The simplest way to draw the boundaries of a home range from a set of location data is to construct the smallest possible convex polygon around the data. This approach is referred to as the minimum convex polygon (MCP) method which is still widely employed, but has many drawbacks including often overestimating the size of home ranges.
The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel methods. This group of methods is part of a more general group of parametric kernel methods that employ distributions other than the normal distribution as the kernel elements associated with each point in the set of location data.
Recently, the kernel approach to constructing utilization distributions was extended to include a number of nonparametric methods such as the Burgman and Fox's alpha-hull method. and Getz and Wilmers local convex hull (LoCoh) method This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods.
Although, currently, more software is available to implement parametric than nonparametric methods (because the latter approach is newer), the cited papers by Getz et al. demonstrate that LoCoH methods generally provide more accurate estimates of home range sizes and have better convergence properties as sample size increases than parametric kernel methods.
Computer packages for implementing parametric and nonparametric kernel methods are available online..