Hyperbolic paraboloid constructed with scattered data points

May 4, 2005  |  Edward Tufte
16 Comment(s)

I need a 3-D image of a saddle, a hyperbolic paraboloid, the shape of which is defined by a rough scatter of data points lying on its surfaces. In turn the 3-D scatter should be projected on the 3 surrounding 2-dimensional planes making up the box around the saddle. Then the points lying in the 3 2-spaces should be projected to univariate sparklines, those sparklines folded to link the plane-pairs will also serve as the tripod axes of the 3-space.

 image1

The 3-D saddle (made from data points, not too many I hope) floats within this box.

The idea is that the 2-D and 1-D projections fail to provide sufficient information to identify the 3-D hyperbolic paraboloid! Note that our saddle is not a surface, it consists of just enough scattered data points over the surface to suggest a saddle shape. So this is not about mathematical surfaces but rather about data scatters.

Has this or similar been done and where can I pick up such an illustration? I recall an example of a 3-D letter made from dots surrounded by 3 2-D planes whose projected points from the 3-D object don’t give away the 3-D shape.

Surely this has been done already in MacSpin or Statview or a similar 3-D data analysis tool. After all, this is the fundamental logic about why we should do multiviariate analysis. If necessary we can construct the sparkline axes.

Might a Kindly Contributor find a good example or perhaps construct one? This is for the sparkline chapter but I’d like also to slip in the idea about n-1 dimensional projections of data points not giving full information n-space data point activities, exactly the problem of A. Square living in Abbott’s Flatland.

Thanks,
ET

Topics: E.T.
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