Guide to Future-Present Archetypes Part 5: Schematic Maps

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UCSD robot mouse.

When attempting to map out the Future-Present, there is not just one map to consider; there are three. These three categorical types of map—our mental maps, symbolic maps, and broken maps--are each a schematic layer in our effort to perceive the world, and it is in their dissonance that the world actually exists. We must identify not only what these maps are, but what they are when they fail. In the fractures, one sees the spidering web of weaknesses, the many possible scenarios of rupture that select without warning. Reality is unpredictable, bursting from its constraining archetypes. And yet it is uncannily similar to all the breaks we’ve seen before, like a river delta resembling a tree.

The first category of map resides somewhere in the brain, perhaps in the hippocampus. It is through these networks that our neurology gives us a sense of space that we might try to express, record, and share with others. In studies performed on mice, “place fields” have been identified in their hippocampal neurons. Everytime the mouse passes through a particular known place in its terrain, a burst of action potential fires through the same neurons. We know less about the human brain, but it is clear that our hippocampus is important to forming memories, and that larger hippocampi correlate with people who have more detailed place knowledge, London cab drivers, for example. Somewhere, lurking inside the chemical differences between the inside and outside of neurons, in the minor voltages and in the ever-changing and evolving cell pattern of our neuroanatomy, is a material record of what we mean when we sense our geography. We cannot read this map— we can only think it. We express this map’s imperfections via our senses. When this map fails, we feel lost.


The second map is spoken aloud, in the possibility of uttering a symbolic map. Humans are never content at forming schema and just keeping them to themselves. Our schemas are meant to be shared, explained, inscribed, and signified. But the topology of these symbolic maps are as complicated and multifaceted as our neurology. It was Alfred Korzybski who constructed the phrase so relevant to our contemporary times, as the second part of a statement first spoken in 1931:

A) A map may have a structure similar or dissimilar to the structure of the territory...

B) A map is not the territory.

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The Universal Texture

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Images via Clement Valla.

These artists (...) counter the database, understood as a structure of dehumanized power, with the collection, as a form of idiosyncratic, unsystematic, and human memory. They collect what interests them, whatever they feel can and should be included in a meaning system. They describe, critique, and finally challenge the dynamics of the database, forcing it to evolve.1

I collect Google Earth images. I discovered them by accident, these particularly strange snapshots, where the illusion of a seamless and accurate representation of the Earth’s surface seems to break down. 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. They are an edge condition—an anomaly within the system, a nonstandard, an outlier, even, but not an error. These jarring moments expose how Google Earth works, focusing our attention on the software. They are seams which reveal a new model of seeing and of representing our world - as dynamic, ever-changing data from a myriad of different sources – endlessly combined, constantly updated, creating a seamless illusion.

3D Images like those in Google Earth are generated through a process called texture mapping.... 

 

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Mapping the Social

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The Internet, specifically social media, is often perpetuated as being a new kind of ‘revolution celebrity’ and indeed to some point its played a hefty distributive role in accelerating the 2011 Egyptian Revolution, Occupy and even the SOPA protests to name but a recent few. Yet, it simultaneously is this other exploitative entity, capitalizing on our movement through online space and constantly collecting data with often vague, ill-defined intentions.  Can social media’s two dynamic roles—both as a constructive social platform for anti-government efforts and a data aggregating system—be synthesized into a critical and valuable commons? Can personal user data collection be used for more than advertising and increased commodification?

Techno-sociologist, Zeynep Tufekci proposes that today, connection and friendship are moving from the ‘ascribed ties’ of inherited local relationships consisting of one’s neighborhood friends, family, etc. to ‘achieved ties’ or relationships located based on the shared affinities of people ‘with whom you interact using multiple means of communication’.  What can such shifts reveal about territorial and even regional interaction? Of neighborhoods, boroughs and its socio-economic behaviors? How can geography be re-defined?

The Livehood Research Project from the School of Computer Science at Carnegie Mellon University is potentially one example of how data collection can be used in a constructive, illuminating way, by demonstrating how place can be defined by social activity (maybe rather than by jurisdiction).  Livehood uses the data of over 18 million foursquare check-ins to map both geographic distance of frequented venues as well as plotting its ‘social distance’, or ‘the degree of overlap in the people that check-in to them’. Through accumulation of foursquare check-ins, Livehood algorithmically condenses this data into neighborhoods allowing a user to view the pattern sets of other people’s use of space.

Though the project in its current stages is still extremely limited (restricted so far to only three US cities, as well as accessible only to foursquare users) Livehood could develop into an extremely valuable tool for future governments and its citizens, as both a social lubricant and political tool. It also could just easily fulfill yet another advertiser’s dream.

 

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