Data validation and DNC (Maritime Charting)

ArcGIS 10.1 Data Reviewer for Desktop extension is a data quality control management application. It simplifies many aspects of automated and visual spatial data quality control tasks. This results in a more efficient and consistent review process. Through a series of tools, Data Reviewer identifies where feature and attribute inserts and changes must be made to data. Data Reviewer logs defects in a portable table that can be passed from the quality control specialist to the editor. The editor implements the required changes to the database.

Data Reviewer serves as a reviewing tool that allows you to manage quality assurance of your data. It allows you to run individual checks or batch jobs (groups of checks) as well as spatially track and record anomalies discovered through visual data review.

Quality control management

Part of quality control management is defining the extent of the data to validate during the Reviewer session. Using the Create Polygon Grid Wizard tool, you can generate a grid that represents the area of interest you want to review. The grid allows you to divide an area of interest into smaller visual extents that can be used with checks.

The polygon grid can also be used with the Reviewer Overview window to manage which grid cells have been reviewed and which have not. Since the grid represents a specific extent in your map, you can use the Reviewer Overview window to navigate the different parts of the grid on your map.

Automated quality control

Data Reviewer contains many checks that can be used to validate your data. The checks are divided into several categories based on the type of validation they perform. For example, the Spatial Parameter Evaluation checks allow you to evaluate spatial parameters of features such as the number of parts, extent, intersections, and vertices.

These checks can be configured individually and run against a specified area of interest in your map. You can run the checks on entire feature classes, your current visual extent, a selected set of features, or based on a definition query that has been set for the feature class.

Learn more about Data Reviewer checks

Collective data review

You can group checks into a batch job. Checks can be added to a batch job using the Reviewer Batch Job Manager. A single check can be included in a batch job several times, each one with a different feature class or spatial parameter inputs. Once you are finished configuring checks for your batch job, you can save it as a Reviewer batch job (.rbj) file. This allows you to reuse the collection of checks on different databases that contain the same feature classes and to distribute the configuration to many users for consistency in the quality-control process.

The batch jobs can be run on the database using the Reviewer Batch Validate tool. This tool allows you to define the extent to use with the batch job—the selected set of features, the current extent, entire feature classes, or features that can be used based on a definition query—and validate your batch job against that extent before running it to ensure the feature classes referenced in the batch job are currently in the map. When the batch job is run, the returned features and table records are automatically written to the Reviewer table.

DNC-specific attribute (DNC<scale>_Attribute_A_10.1.0.0.rbj) and spatial (DNC<scale>_Spatial_R_10.1.0.0.rbj) batch jobs can be found under:

The version information at the end of the file name represents the release the model was associated with. It is recommended that you always use the latest version. The first two values represent the major release value. The second value represents the minor release value. The third value represents the service pack value, and the fourth value represents a patch. For example, 10.0.1.0 represents 10 Service Pack 1 and 10.1.0.0 represents 10.1 release.

Attribute checks are applied during the editing runtime validation process and are associated with the product library. You will be required to run spatial checks using Data Reviewer.

Import topology errors into a Reviewer table

When reviewing the data, it is not the responsibility of the quality-control specialist to correct discovered errors. The Topology Rules check will add the topologic errors discovered when validating topology to the current Reviewer table.

Visual quality control

Visual quality control consists of manually reviewing the data for anomalies, which range from miscoded features due to source interpretation to extraction density differences. Rendering the features with specific symbols can help detect miscoded features, while viewing the data at a smaller scale can help detect extraction density problems. There are many other types of problems that can be detected as well.

With Data Reviewer, you can manually view the data in your map and add records to the Reviewer table in a number of ways. You can use the Notepad tools to digitize missing point, line, and polygon features in the map. For example, if a road is missing, you can use the Line Notepad tool to draw the missing road. The feature is then written as a record in the Reviewer table and can later be given the attributes of a road.

You can also select features in the map and write them to the Reviewer table as necessary. The Browse Selected Features area in the Review Attributes dialog box allows you to individually view features in the current selection and individually commit them to the Reviewer table by clicking the Commit To Reviewer Table button or all at once by clicking Write All.

The Commit To Reviewer Table tool can also be used apart from the Review Attributes dialog box. For example, if you are using the Reviewer Overview window, you can go to a specific cell and use the Commit To Reviewer Table tool to write individual features to the Reviewer table.

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12/22/2014