It doesn't have to be for spatial data, that's just the easiest way to get N-dimensional data. Basically, every variable in your problem becomes a dimension. Y

It doesn't have to be for spatial data, that's just the easiest way to get N-dimensional data. Basically, every variable in your problem becomes a dimension. You could think of grocery shopping as a 10-dimensional problem involving cost, dietary restrictions, nutritional value (we'll say there are 7 for this example) and brand preference. You could potentially use a VP tree to search for all products that fall within a 10-D sphere that encodes your product tolerance.