What does "Rigorous sigma zero value is low" warning mean?

[DrewDowling]

Any time I run a Consistency Check I get this warning.

I think I understand in theory what the warning is but I've no idea how to fix it.

I have tried to change the Direction and Distance accuracy values to NULL in the COGO lines that I'm running the check on but it makes no difference.

[ChristineLeslie]

Hi Drew

Rather than set the accuracies to NULL, I would decrease their values - ie make the accuracies higher. See this excerpt from the doc below, which gives an example:

If the Rigorous Sigma Zero value is significantly less than 1, the estimated a priori accuracies (or weights) may be too low, meaning that the numeric accuracy values may be too large. This also means that some or all the measurements can be given higher weights based on the statistics. For parcel data, this can often indicate that the COGO dimensions were calculated from the line shape geometry rather than entered from the original parcel record.

The tool displays a warning message to indicate that the direction and distance values are unusually good for the given distance and direction accuracy values. To achieve a Rigorous Sigma Zero value closer to 1, increase the estimated accuracies (give them a higher weight) by decreasing the numeric values. For example, decrease a 30 seconds accuracy estimate for directions to 5 seconds to give it higher weight, and run the adjustment again.

If the Rigorous Sigma Zero value is larger than 1, the estimated a priori accuracies (or weights) may be too high, meaning that the numeric accuracy values may be too small. To improve the result, first use the adjustment feature classes to identify and correct any measurements that are flagged as outliers. Then run the adjustment again. If there are no measurements flagged as outliers, decrease the estimated accuracies (increase the numeric values). For example, increase a 0.59-foot estimate for distances to 0.8 feet.

Christine

[DrewDowling]

@ChristineLeslieThanks for the reply. I'm running the tool on a new subdivision with good survey data. So I would expect the values to be accurate.

That said my cogo skills are poor and I can see a particular traverse where the survey distance doesn't match ST_Length by approx 10 feet.

Despite this no outlier is created after running the Consistency check or the Weighted adjustment tools. . In this case I should manually enter a distance accuracy of 10ft? So then connecting lines where ST_Length and survey length do match should I enter the smaller mismatch, e.g. .25'?

Would it not be better to automatically calculate the distance accuracy value by getting the difference of the survey and ST_Lenght values?

[ChristineLeslie · Accepted solution]

Hi 

The consistency check is run on the entered COGO dimensions. So if a particular COGO dimension is not consistent with other COGO dimensions, it will be flagged as an outlier. So consistency check is is checking COGO dimensios against other COGO dimensions and not against the shape_length. 

I think it will only look at shapelength if the COGO values on the line are null.  Because of that I wouldnt be using shape_length to get accuracy values. I would increase the accuracy values until you get Rigorous Sigma Zero value closer to 1. So you may have to run the check a few times, trying different accuracies - but it sounds like they do need to be high because the data is survey correct.

If an  entered dimension is wrong by 10 feet (either a typo from data entry or an actual typo on the document) - the consistency check should pick this up. 

To detect COGO mismatches with shape length - you can use the configured data quality layer -COGO mismatch.

https://pro.arcgis.com/en/pro-app/latest/help/data/parcel-editing/parcelfabricdataqualitylayers.htm#...

Christine

[DrewDowling]

@ChristineLeslie 

Ahh that's making more sense. In my head I was conflating the "distance mismatch" data quality layer and the "Adjustment Lines Distance" layer under Analysis.

Thank you for clearing it up.