Z-cluster tolerance and ranks

Feature classes that model terrain or buildingsthree dimensionally have a z-value representing elevation for each vertex. Justas you control how features are snapped horizontally with x,y cluster toleranceand ranks, if a topology has feature classes that model elevation, you cancontrol how coincident vertices are snapped vertically with the z clustertolerance and ranks.

 

The z cluster tolerance defines the minimumdifference in elevation, or z-value, between coincident vertices. Vertices withz-values that are within the z cluster tolerance are snapped together duringthe Validate Topology process.

 

If you're modeling city buildings, two buildings maybe adjacent to one another and appear to share a common edge in the x,y domain.If elevation values for building corners were collected using photogrammetry,you should be concerned about maintaining the relative height of each buildingstructure during the topology validation process. By setting the z clustertolerance to a value of zero, you can prevent z-values from clustering when youvalidate topology.

 

If you're modeling terrain, you may have datasetscollected with different x,y and z accuracies. In this case, you may want toset a z cluster tolerance greater than zero to allow snapping. To avoidz-values collected with a high level of accuracy snapping to z-values of loweraccuracy, you can assign each feature class a rank. Lower ranked features'z-values snap to the elevation of higher ranked vertices if they fall withinthe cluster tolerance. Z-values of vertices belonging to feature classes of thesame rank are averaged if they fall within the cluster tolerance.

 

The validate topology process averages and snapsz-values in such a way that each z-value adjusts by a total amount that is notmore than the z cluster tolerance. This causes z-values of vertices with thesame x,y to average or snap into groups.

For example, if the z cluster tolerance is 5,z-values of these six coincident vertices average into two groups, 11.25 and3.5:

n the following example, the coincident verticeshave different ranks, and the cluster tolerance is 5. Z-values average and snapinto three groups, 22.5, 7.5, and 1.25:

Z cluster tolerance values can range from zero tothe extent of the z domain (maximum z-value–minimum z-value).

Ranks are a relative measure of accuracy. Thedifference in rank of two feature classes is irrelevant, so ranking them 1 and2 is the same as ranking them 1 and 3 or 1 and 10.