Graph all the things
analyzing all the things you forgot to wonder about
We can interpolate between points on a sphere, meaning that, given the value of a function (such as temperature) at various points on a sphere (such as Earth), we can estimate the temperature between those points. For instance, here is an interpolation based on the temperatures in Bangalore, Beijing, and Moscow today, where orange is warm and blue is cold:
From geometry, we can derive an elegant method to do this to a region on a sphere. Using graph theory and enough points, we can extend this to the whole sphere (in the next post).
Here's the temperature today in a few cities:
You want to estimate the temperature in Islamabad using this information - you're a bit crazy like that. You probably already know how to do linear interpolation on the temperature between two points on a one-dimensional line: if you know and , then you can estimate for by
But how would you interpolate the temperature between these cities? On a surface like the Earth, the answer isn't so obvious. Not only are we working in two dimensions (latitude and longitude), but the Earth is also curved. I'll show a beautiful way to perform a "linear" interpolation on a sphere.
We can generalize our result on the line to two dimensions. Let's say you know the temperature at points and want to estimate it at within triangle :
We want to estimate the temperature at by for some coefficient with ; some mixture of the temperatures. It actually easiest to think of this in vector form, shifting the origin to be where is:
Intuitively, the coefficients for our mixture of temperatures should satisfy , leaving . This seems simple enough, but there is a more convenient way to write this in terms of the areas of triangles in the following figure:
Using our vectors from before, the area of is
Here we have positioned the plane at the base of the hemisphere so that the projected point is the center of the sphere and the origin. At this juncture, one might despair because actually calculating the projection and evaluating areas from it would be tedious. But fear not: there will be a deus ex machina, and that machina is the determinant. If we take three vectors such as (in 3-space, drawn from ) and stuff them into a matrix (as its columns), the determinant of that matrix is very special. Its magnitude is where is the volume of the tetrahedron formed by and . Its sign indicates whether are in counterclockwise order or not, which is a useful fact I will get to in my next blog post.
Anyway, the volume of a tetrahedron is actually extremely useful. You may recall from geometry class that where is the base's area and is the height. From its projection onto the plane, we can choose and , we get
The actual temperature there is 15C, which is pretty close (lucky) considering that temperature can vary unpredictably across just a few hundred kilometers. To accurately estimate the temperature using this method, we would need to measure it it many more points; apparently NOAA uses something like 1500 weather stations around the globe. By the time I finished writing this, temperatures had changed slightly, but here's a real heat map of Asia:
I'll show one way to interpolate with very many known points in my next post.