如何产生组合网络图和虚构地图的可视化？ [英] How can I produce visualizations combining network graphs and imaginary maps?
问题描述
基本上，我正在寻找类似这个令人敬畏的研究项目：描述了支持GMap的算法。如果你听说过Voronoi图（摇滚，但让我的头受伤），这篇文章是给你的。我在Calc 1之后退出了，所以我希望避免记住什么是sigmas和epsilons。
你能做一个简单的最近点排序算法吗？所以它看起来像这样：你有你的力量定向布局，并计算了一些边界框。现在你想渲染它。调整您的边界框，以符合原点，然后计算每个像素的颜色，找到最接近的点。这应该产生一些类似的区域，应该很简单。当然，它不会像GMap那么漂亮，但也许是开始吗？运行时会很糟糕，但是...我不知道你，但计算边界线对我来说听起来要难得多。
Basically, I'm looking for something like this awesome research project: Gmap, which was referenced in this related SO question.
It's a rather novel data visualization that combines a network graph with an imaginary set of regions that looks like a map. Basically, the map-ification helps humans comprehend the enormous data set better.
Cool, huh? GMap doesn't appear to be open source, though I plan to contact the authors.
I already know how to create a network graph with a force-directed layout (currently using Prefuse/Flare), so an answer could be a way to layer a mapping algorithm on top of an existing graph. I'm also not concerned about the client-side at all right now - this would be a backend process, and I am flexible about technology stack and data output at this stage.
There's also this paper that describes the algorithm backing GMap. If you have heard of Voronoi diagrams (which rock, but make my head hurt), this paper is for you. I quit after Calc 1, though, so I'm hoping to avoid remembering what sigmas and epsilons are.
As a start, could you do a simple closest point sort of an algorithm? So it looks something like this: You have your force directed layout and have computed some sort of bounding box. Now you want to render it. Adjust your bounding box to line up to the origin and then as you calculate the color of each pixel, find it's closest point. This should generate some semblance of regions and should be quite simple to try out. Of course, it isn't going to be as pretty as GMap, but maybe a start? The runtime would be awful, but... I don't know about you but computing boundary lines directly sounds a lot harder to me.
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