Data normalization is essential when combining city-level metrics from multiple public and private sources.
Why It Is Needed
Sources differ in geography, release cadence, and units. Without normalization, comparisons can be misleading.
Common Steps
- Align geographies (city vs metro)
- Standardize units and scales
- Handle missing values and outliers
- Apply weighting logic consistently
Methodology Integrity
Transparent normalization choices improve reproducibility and trust in rankings.
User Impact
Normalization quality determines whether cross-city comparisons are fair and decision-useful.