Getting started
A point layer must be loaded into ArcMap in order to use this tool. If you wish to use point weights in the kernel density estimate,
ensure that the attribute table contains a numerical field with a weight for each point. Note that a weight of 1 is neutral, and a
weight of 0 effectively eliminates the point from the dataset (the point contributes nothing to the density estimate). Negative
weights will result in nonsensical results or errors during processing.
Output folder
As this tool can generate many different output layers, the user is asked to specify an output folder (preferably a new, empty folder).
An empty folder will ensure that the program does not encounter naming conflicts with preexisting data layers.
Output raster
The extent of the output raster is determined automatically as: the extent of point data + the smoothing factor. This tool completely
ignores any Spatial Analyst options you may have set (this tool is independent from Spatial Analyst). The output raster name should
be 14 characters or less, and not contain any special characters. The output is a floating point Grid. It is highly recommended that you
apply a scaling factor to preserve the precision of the density estimate (see below).
Smoothing factor
The smoothing factor (also referred to as the bandwidth or h statistic) is what controls how smoothed the kernel density estimate is. There are
a number of ways of determining what the smoothing factor should be. Two objective approaches include the href estimate, and leastsquares
cross validation (LSCV). The appropriateness of these estimates depends to a large degree on the nature of your data. There is some evidence that
neither of these estimates perform particularly well. In most of the applications I deal with, an estimate of the smoothing factor based on
expert biological knowledge and careful inspection of the resulting kernel density estimate is often the best approach.
Scaling factor
Kernel density estimates often produce very small numbers, e.g. 0.000000147. The scaling factor simply multiplies these small values by
a constant (e.g. 1000000). It is usually very wise to use a scaling factor, especially because Grids only permit the storage of single precision
floating point numbers. If you do not use a scaling factor, you are likely to lose a great deal of precision in the kernel density estimate as a
result of the density values being truncated. The important thing to note about the scaling factor is that the relative values in the output cells
are the same, it is simply the units of the density estimate that change. There is therefore no downside to using a scaling factor and it is
highly recommended that you do so. (The scaling factor will not affect the percent volume contours either).
Percent volume contours
Note that a percent volume contour is not the same as the simple contours that are typically produced in tools like Spatial Analyst. A percent
volume contour represents the boundary of the area that contains x% of the volume of a probability density distribution. A simple
contour (like the ones that are produced in Spatial Analyst) represent only the boundary of a specific value of the raster data, and does not
in any way relate to probability. For applications like animal home range delineation it is the percent volume contour that is required. The 95%
volume contour would therefore on average contain 95% of the points that were used to generate the kernel density estimate.
