Tracing Utility Networks: Why Your Topology Strategy Matters

Maximizing the value of your utility data is essential to business performance. Network tracing across your utility infrastructure plays a key role in assessing the impact of outages and guiding investment in maintenance and operational performance. The underlying topological model is a key factor in enabling effective network tracing. The following examines the differences between core topological models and considers the trade-offs in efficiency, flexibility, and maintenance when adopting one over the other.

Tracing Utility Networks

Tracing Utility Networks requires information about the network, managing node behavior and connectivity. Nodes that can be closed, such as valves, permanently shut valves, one-way valves, etc., need to be taken into consideration.  The links through the network to find connected sources or services is also fundamentally important.

Knowing which node connects to which pipe, the type of each node, and how properties are connected - including which parcels or services they link to - is essential for calculating a shut-off area and identifying which parcels will be affected during a network trace.

Using a water network as an example, a shutoff is defined as an area between nodes that can be isolated. This is known as a primary shut-off (PS in the diagram). A secondary shut-off (SS) is a shut-off that is dependent on another shut-off as its only source of water.  Typically, shut-off blocks in a cul-de-sac would be dependent on such other primary shut-offs as their only water source.

It is important to note that under some conditions a secondary shut-off may overlap other secondary shut-offs.  In the diagram above, SS1 is a secondary of PS1 and SS2 is a secondary of PS2.

These calculations can be done either by computing full geometric connectivity (topology) during each trace or by verifying logical connectivity through IDs using attribute-based relationships. Implementing and maintaining a full geometric network is typically more complex and difficult to maintain.

Understanding Physical vs. Logical Network Models

In the world of Geographic Information Systems (GIS), topology refers to the spatial relationships between connected assets, such as pipes, cables, or roads, ensuring that they are properly linked and behave as expected in network analysis. A well-defined topology allows for accurate modeling of how resources flow through a network, which is essential for utilities managing complex infrastructure. The accuracy and maintenance overhead of your topology is a key consideration when selecting the best topological model for managing your data.

A physical network model represents real-world asset connections with strict topological integrity, ensuring that all network components are explicitly linked by virtue of their coincident coordinate locations. This approach promotes spatial accuracy and eliminates ambiguity in network tracing. However, maintaining a physical network model requires continuous updates to reflect infrastructure changes. Every modification, such as a new connection or attribute, must be carefully incorporated to maintain network integrity. Additionally, because each attribute must be predefined within rigid structures, expanding or adjusting the model can become cumbersome over time.

Water network subset showing a physical network model

A logical network model, by contrast, defines connectivity through attribute relationships established during data capture, making it less prone to connectivity loss or breakage than models relying solely on strict spatial connectivity. Instead of enforcing physical connections, it allows for a more dynamic approach where asset relationships are stored in the data structure. This makes the logical network model more adaptable to real-world conditions without reducing quality, minimizing the need for constant updates while improving speed of network tracing. Logical networks, such as those implemented by Open Spatial, can accommodate slight variations or connectivity tolerances, improving operational flexibility. Additionally, they allow utilities to conduct complex analyses, such as determining shutoff/isolation blocks or service dependencies, without requiring strict definitions like the physical model, all while being 100% topologically correct.

Water network subset showing a logical network model

Key Considerations

• Efficiency in Maintenance – Physical models demand a consistently accurate topological underlay. Any edit to a feature requires a corresponding update to the topology to preserve connectivity, which can be time-consuming and error prone. Additionally, physical models typically allow only one topology per asset class, meaning all features—such as mains and property connections—must be grouped as a single class (e.g., water lines) and then differentiated through subclasses. In contrast, logical models are more adaptable to real-world changes and require less frequent maintenance whilst maintaining accurate network results. Their use of attribute-based connectivity provides a more resilient and flexible foundation for network tracing and ongoing data management.

• Network Breaks & Tolerance – Logical models allow for some connectivity tolerance, reducing errors and supporting operational realities such as temporary outages or infrastructure inconsistencies.

• Data Structure & Scalability – Logical models streamline data management by supporting evolving utility networks without requiring extensive structural changes.

• Operational Flexibility – Logical models enable utilities to implement complex validation rules, build dynamic views, and configure shutoff areas efficiently.

Choosing the Right Approach

A logical network model offers adaptability, reduced maintenance, and a more practical representation of real-world infrastructure, making it a strong choice for utilities looking for efficiency and scalability. A physical model ensures strict accuracy but requires significant resources to maintain and update. Utilities should assess their operational needs, long-term data strategy, and maintenance capabilities to determine the best approach for managing their network.

Join the Discussion

If you are interested in learning more about the differences between physical and logical network models and the downstream implications to your network trace functionality, join us on Wednesday, July 9, 2025 at 10:00AM AEST or 2:00PM PDT for our free webinar, “Tracing Utility Networks: Why Your Topology Strategy Matters.” In this webinar we will dive deeper into the functionality of each network model, and demo network trace functionality. Register today or schedule a call with Open Spatial to learn more.