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Symbolizing tree canopy spread

June 12 2008 | 0 comments
Categories: Cartographic Effects, Cartographic Representations, Symbology

As part of an environmental assesment we record tree canopy spread in four directions (N,S,E,W), as a distance from the main tree trunk.

Would it be possible to visualise this as an off-set elipse around the tree centre point, either using symbology or by creating a new 'canopy' feature class?



Mapping Center Answer:

Cool question.  The answer is yes, but you may have to fuss with the solution a bit to tune to your liking.

First, the solution is going to use representation symbology--it's the only way to drive multiple aspects of a symbol with data.  So, your data will need to be stored in a geodatabase, and you will need 9.2.  The first step is to convert your symbols to representations (just use the default symbol; it will be entirely replaced with what you do in the representation symbol editor).

The idea I'm thinking of involves creating multiple point symbol layers in a representation. There will be one point symbol layer for each of your directions.  Create circle symbols (pick the color of green you want) for each of four symbol layers.  Each symbol layer will be offset in the direction of your directional fields (you may need to set the West and South fields to be zero minus the distance in order to get a negative offset).  The size and offset distance will be driven by your field values for each direction. 

If you just used your raw field values that will give you something that looks like a lobsided 4-leaf clover.  A couple of ideas for fixing that:  First, just add a fifth marker in the middle that uses one of the fields (if you've got a trunk diameter, that may be a good field to use as the basis for the central marker.  That may still look awkward, so the second idea is to add additional fields that would reduce the amount of offset using a linear function, which could be as simple as DR/2 (where DR = Directional radius).

Hopefully the fact that you will need to add a few more fields to make this work will be worth the effort--you've got a nice rich dataset and that in my mind sets you up well.

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