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Reviewer rule design

Available with Data Reviewer license.

Reviewer rules are preconfigured checks that allow you to validate features based on specific conditions that are not compliant with established data quality requirements defined by your organization.

Reviewer rules are created for detecting anomalies with features, attributes, and relationships in your database. Reviewer rule results are logged in a Reviewer session, which is used to manage the life cycle of the data analysis.

Automated validation capabilities

The first step to take when designing a Reviewer rule is to understand the capabilities of automated checks in Data Reviewer. The following data check types assess different aspects of a feature's quality.

  • Spatial checks—Analyze the spatial relationships of features. You can analyze whether features overlap, intersect, are within a specified distance of each other, or touch. For instance, you may want to verify that a road does not cross into the ocean. You can also analyze whether features are within a certain distance of one another. For instance, a fire hydrant must be connected to a water lateral.
  • Attribute checks—Analyze the attribute values of features and tables. This can be simple field validation, such as a geodatabase domain, or have more complex attribute dependencies. For many features, one attribute is dependent on another attribute of the same feature. For instance, if a road is under construction, it may not be accessible. An attribute check can be configured to monitor the status and accessibility of the roads.
  • Feature Integrity checks—Analyze the properties of features. Not all features in a database follow the same capture criteria. You may have collection rules that define how close two vertices can be or whether multipart features are allowed in your data. Feature integrity checks ensure that collection rules are followed for each feature class. For instance, the Cutbacks check can be used to identify features that contain sharp angles.
  • Metadata checks—Analyze the metadata information of the feature datasets and feature classes. Metadata can contain critical information about the source used to collect the derived data, which can significantly impact the reliability of the data. For instance, the date range of source data could significantly impact the accuracy of maps produced and the analysis performed using the data.

    For more information, see ArcGIS Data Reviewer checks in ArcGIS Pro.

Identification of Data Reviewer checks

The identification of automated Reviewer checks is often a task performed by a subject matter expert with deep knowledge of the GIS data model and the data maintenance processes GIS editors execute. A subject matter expert can quickly identify a variety of issues, such as feature integrity, attribute completeness, spatial relationships, and metadata content.

Based on the data quality requirements previously referenced, the subject matter expert can start relating Data Reviewer checks to any of the data quality requirement categories using a requirements traceability matrix and other reference documents. For more information about the use of a requirements traceability matrix, see Identify data quality requirements.

The following table is an example of a populated requirements traceability matrix that references some of the automated validation capabilities described above for traceability purposes.

This list can provide organizations with a quick reference for looking up a specific capability for a product and its use when collecting requirements.

IDRequirementRequirement numberRequirement categorySoftwareProduct capabilityData Reviewer check

5

Ability to ensure that source data will be migrated into the production database and have appropriate domains and relationships

D002

Data Requirement—Logical consistency

Data Reviewer

Domain Check

Relationship Check

Subtype Check

Yes

7

Ability to ensure that production data is for mobile collectors and is attribute accurate

D004

Data Requirement—Thematic accuracy

Data Reviewer

Table to Table Attribute Check

Regular Expression Check

Yes

8

Ability to ensure that there is no overlap between event measures during the project period of 2010–2020

D005

Data Requirement—Temporal quality

Data Reviewer

Invalid Events Check

Yes

10

Ability to identify the number of cells that are not populated (NULL) for each required attribute field

D006

Data Requirement—Thematic accuracy

Data Reviewer

Query Attributes Check

Yes

11

Ability to identify parcels that have no overlaying building footprint features

D007

Data Requirement—Logical consistency

Data Reviewer

Feature on Feature Check

Yes

13

Ability to validate a unique ID attribute linking a parcel to matching building footprint features

D008

Data Requirement—Logical consistency

Data Reviewer

Feature on Feature Check

Yes

14

Ability to confirm all features are compliant with metadata standards

D009

Data Requirement—Data completeness

Data Reviewer

Metadata Check

Yes

Sample Requirements Traceability Matrix