Why PAI is the Key to UN Migration Success
Why Positional Accuracy Improvement is the Key to Utility Network Migration Success
Published: September 2, 2025
The field technician glanced at the newly generated Esri Utility Network (UN) map on her tablet — and immediately knew something was wrong...
She had been dispatched to fix a downed line, but the location pin dropped her on the wrong street entirely. The problem traced back to years of legacy system errors carried over from the old Smallworld database, placing assets just far enough off that crews could lose critical time searching in the wrong place.
In the world of utility infrastructure, a misdirected truck can mean missed service windows, costly rework, and even public safety risks when the fix cannot wait.
One error likely signals hundreds more buried in the data. Flagging it is a start, but it does not solve the deeper challenge of migrating legacy data that was never built to meet the precision demands of today’s grid. This is not about cleaning up a map; It is about restoring trust in your network model and meeting the demands of regulators, operators, and customers alike.
That is where Positional Accuracy Improvement (PAI) matters. PAI automatically aligns legacy features to real-world coordinates, eliminating inherited misalignments and ensuring migrated data reflects true field conditions. Precision is not just a nice-to-have – it is mission-critical. Without it, every downstream decision made on that data is compromised. To grasp the true risk, we have to confront a hard truth about legacy systems.
Legacy Data Is Not Where You Think It Is
Legacy GIS platforms like GE Smallworld were built on old-school “land based” maps – rough composites of planning docs, cadastral sketches and road blueprints. These maps weren’t designed for precision. Instead, they prioritized relative position (think: aligning property boundaries with nearby roads) over absolute location. Esri researchers call this a “macro-scale” view – with errors ranging anywhere from 25 to 50 feet.
Downey, California, found out just how off things could get. When the city overlaid its Smallworld-era basemap with GPS-aided aerials, they uncovered an average positional error of 139 feet. The new planimetric data, on the other hand, was accurate to within two feet. Why the mismatch? Early projects often “rubber-sheeted” accurate surveys to fit older, schematic basemaps – locking in systemic inaccuracies. Utilities inherited this flawed legacy, with networks often mapped as engineers imagined them, not as they were actually built.
Those legacy land bases can be wildly out of sync with reality. NfoldROI’s conflation team routinely finds assets “hundreds of feet off,” and Esri confirmed misalignments of up to 800 feet in Downey’s case. When you pull that data into a modern GIS with real-world imagery, pipelines and parcels don’t line up – and things get messy.
These errors are more than annoying – they’re expensive. Misaligned geometries and missing data delay validation, drive up costs, and frustrate everyone during a UN migration. NRECA points out that many co-ops still lack accurate “as-built” records, and the California Public Advocates Office recently flagged $2.9 million in excess costs due to trench miscalculations at SoCalGas, directly linked to GIS misalignment.
The ripple effect is real. GIS inaccuracies – from missing updates to bad basemaps – undermine core functions like asset management and outage response. One large California water utility learned this the hard way: after upgrading to a new land base, their existing network data no longer lined up. Manual fixes were slow and costly, but automated positional accuracy tools helped them shrink asset marking “buffers,” reduce truck rolls, and cut labor costs.
Fixing this by hand is nearly impossible at scale. Adopting more accurate parcel data often stalls because realigning maps manually takes months. That’s why automation isn’t just helpful – it’s essential.
How PAI and 1Spatial Deliver
This is where PAI shines. Positional Accuracy Improvement involves systematically identifying discrepancies between your legacy geometries and real-world locations, then applying carefully calculated shifts – shift vectors – to bring the data into alignment.
Our proprietary approach, delivered through 1Integrate, ensures this process is both scalable and precise:
- Automated shift vector creation and conflation: When you have reliable reference data (e.g., parcels, GPS control points), 1Integrate can pair Positional Accuracy Improvement (PAI) with our automated conflation tools to not only generate the shift vectors needed to realign your network, but also seamlessly merge and reconcile overlapping datasets. This ensures your migrated network aligns precisely with authoritative sources, reducing manual edits and accelerating data readiness.
- Manual flexibility: When reference data is incomplete, 1Integrate supports manual creation of shift vectors by visually comparing your legacy drawings with basemaps.
- Granular control: You can define stop points (areas where data is already accurate and must remain fixed) while applying different shift vectors to adjacent areas. This means one section of your network can be shifted east, while the neighboring section is shifted north (or any other direction), correcting localized distortions without compromising the accuracy of surrounding data.
Critically, our PAI process preserves topology. After applying shifts, 1Integrate intelligently snaps features back to their original logical connections, ensuring networks remain valid and functional in their new, accurate positions.
Do You Really Know Where Your Network Is?
Ask yourself this question: Can you trust your current network data to reflect reality?
Many utilities assume their GIS data is "good enough" until a migration exposes its flaws. The truth is that misalignment issues remain hidden until you switch from legacy systems to GIS platforms grounded in real-world coordinates.
Without proactive positional accuracy improvement, you risk migrating flawed data – undoing the very value you hoped to gain from moving to the Esri Utility Network. Worse, your teams could spend months fixing issues post-migration, eroding confidence and increasing costs.
What Comes Next
The good news? You can address this risk head-on – before it derails your migration.
Here’s what the PAI journey looks like when you partner with 1Spatial:
- Assessment: We evaluate your legacy data and basemap alignment.
- Shift vector creation: Automated where possible; manual when needed.
- Flexible application: Fine-grained shifting with stop points and boundaries.
- Topology preservation: Auto-snapping ensures network integrity.
- Validation: We deliver spatial reports so you can see exactly what’s been adjusted.
The result is a utility network that is accurate, validated, and ready for future-proof operations and analytics.
With PAI, your Utility Network migration becomes faster, cleaner, and future-proof. Because in critical infrastructure, accuracy is not optional, it is the foundation.
Are you planning your Smallworld to Utility Network migration? Do not leave your data accuracy to chance. Contact 1Spatial today to discuss how our Positional Accuracy Improvement methodology can help you accelerate your migration, minimize errors, and deliver confidence in every coordinate.