In an era driven by data-driven decision-making, the accuracy and reliability of geospatial information have become non-negotiable. The introduction of the Utility Network has focused a spotlight on data quality. Creation of a Network Topology without dirty areas requires significant up front data assessment, error prioritization and cleanup.
GeoData Sentry is a geodatabase quality assessment tool. GeoData Sentry can be quickly configured to test and report key data issues that may impact current operations or migration of data. The benefits of proactively assessing the quality of the entire geodatabase are:
Error Prioritization to eliminate the most critical errors
Error Trend Analysis
Continuous Process Improvement
GeoData Sentry has an extensive Test Inventory including Attribute Tests, Spatial Relationship and Logical Connectivity Tests. These tests detect errors ranging from the simple invalid values with Domain and Subtypes to Invalid or Duplicated Geometries to Critical Business Rule testing for Pressure Classes or Phase Mismatch.
With GeoData Sentry, Test Suites can be generated from the geodatabase to rapidly configure a test configuration that can be run for a quick data assessment, or as an input into a ongoing scheduled validation to support Continuous Process Improvement. Over time, Trend Analysis can be executed to identify repeated errors, new errors, and areas of vulnerability within application workflows and user training.
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Junction Edge Validation is a vital aspect of maintaining the accuracy and integrity of geometric networks. This powerful test identifies instances where a junction is erroneously connected to an edge where it should not be, based on the defined connectivity rules.
In the world of geometric networks, specific rules govern junction-to-edge connections to ensure data consistency and functionality. For example, according to the connectivity rules, Service Location junctions can only connect to Service Pipes, and Overhead Service Taps can solely link with Overhead Conductors. By conducting the Junction Edge Test, organizations can promptly detect any violations of these rules, ensuring that junction-to-edge connections remain in line with the defined geometric network guidelines.
Maintaining accurate geometric network connections is crucial for efficient data analysis, network tracing, and accurate representation of utility systems. By leveraging Junction Edge Validation, utility companies can enhance the reliability of their geometric networks and deliver optimized utility services.
Managing the geodatabase involves essential tasks such as Data Model updates and their implementation. Each of these steps is important to ensuring a successful data model update, and although different organizations may have their own specific methods, the overall process follows a general pattern.
This diagram outlines the key steps involved in promoting a data model update to the production environment. The process begins with Requirements Gathering, where the next set of changes is identified. This includes discovering the necessary changes, analyzing them, and deciding when they are ready for development.
The Development phase comes next, where tasks are completed, and unit testing is conducted to determine the readiness of each item for testing.
Following that is the Test Promotion phase, which assesses whether the items are prepared for User Acceptance Testing (UAT) Quality Assurance (QA).
The UAT/QA Promotion phase thoroughly tests the changes to ensure they are production ready. At this point, the development team and business create release notes to communicate the updates.
Finally, in the Production Deployment phase, the approved changes are deployed to the production environment. The team confirms the consistency of these changes with the UAT/QA database, performs smoke testing for a quick validation, releases the updates to production, and shares the release notes with the users.
Data Model Conformance plays a crucial role in maintaining accurate data within a geodatabase. It refers to the degree to which data adheres to the defined rules and properties of the geodatabase. To ensure the integrity of the data, various tests can be configured, including subtypes and domains. These tests help identify errors in the data or inconsistencies in the geodatabase’s data model properties. Evaluating and addressing these errors is essential to mitigate any potential impact on Utility Network migration.
One critical aspect of Data Model Conformance Validation is Subtype Validation. This test suite specifically focuses on feature classes and related tables that utilize subtype control. Its purpose is to verify the validity of all subtype values within the geodatabase. The test suite is automatically generated based on the subtype rules defined in the geodatabase.
Why are subtypes so important? Subtypes serve as the cornerstone of ArcGIS, forming the basis for the Asset Types associated with feature classes in the Utility Network. Ensuring correct feature migration relies heavily on the presence of valid subtypes in the source data. By validating subtypes during Data Model Conformance, organizations can guarantee the accuracy and reliability of their Utility Network data.
By conducting thorough Data Model Conformance Validation, organizations can identify and rectify errors in the data and geodatabase properties. Furthermore, evaluating the detected errors for their impact on Utility Network migration becomes essential. This validation process helps organizations maintain data integrity, streamline migration efforts, and leverage the full potential of their geodatabase.
Logical Connectivity Validation is a vital step in guaranteeing accurate connections within a utility network. GeoData Sentry generates these tests from the Geometric Network Connectivity.
There are three categories of tests that validate these connections, taking into account utility-specific business rules. For example, an electric utility’s geometric network may require that an overhead device can only be connected to an overhead conductor, or that a service meter must only be connected to a service lateral. Similarly, a water utility may enforce the rule that all types of fire hydrants must be connected to fire hydrant laterals. These business rules, stored in the geometric network connectivity rules, control the data at the subtype level.
The Geometric Network architecture allows for tracing and analysis even with junction/edge connection errors. In contrast, the Utility Network architecture creates a Dirty Area for each invalid network connection. To create Utility Network subnetworks, it is necessary to clear all Dirty Areas in the network topology. This emphasizes the importance of feature-to-feature connections based on asset group and asset type.
By conducting thorough Logical Connectivity Validation, organizations can ensure that utility network connections adhere to the defined business rules. This validation process supports accurate tracing, analysis, and the creation of Utility Network subnetworks. Maintaining proper feature-to-feature connections based on asset group and asset type becomes essential for a well-functioning utility network.
Laurel Hill GIS will be attending the Esri Infrastructure Management & GIS Conference in Palm Springs, California on October 10th-12th. As Esri says, “Join professionals specializing in infrastructure management from several interconnected industries—architecture, engineering, and construction (AEC); electric; gas; telecommunications; transportation; and water.”
We”ll be at booth 302 in the Expo area. Stop by to see demonstrations of our entire product line, chat about your projects and challenges, or just say hello.
If you have specific questions about a product or wonder how it might apply to your organization, this is a great opportunity to talk to our experts about how the GeoData Suite can fit into your workflow and maximize your data assets.