Local convex hull

Described in Getz and Wilmers, LoCoH (Local Convex Hulls) is a method for generating utilization distributions (homeranges) that finds the UD by following a number of simple steps:

1. Locate the k-1 nearest neighbors for each point in the dataset.
2. Construct a convex hull for each set of nearest neighbors and the original data point.
3. Merge these hulls together from smallest to largest.
4. Divide the merged hulls into isopleths where the 10% isopleth contains 10% of the original data points, the 100% isopleth contains all the points, etc.

The LoCoH method has a number of strong points:

• It generates a density distribution denoting.
• As more data is added, the homerange becomes more accurate.
• It is handles 'sharp' features such as lakes and fences well.
• The generated homerange has a finite region.

LoCoH has a number of implementations including a LoCoH Web Application.

LoCoH was formerly known as k-NNCH, for k-Nearest Neighbor Convex Hulls.

See also

• Homerange

References

Getz, W. and C. Wilmers. 2004. A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27: 489-505. View PDF Getz, W.M, S. Fortmann-Roe, P. C. Cross, A. J. Lyonsa, S. J. Ryan, C.C. Wilmers, in review. LoCoH: nonparametric kernel methods for constructing home ranges and utilization distributions. View PDF