We wanted to begin withĀ data embodiment; just as we process images differently than tables of numbers, we understand physical objects in their own way. What kind of object could we build to express data in a (literally) tangible way? We chose for this a topic that we feel is interesting on its own, the geographical distribution of economic activity through the world.
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Our first task was to locate a good source of data. Country-level information about economic activity is easy to find, but this obscures most of the interesting variation (think of the difference between the depths of the Amazonas and the area around SĆ£o Paulo), and more granular data isnāt available for all countries, nor necessarily comparable.
Luckily, thereās a well-known first order approximation used by economists, which is the amount of night illumination visible from space.Ā This tends to have a good correlation with economic activity, as it reflects levels of wealth as well as population sizes.Ā And NASA has precisely what we needed: eerily beautiful worldwide images of nighttime Earth from space, put together as if it was night on the whole planet
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To turn this image into a map of economic activity for each geographical coordinate, we used the Python libraryĀ PILĀ to read RGB values from one of the mid-resolution files, and then turned those into scalar brightness values with a weighted average. This left us with a NumPy array that was a direct encoding of the map we wanted (once you read array indexes as geographical coordinates)(...) you can read all the process here.
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