## Summary

Analyzes the parcel fabric measurement network by running a least-squares adjustment on the input parcels. A least-squares adjustment is a mathematical procedure that uses statistical analysis to estimate the most likely coordinates for connected points in a measurement network. A least-squares adjustment can be run on the parcel fabric to evaluate and improve spatial accuracy of parcel corner point locations.

Learn more about running a least-squares adjustment on the parcel fabric

Different types of adjustments can be run on the parcel fabric depending on whether you are evaluating spatial accuracy or improving spatial accuracy.

- Consistency check—Uses a free network adjustment to check measurements (dimensions) on input parcel lines for blunders and outliers
- Weighted adjustment—Uses two or more fixed or weighted control points to estimate updated coordinates for points on the input parcels

## Usage

Run the least-squares analysis on a selection of parcels and lines. If no parcels are selected, the adjustment will be run on the entire parcel fabric dataset.

##### Note:

Running a least-squares adjustment on a large number of parcels may result in long processing times and may consume a large amount of disk space.

A selection of both parcels and lines can be used as input to the least-squares analysis. When parcels are selected, dimensions on the parcel lines and coordinates of the endpoints of the parcel lines will be used as input to the adjustment. If lines or connection lines are individually selected, the dimensions on the lines and the coordinates of its endpoints will be used as input to the adjustment.

##### Tip:

Select connection lines if you want them to be included in the least-squares adjustment.

Use the Consistency check option as the Analysis Type parameter value to evaluate the dimensions on parcel lines for mistakes and outliers. For example, run a consistency check on newly entered data to detect mistakes before integrating it with the parcel fabric.

Use the Weighted least squares option as the Analysis Type parameter value to update and improve the spatial accuracy of parcel point coordinates. Another example of running a weighted least-squares analysis is to evaluate how newly added data affects the spatial accuracy of points. At least two fixed or control points are required for a weighted least-squares analysis.

The results of the least-squares analysis are stored in adjustment feature classes for analysis purposes. The adjustment feature classes allow you to view and analyze the adjusted data before applying the adjustment to the parcel fabric points. Adjustment results include adjusted dimensions, adjusted points, and other statistical data such as positional uncertainties and error ellipses.

If the results of the weighted least-squares analysis are acceptable, run the Apply Parcel Least Squares Adjustment tool to apply the results of the least-squares analysis to parcel fabric points.

## Syntax

AnalyzeParcelsByLeastSquaresAdjustment(in_parcel_fabric, analysis_type, {convergence_tolerance})

Parameter | Explanation | Data Type |

in_parcel_fabric | The input parcel fabric to be analyzed by least-squares adjustment. | Parcel Layer |

analysis_type | Specifies the type of least-squares analysis that will be used in the adjustment. - CONSISTENCY_CHECK —A free-network least-squares adjustment will be run to check dimensions on parcel lines for inconsistencies and mistakes. Fixed or weighted control points will not be used by the adjustment.
- WEIGHTED_LEAST_SQUARES —A weighted least-squares adjustment will be run to compute updated coordinates for parcel points. The parcels being adjusted should connect to at least two fixed or weighted control points.
| String |

convergence_tolerance (Optional) | The tolerance representing the maximum coordinate shift expected after iterating the least-squares adjustment. A least-squares adjustment is run repeatedly (in iterations) until the solution converges. The solution is considered converged when maximum coordinate shift encountered becomes less than the specified convergence tolerance. The default value is 0.05 meters. | Linear Unit |

#### Derived Output

Name | Explanation | Data Type |

updated_parcel_fabric | The updated parcel fabric. | Parcel Fabric |

updated_adjustment_points | The updated AdjustmentPoints feature class. | Feature Layer |

updated_adjustment_lines | The updated AdjustmentLines feature class. | Feature Layer |

updated_adjustment_vectors | The updated AdjustmentVectors feature class. | Feature Layer |

## Code sample

The following Python window script demonstrates how to use the AnalyzeParcelsByLeastSquaresAdjustment tool to analyze a parcel fabric measurement network in immediate mode:

```
import arcpy
arcpy.parcel.AnalyzeParcelsByLeastSquaresAdjustment('c:/Parcels/Database.gdb/CountyParcels/CountyFabric',
'CONSISTENCY_CHECK', 0.05)
```

## Environments

## Licensing information

- Basic: No
- Standard: Yes
- Advanced: Yes