. 2007 Feb 14;2(2):e207. doi: 10.1371/journal.pone.0000207

Table 3. Comparison of the areas in km² estimated for the four buffalo GPS collar sets of data (n points) by the 95% and 100% isopleths for nonparametric (LoCoH) and parametric kernel^a methods.

Collar	n	HR	k-LoCoH	r-LoCoH	a-LoCoH		Kernel
parameter			(k ₁)	(r ₁)	(a ₁)	(â)
T07	27	95%	190	268	166	173	244
	89	100%	289 (53)	321 (3475)	236 (32383)	253 (44850)
T13	25	95%	96	114	89	95	142
	72	100%	238 (51)	144 (1470)	211 (28156)	224 (35000)
T15	28	95%	126	146	128	153	121
	46	100%	276 (53)	156 (1555)	205 (35684)	257 (72000)
T16	16	95%	56	46	55	55	84
	75	100%	118 (41)	49 (678)	90 (23401)	90 (23401)

For the k, r, and a-LoCoH methods the parameters used, as described in the text, are the heuristic values k ₁, r ₁, and a ₁ and the MSHC value â (given in parentheses).

Implemented in Animal Movement Extension for ArcView 3.x [29] using Silverman's ad-hoc method for selecting the smoothing parameter h [14].

Table 3. Comparison of the areas in km2 estimated for the four buffalo GPS collar sets of data (n points) by the 95% and 100% isopleths for nonparametric (LoCoH) and parametric kernela methods.

Table 3. Comparison of the areas in km² estimated for the four buffalo GPS collar sets of data (n points) by the 95% and 100% isopleths for nonparametric (LoCoH) and parametric kernel^a methods.