A classic application of a self-organizing map is in combination with heatmaps.Ī self-organizing map is an artificial neural network that learns to represent multi-dimensional data on a (usually 2D) map. Thanks to the amazing thing that is general purpose computing, and thanks to a particularly clever algorithm by the Finnish academician Teuvo Kohonen, we can leave the work to the machines. … UNLESS YOU HAVE THINKING MACHINES THAT CAN DO THE WORK FOR YOU. Enter Teuvo Kohonenīut even when we limit our goals and recognize that the map can’t be perfect, it’s very hard to create a suitable map for even a few stars, and virtually impossible to do so for thousands of them… We are trying to minimize the distortion but we can’t ever hope to get rid of it completely. Note also what isn’t our goal here: perfect representation of 3D space on a 2D map. If star C is three times farther away from star B than star A in space, we want to see the same thing on the map.See what stars are neighbouring any given star.See if star is solitary (no close neighbours).It is possible to make basic observations about the depicted stars at a glance, with a reasonable level of certainty.Instances of outright lies (for example, Sirius and Mirzam rendered next to each other) are minimized.It’s obvious that any 2D map of 3D space will be imperfect. So again, similarly to 3D virtual atlases, with 2D views it’s not easy to do basic things at a glance. Two adjancent stars are often actually quite far from each other – but the viewer doesn’t know this until after they have read that information. In this respect they are almost as bad a representation of reality as the night sky. They show each star at its proper X and Y coordinates, but they completely discard the Z (depth) coordinate. The problem is that the currently available 2D maps are views. Chung’s maps being probably the best examples. There are 2D star maps already, of course – Winchell D. At a glance, we can see clusters, gaps, strings, and so on. It’s very easy for us humans to see that two cities are close to each other, for example. 4 Even simple things like trying to find clusters of stars are extremely difficult.Ĭompare this to a traditional (2D) map. Humans are not good at navigating through true three-dimensional space. 3 Digital Universe does an excellent job in letting you navigate the universe in 3D – but it's still very hard to do things that would be simple on a 2D map.Įven if you have a 3D atlas of stars which allows you to travel freely through virtual space, like Digital Universe or Redshift, that experience is no less confusing. Is Sirius close to Mirzam? They are on the night sky (Mirzam is a bright star right next to Sirius) but they are most definitely not in 3D space (Sirius is 9 light years away, Mirzam is 500). The three dimensions are super confusing.
The reason is that most people (including writers, screenwriters and game masters) don’t actually have any idea what the topology of our star neighbourhood is like. It’s all planet this and star system that, and how many parsecs between them.īut notice one thing: those places are either completely made up 1 or they are random stars taken from our night sky without any context.
Our sci-fi books, movies and games are filled with exploration of the galaxy and the universe at large.