth, and the mapping of the aerial photography; they didn’t match up. Depth cues in the aerial photographs, like shadows and lighting, were not aligning with the depth cues of the 3D model.

The competing visual inputs I had noticed produced some exceptional imagery, and I began to find more and start a collection.  At first, I thought they were glitches, or errors in the algorithm, but looking closer, I realized the situation was actually more interesting — these images are not glitches. They are the absolute logical result of the system.

Google Earth’s textures however, are not shallow or flat. They are photographs that we look through into a space represented beyond—a space our brain interprets as having three dimensions and depth. We see space in the aerial photographs because of light and shadows and because of our prior knowledge of experienced space. When these photographs get distorted and stretched across the 3D topography of the earth, we are both looking at the distorted picture plane, and through the same picture plane at the space depicted in the texture. In other words, we are looking at two spaces simultaneously. Most of the time this doubling of spaces in Google Earth goes unnoticed, but sometimes the two spaces are so different, that things look strange, vertiginous, or plain wrong. But they’re not wrong.

Our mechanical processes for creating images have habituated us into thinking in terms of snapshots - discrete segments of time and space (no matter how close together those discrete segments get, we still count in frames per second and image aspect ratios). But Google is thinking in continuity. The images produced by Google Earth are quite unlike a photograph that bears an indexical relationship to a given space at a given time. Rather, they are hybrid images, a patchwork of two-dimensional photographic data and three-dimensional topographic data extracted from a slew of sources, data-mined, pre-processed, blended and merged in real-time. Google Earth is essentially a database disguised as a photographic representation.

it is precisely because humans did not directly create these images that they are so fascinating. They are created by an algorithm that finds nothing wrong in these moments.

Clement Valla:  Rhizome | The Universal Texture

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pergoogle:

“Main Street,” Google Image search by Rob Walker, July 5, 2012

pergoogle:

“Main Street,” Google Image search by Rob Walker, July 5, 2012

Tags: Cities Visuals