Download Kernel Home Range Estimation for ArcGIS, using VBA and

using a fixed biweight kernel and no standardization, in ABODE (Figure 4.1.6.f.) (standardization
is explained in Section 4.5).
a
b
c
d
e
f
Figure 4.1.6. Progression of kernel density estimation for a real dataset.
This method of point-to-point evaluation, followed by pixel to point evaluation is a short cut, but
serves the same purpose as the more lengthy analysis at each and every pixel. The added
benefit of this procedure is that outliers in the dataset are effectively eliminated from the analysis
before the time consuming pixel to point analysis begins. Using a biweight kernel satisfies one of
the tenets espoused by Burt (1943) in his home range definition. In this case the occasional
sallies are essentially removed from the dataset, though it must be understood that this
discrimination is based solely on the spatial qualities of the distribution, and not on verified
dispersal or exploratory movements. It is important that the user understands how these outliers
are treated in different software packages. In one of the most commonly used packages
available at the moment (Figure 4.1.7.a.), pixels surrounding outliers are giving a density value,
albeit a very low value. When contouring at 95% of the volume of the density surface, this may
not be a problem, since the outliers will probably not be picked up. No testing has been done, to
see what the likelihood is of seeing significant differences at different percentage home ranges
(volume contouring). In the example shown, the commonly used estimator (AMAE) evaluates
pixels up to 4200m away, when the smoothing factor selected was 3000m. The contours begin at
3000m from the points. In Figure 4.1.7.b., ABODE evaluates only pixels for which their center is
within 3000m from the points, and only for those points that have other points within the search
radius. In this case, contours depend on the volume of the density surface, and may begin for
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