I have been looking for a way to try and make the output of the Kernel Density function in ArcGIS for Desktop make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound?
Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?

EDIT: Just wanted to reference How to interpret GRASS v.kernel results? as the method for checking the values of the output is beneficial. This helps to clarify how these values are computed and provides a way to validate results which helped me to understand the assigned values.

I have been looking for a way to try and make the output of the Kernel Density function in ArcGIS for Desktop make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound?
Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?

EDIT: Just wanted to reference How to interpret GRASS v.kernel results? as the method for checking the values of the output is beneficial. This helps to clarify how these values are computed and provides a way to validate results which helped me to understand the assigned values.

I have been looking for a way to try and make the output of the Kernel Density function in ArcGIS for Desktop make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound?
Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?

I have been looking for a way to try and make the output of the Kernel Density function in ArcGIS for Desktop make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound?
Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?

EDIT: Just wanted to reference How to interpret GRASS v.kernel results? as the method for checking the values of the output is beneficial. This helps to clarify how these values are computed and provides a way to validate results which helped me to understand the assigned values.

I have been looking for a way to try and make the output of the kernel density function in ArcMap make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound? Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?

I have been looking for a way to try and make the output of the Kernel Density function in ArcGIS for Desktop make a little more sense to our administration.

I ran the function on a sample dataset with 500 points, and the 'density' where only 1 point was within the search radius was given a value of 6.2342 at the peak. I then divided the entire raster by this value.

The scale is now at least in relation to the peak value found at one point.

Is this methodology sound?
Has anyone found better ways to label the scale that go beyond 'more dense, less dense'?