There are two types of adjustments that you can run on a parcel fabric: a consistency check and a weighted least-squares. The type of adjustment you run depends on whether you are checking dimensions for mistakes or you are evaluating and improving the spatial accuracy of your parcels.

In the parcel fabric, you can run a least-squares adjustment on parcels using the Analyze By Parcel Least Squares Adjustment and Apply Parcel Least Squares Adjustment geoprocessing tools.

Use the Analyze By Parcel Least Squares Adjustment tool to run a least-squares adjustment and store the results in adjustment feature classes. Least-squares adjustment results are stored in adjustment feature classes for visualization and analysis purposes. The original parcel fabric features are not altered by this tool.

In the case of a weighted least-squares adjustment, if the results in the adjustment feature classes are acceptable, you can adjust the original parcel fabric features by running the Apply Parcel Least Squares Adjustment tool to apply the results of the adjustment to the parcel fabric line and point features.

##### Note:

When a large number of parcels (more than 10,000) is used as input to the Analyze By Parcel Least Squares Adjustment tool, it will use a large amount of disk space, and you may experience long processing times. When the tool runs, disk space is allocated and used in the system's temp directory.

## Run a consistency check

A consistency check uses a least-squares adjustment to check parcel line dimensions for mistakes and outliers. For example, you can run a consistency check on a subdivision entered from a new record to check for any mistakes on the parcel line dimensions. Constrained or weighted points (control points) are not required in a consistency check and if points are constrained or weighted, they will be treated as free points.

A consistency check should be run on small subsets of parcels, such as parcels entered from a record, and is run for analysis purposes only. The results of a consistency check should not be applied to the parcel fabric.

To run a consistency check, follow these steps:

- Set a priori accuracies for parcel line dimensions.
A priori accuracies are specified in the Direction Accuracy and Distance Accuracy fields of the parcel line or connection line feature class. If the fields are missing accuracies, the defaults of 30 seconds for directions and 0.15 meters (0.49 feet) for distances are used.

##### Note:

A higher value (lower accuracy) in the accuracy fields gives a measurement a greater allowable range, and therefore a lower influence on the adjusted coordinate positions when compared with a measurement that has a lower value (higher accuracy) in the accuracy fields. A higher value in the accuracy fields means a lower weight in the adjustment network, and conversely, a lower value in the accuracy fields means a higher weight. - Open the Analyze By Parcel Least Squares Adjustment geoprocessing tool and select your input parcels or lines.
You must select connection lines to use them as input to a least-squares adjustment.

##### Tip:

Both parcels and lines can be used as input. Selecting lines only can result in slightly faster processing times, as the least-squares adjustment engine does not have to extract lines from the selected parcels.##### Tip:

Use the Parcel Features command in the Selection group on the Parcel Record Workflows tab to select the lines and points of a parcel selection.

- Choose Consistency check for Analysis Type.
- Specify a convergence tolerance or accept the default value.
The least-squares adjustment engine runs the adjustment iteratively until the solution converges. For each iteration, the adjusted point coordinates from the previous iteration are used. A solution is converging when the coordinate shifts (corrections) become smaller after each iteration. The solution is considered converged when the maximum coordinate shift is less than the specified convergence tolerance.

- Run the tool to run the consistency check on the input parcels.
The least-squares adjustment engine populates the adjustment feature classes with the results of the adjustment. The adjustment feature classes are automatically added to the map, and the layers are grouped under Analysis in the table of contents.

##### Note:

The adjustment feature classes are overwritten with a new set of results each time a least-squares adjustment is run. - Assess the Standard Error (Sigma) value and make changes if necessary.
- After the tool runs, click View Details at the bottom of the Geoprocessing pane to view the summary report of the least-squares adjustment.
- Expand Messages to view the results.
The Standard Error (Sigma) value is a broad indicator that provides a statistical assessment of overall reliability of the least-squares adjustment. It is often used as a guide for determining how well the a priori accuracy values have been estimated. For well-estimated a priori accuracy values, the Standard Error (Sigma) value is expected to be close to a value of 1.

If the Standard Error (Sigma) 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 Standard Error (Sigma) 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 Standard Error (Sigma) 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.

- Analyze the results of the least-squares adjustment using the adjustment feature classes that have been added to the map.
- If you made edits to line dimensions, run the consistency check again to ensure that there are no other outliers or unreliable dimensions.

