Summer 2012 Edition
By Mike Price, Entrada/San Juan, Inc.
This article as a PDF.
Because ArcMap typically draws points in ascending table order, when displaying a complex point set that has closely placed points, low-grade samples often cover nearby high-grade samples. To properly view and interpret the high-grade data often requires zooming in to rather large scales.
To begin, save the completed Battle Mountain map document as Battle_Mountain02.mxd and remove all geodatabase point data.
One solution was to sort the data in descending order, save the sorted dataset as a new table, and repost the points as an event theme. Although this works, it requires separate sorts for all elements. If new points are added, all changed datasets must be re-sorted and reimported.
Symbol Levels, available in ArcGIS for Desktop 10 and 9.x, helps solve this problem. This layer property lets you override the default drawing order of features in ArcMap and control the drawing order and position in relation to other symbols. To see how Symbol Levels can help you draw low-grade points first and high-grade points last, use the data from the completed exercise described in "Prospecting for Gold: Building, mapping, and charting point geochemical data" to re-create the levels used in the tutorial using the workflow described here.
To get started, save the completed Battle Mountain map document as Battle_Mountain02.mxd and remove all geodatabase point data.
Notice that many high-grade gold samples appear to be merged with lower-grade orange and yellow points. Even the larger size of the high-grade points does not make them easy to see at this scale. To properly view these clustered points, zoom in until they appear as unique features—probably a scale larger than 1:25,000 to eliminate overlaps. You could set a reference scale now, but as you zoom back out, points become too small to be meaningful, so try Symbol Levels.
The secret to building and applying symbol levels is to create a logical, well-designed legend for each point set. If you classify point or polyline data using Categories or Quantities, select a style that will separate your data into meaningful groups.
Because the gold geochemistry represents continuous numeric data with an approximate log-normal distribution, a graduated color and symbol legend works well. Define breaks that divide the population into similar-sized groups or bins and place the most important bin at one end of the range. In the Battle Mountain exercise, gold data had seven breaks at 50, 100, 200, 500, 1,000, and 2,000 parts per billion (ppb). Because the high value points (those over 2,000 ppb) are spread throughout the dataset, they are often hidden by lower values that post later as the layer is drawn. Symbol Levels can be used to float these high-grade values to the top of the drawing order.
It is easy to apply Symbol Levels to a well-designed legend, and it certainly contributes to colorful, informative maps. Import and apply symbol levels to the other soil geochemistry data used in the exercise.