Available with ArcGIS Pro Standard and Data Reviewer licenses.
The Monotonicity check finds polyline features that contain monotonic errors in either elevation or measurement values (z-values or m-values).
ArcGIS Data Reviewer checks support multiple methods for implementing automated review of data. The following table identifies the supported implementation methods for this check:
Reviewer batch job
Attribute (validation) rule
(ArcGIS Pro 2.4 or later)
(ArcGIS Pro 2.5 and ArcGIS Enterprise 10.8 or later)
Map-based Reviewer rules are deprecated and are no longer available for use. It is recommended that you migrate automated review workflows to Data Reviewer capabilities available in attribute rules. Opening or saving a map document (.aprx) at ArcGIS Pro 3.0 or later removes all the existing map rules from the document.
The Monotonicity check identifies features that contain z-values or m-values that are not strictly increasing or decreasing in value. Features that contain nonmonotonic values can impact the results of your analysis and models.
Features can also be evaluated to determine whether they are trending in a specific manner. Features that contain node values that do not trend as expected can impact the results of your analysis.
When configured as a constraint rule, an error notification is returned when a feature is created or modified based on the conditions defined in the parameters of the rule.
When configured as a validation rule, an error is created during validation when a feature contains either a vertex that is nonmonotonic or meets other conditions specified in the rule. In m-value validations, vertices that don't contain measure values (NaN) are also returned as errors.
- In water resource management, elevation values stored in streamline features are used to add detail to digital elevation models (hydro DEM conditioning) to enforce known drainage patterns.
- In roadway management, roadway routes that have two or more consecutive vertices with the same measurement can adversely affect length calculations, for example, United States Department of Transportation mileage reports.
Non-monotonic—The following image illustrates two (2) line features with z-values and m-values that are not strictly increasing or decreasing in value:
The subtype to which the rule is applied.
A query that identifies the features to which the rule is applied.
The property to be evaluated.
Choose either z-values or m-values for evaluation.
Error conditions evaluated by the rule. Any feature whose values match those defined in the rule is returned as an error.
The editing events that trigger the rule to take effect.
A unique name for the rule.
This information is used to support data quality requirement traceability, automated reporting, and corrective workflows.
A description you define of the error when a noncompliant feature is found.
This information is used to provide guidance to facilitate corrective workflows.
The severity of the error assigned when a noncompliant feature is found.
This value indicates the importance of the Reviewer result relative to other results. Values range from 1 to 5, with 1 being the highest priority and 5 being the lowest.
Tag property of the Reviewer rule.
This information is used in rule authoring and management workflows to support traceability and reporting of data quality requirements.
- Polyline features must be either z-enabled or m-enabled to be used in this check.
- Differences in z-values or m-values between adjacent vertices that are within the respective tolerance are not compared when evaluated for monotonicity.
- Polyline features must have a defined vertical coordinate system to be used in this check for evaluating z-values.
- A feature’s From node and To node values are used to determine whether z-values or m-values should be strictly increasing or decreasing when evaluated for monotonicity.
- Vertices that are within the feature's x,y tolerance are not compared when evaluated for monotonicity.
- Multipart features are evaluated in part order (Part 0, Part 1, and so on) when evaluated for monotonicity.
- When this check is authored as an attribute (validation) rule and multiple error conditions are detected on a feature (for example, non-monotonic and increasing), a single error feature is created. When this check is contained in a Reviewer batch job, an error result is created for each condition.
- The Attribute filter parameter is limited to comparison (=, <>, >, <, >=, <=) and logical (AND/OR, IN/NOT IN, LIKE/NOT LIKE, IS NULL) operators.