## Run a weighted least-squares adjustment

A weighted least-squares adjustment uses parcel line dimensions and control points to compute updated and more accurate coordinates for parcel fabric points. A minimum of two control points (points with known x,y coordinates) are required for a weighted least-squares adjustment. Control points can be completely constrained (do not move in the adjustment) or weighted (some movement allowed based on accuracy).

To run a weighted least-squares adjustment, follow these steps:

- Set a priori accuracies for parcel line dimensions.
A priori accuracies are specified in the Direction Accuracy and Distance Accuracy fields of the parcel line or connection line feature class. If the fields are missing accuracies, the defaults of 30 seconds for directions and 0.15 meters (0.49 feet) for distances are used.

##### Note:

A larger value (lower accuracy) in the accuracy fields gives a measurement a greater allowable range, and therefore a lower influence on the adjusted coordinate positions when compared with a measurement that has a smaller value (higher accuracy) in the accuracy fields. This means that a larger value in the accuracy fields is correlated with a lower weight in the adjustment network, and conversely, that a smaller value in the accuracy fields is correlated with a higher weight. - Define the weighted or constrained control points to be used in the least-squares adjustment.
Control points can be completely constrained or weighted by accuracy.

- To set a control point as constrained, set the Adjustment Constraint attribute in the parcel fabric Points feature class to XYZ Constrained. Constrained control points always have an accuracy of 5 millimeters.
- To set a weighted control point, set the Adjustment Constraint attribute in the parcel fabric Points feature class to XY free, Z Constrained and add an a priori accuracy estimate to the XY Accuracy field.

- Open the Analyze By Parcel Least Squares Adjustment geoprocessing tool and select your input parcels or lines.
You must select connection lines to use them as input to a least-squares adjustment.

##### Tip:

Both parcels and lines can be used as input. Selecting only lines can result in slightly faster processing times, as the least-squares adjustment engine does not have to extract lines from the selected parcels. - Choose Weighted least squares for Analysis Type.
- Specify a convergence tolerance or accept the default value.
The least-squares adjustment engine runs the adjustment iteratively until the solution converges. For each iteration, the adjusted point coordinates from the previous iteration are used. A solution is converging when the coordinate shifts (corrections) become smaller after each iteration. The solution is considered converged when the maximum coordinate shift is less than the specified convergence tolerance.

- Run the tool to run a weighted least-squares adjustment on the input parcels.
The least-squares adjustment engine populates the adjustment feature classes with the results of the adjustment. The adjustment feature classes are automatically added to the map, and the layers are grouped under Analysis in the table of contents.

- Assess the Standard Error (Sigma) value, and make changes if necessary.
- After the tool runs, click View Details at the bottom of the Geoprocessing pane to view the summary report of the least-squares analysis.
- Expand Messages to view the results.
The Standard Error (Sigma) value is a broad indicator that provides a statistical assessment of overall reliability of the least-squares adjustment. It is often used as a guide for determining how well the a priori accuracy values have been estimated. For well-estimated a priori accuracy values, the Standard Error (Sigma) value is expected to be close to a value of 1.

If the Standard Error (Sigma) 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 a higher weight 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.

There are no practical benefits in changing accuracy values, running the least-squares adjustment again, and applying the results to your fabric points. However, if you want to achieve a Standard Error (Sigma) value closer to 1, increase the estimated accuracies (provide 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 Standard Error (Sigma) 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, use the adjustment feature classes to identify and correct dimensions that are flagged as outliers and 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.

- Analyze the results of the least-squares analysis using the adjustment feature classes that have been added to the map.
When running a weighted least-squares adjustment, analyze the results stored in both the Adjustment Lines and Adjustment Points layers.

- If you made edits to line dimensions or control points, run the adjustment again to ensure that there are no other outliers or unreliable dimensions.
- If the results of the least-squares adjustment are acceptable, open the Apply Parcel Least Squares Adjustment geoprocessing tool.
No selected parcels or parcel lines are necessary, as the parcel fabric will be updated with the results stored in the adjustment feature classes.

- Specify a movement tolerance.
A movement tolerance is used to prevent tiny, nuisance adjustments in parcel fabric points. The default tolerance is 0.05 meters (0.16 feet), which means that parcel fabric points will not be updated with adjusted point locations if the coordinate shift between the two points is less than 0.05 meters.

- Optionally, choose Update Attribute Fields to update the parcel fabric points feature class with statistical metadata from the adjustment point feature class.
- Run the tool to apply the adjustment results to the parcel fabric and update the locations of parcel fabric points and lines.