Data Governance
How we source, normalize, score, and maintain the UQLI™ dataset.
Data Sources
Methodology Summary
Normalization: Winsorized (p5/p95) min-max normalization; floor score 2.0 (no city scores 0)
Dimension aggregation: Arithmetic mean of normalized indicator scores within each dimension
Composite score: Weighted geometric mean across 10 dimensions (0–100 scale)
Confidence rating: Based on indicator coverage — High (≥80%), Medium (60–79%), Low (<60%)
Full methodology documentation is available at /methodology.
Data Cycle
UQLI scores are refreshed annually. The current active cycle is 2025. Each cycle includes a full re-normalization against the latest available source data.
Quality Controls
- Automated drift detection: score changes >15% trigger manual review
- Methodology version history maintained and publicly documented
- All source data timestamped and archived at ingestion
- Confidence ratings computed per-city based on indicator completeness
Reproducibility
All methodology versions are archived and versioned. Scores can be verified and cross-referenced at universalqli.com/verify. Source data for each indicator is traceable to the original federal agency dataset.
Data Corrections
To report a potential data error, email data@universalqli.com with the city, indicator, and supporting evidence. Corrections are reviewed and processed within 30 days.
Open Data Commitment
All composite scores, US city ranks, and confidence ratings are publicly accessible. Dimension-level scores are available to Research+ subscribers. Raw indicator data is subject to source licensing agreements and is not redistributed.