What's a good measure of spread in a multidimensional space?
In a single dimension variance would be the measure I need, but in a multidimensional space I need more than just variances. Note that in a single dimension, I need something different than the range covered by the data points.
Consider a hypercube representing the space of all possible data points (all attributes are limited to values in the interval $(0,1)$). I need a measure of spread which is optimal when all corners of the cube are equally populated (and there are no other data points within the cube). When only two opposed corners would be populated, all variances would be maximal, but this is not what I need. Also a population which covers all corners, but also contains points more toward the middle of the cube should have a lower spread.
My first hunch would be to sum up all values on the main diagonal of the covariance matrix, and substract all other values (all proper covariances). This idea is very ad hoc, however, and I don't know whether this is thinking in the right direction.
Please help me find a good measure of spread/variance in a multidimensional space.