Mountains from Maps

Let's talk mappings.

Layering is the main graphical problem when trying to show a landscape. Physical features, information, densities, and locations exist in the same place in a typology. We have to find a way to show them together. In this map of la Jolla, California, I've used three hatches that can be seen over each other. The park hatch and roads are transparent so they can be seen through the other information and the information can be seen through them. Its reasonably legible.

This is my attempt at combining all the information into one language. Sea and parks are near-black to show access to nature. High median income is darker to show affluence. Low population density is darker to show ideal living conditions. The "story" of the map is how these can relate and congregate into high-income, low-population, nature-accessing areas.

Its a little hard to see, so we add a blur effect to see an overall picture.

Now lets talk height maps.

Height maps are images that use value to indicate the height of a vertex. Black areas will be at the lowest point and white at the highest point. After the mesh is formed to the height map, a column can be automatically placed at each vertex. Then a material samples the color from each part of the image and applies that color to a corresponding column. With height maps you can turn the information from an image into a landscape.

Here's the non-blurred La Jolla mapping as a height map.

From there we can experiment with columns applied at the vertices with different scales, columns, and specificity.

And now we render.

We have an effective, scalable, 3D landscape in model format. Only one step left.

The format is easily 3D printable, with almost no editing between the model and the makerbot. A couple hours and $10 worth of plastic and we get our own 3D representation of the information we want to represent.

The execution of a precedent is not as important as how repeatable the process is. With this project I've laid the road for future 3D representations of informative landscapes.