More complicated forms of data include marked point sets and spatial time series.
The coordinate-wise mean of a point set is the centroid, which solves the same variational problem in the plane (or higher-dimensional Euclidean space) that the familiar average solves on the real line — that is, the centroid has the smallest possible average squared distance to all points in the set.
The trace, the determinant, and the largest eigenvalue of the covariance matrix can be used as measures of spatial dispersion.
A measure of spatial dispersion that is not based on the covariance matrix is the average distance between nearest neighbors.
A simple probability model for spatially homogeneous points is the Poisson process in the plane with constant intensity function.
Using Ripley's K function it can be determined whether points have a random, dispersed or clustered distribution pattern at a certain scale.