LoCoH is a powerful family of algorithms for finding homeranges or utilization distributions. The LoCoH algorithms work by creating convex hulls around each point in the data set and then iteratively joining these hulls together from smallest to largest into isopleths. The 10% isopleth contains 10% of the points, the 100% isopleth 100% encompasses all the points. The smaller the hull, the more heavily used the region. Therefore, these isopleths can be used to determine how frequently a region is used.
There are current three LoCoH algorithms:
- Fixed k LoCoH Fixed k LoCoH works by creating hulls from the (k-1) nearest neighbors to the root point. Also known as k-NNCH, Fixed k LoCoH was originally described in Getz and Wilmers (2004).
- Fixed r LoCoH Fixed r LoCoH operates by generating local hulls from all the points within r distance from the root point. This method is very similar to Fixed Kernel Methods.
- Adaptive LoCoH Adaptive LoCoH creates hulls from the maximum number of nearest neighbors to a point such that the sum of their distances from the root point is less than or equal to a distance measure a.
Compared to other methods for constructing homeranges (such as Minimum Convex Polygon and kernel methods), the LoCoH methods have several advantages that make them particularly well-suited for landscapes with 'sharp' features such as lakes, fences, or steep terrain. These type of landscape features often result in spatial distributions containing holes, sharp boundaries, corners, or corridors. In these cases, the LoCoH density isopleths have been shown to better approximate the true area represented by the data than kernel or alpha-hull methods. LoCoH isopleths also have the property of converging to the true area represented by the data as the number of data points increases, thus the method is particularly well suited when there is a lot of observational data (e.g., from a GPS collar).
This web-based version of LoCoH allows you to explore the k-NNCH method with your own point data. The number of points that can be analyzed via this web-based version of LoCoH is limited due to memory limits on the server. However if you get an 'out of memory' error message you can download the R script to run it on your own PC. LoCoH is also available as toolbox for ArcGIS 9.x.
An in review journal article comparing and contrasting the three LoCoH algorithms in addition to kernel algorithms is Getz Et al, 2007.