Utilization distributions are constructed from data providing the location of an individual or several individuals in space at different points in time by associating a local distribution function with each point and then summing and normalizing these local distribution functions to obtain a distribution function that pertains to the data as a whole. If the local distribution function is a parametric distribution, such as a symmetric bivariate normal distribution then the method is referred to as a kernel method, but more correctly should be designated as a parametric kernel method. On the other hand, if the local kernel element associated with each point is a local convex polygon constructed from the point and its "k"-1 nearest neighbors, then the method is nonparametric and referred to as a "k"-LoCoH or "fixed point" LoCoH method. This is in contrast to "r"-LoCoH (fixed radius) and "a"-LoCoH (adaptive radius) methods.