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Coding the Logic of Physical Places

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Tue, 2012-10-09 22:58 — isha

As anyone with iOS6 knows, we have been taking our map application for granted. It's clearly not as simple as it looks. This weekend, I came across an article that threw light upon why Google Maps has such a solid system-- and unsurprisingly, it relates to principles of information organization. Apparently Google has a separate, more complex, "deep map" (hidden from public view) that lays out the logic of the physical places mapped in the front-end application we interact with. And they're been iterating on it for years.

Here's how Google Maps brings the physical world into a digital space.

First of all, there is the task of making sure the digital data of roads and highways matches up with satellite imagery. This image shows the small errors in this matching process-- places where digital information doesn't quite line up with the physical space. Problems like these are fixed manually.

Next is the process of utilizing data produced by the Street View project. Google teams actually go out an drive through all accessible roads, and on each of these drives they produce two types of data: first, proof that the routes can actually be taken; and second, the photographs.

Currently, Google can run algorithms that extract street signs from the Street View photographs and paste them into the underlying "deep map." Humans then sort through all this information to develop code that translates the semantic meaning of, say a "one way" road sign, so that computers can duplicate the judgment of a person driving on that road. People are doing the grunt work of turning information about a physical space into digitally retrievable data, but the hope is that as optical character recognition (OCR) improves, Google will be able to automatically recognize and index any words visible from Street View photographs into its compendium of information about the physical world.

The goal is to take our mental maps and turn them into indexable, retrievable data. I guess if anyone will find a way to bridge semantic, sensory, and proliferation gaps, it'll probably be Google. But until a computer program can seamlessly render semantics, Google has thousands of employees to sift through physical data and turn human perspicacity into code.