STAR*NET V6 - Circe

Chapter 8 Analysis of Adjustment Output
Here are a few suggested steps that you can take to determine the source of adjustment
problems. As you gain experience, these steps will become second nature, and you will
often be able to sense the cause of the problems without having to proceed in a step by
step fashion.
Remember too that Starplus Software will try to help you resolve problems. We do
request that you attempt to resolve them by following these steps before you call,
because the more experience you develop in analyzing your adjustments, the better you
will be able to handle new situations in the future.
1. Review the Error File, and resolve all errors and warnings that may be present.
2. Check all your option settings. For local (non-grid) jobs, do you have the “Default
Scheme” set to match your controlling coordinates? Are the proper values entered?
For grid jobs, is your zone set correctly, and are your entered controlling coordinates
consistent with that zone? In all cases, are your observations consistent with the units
set in your Project Options?
3. Make sure that all Input Observations are summarized in the listing file. Review the
values for any obvious blunders and their standard errors to see if they look
reasonable.
4. If you are using traverse data, select the “Traverse Closures of Unadjusted
Observations” listing option, and review the approximate traverse closures.
5. Run Data Check Only so that a plot file is generated from the input data. View the
network graphically, looking for obvious gaps and blunders in the data. Misnaming
of stations will become obvious here, because you may see the misconnections
across the network.
6. Check that the “Angle Station Order” and “Coordinate Order” options are set
correctly – an incorrect setting for either option will cause real problems!
7. Review the Statistical Summary carefully. See whether there is one data type that has
a much worse Error Factor than the others. If so, look carefully at the input
observations and residuals for that data type.
8. Review the listing of observations and residuals. Look for single residuals that have
standardized residuals larger than 3.0, and have been tagged with a “*”. Try to
determine the cause for the large residuals. Remember that a single gross error can
cause large residuals in a number of observations. Always start by looking at the
largest one or two residuals in each data type. Look for a pattern of the same station
being associated with multiple large residuals.
9. Check your input standard errors. Are they reasonable, and in line with your normal
field techniques? Have you included Centering Error values when you have an
adjustment with a lot of short sights?
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