Understanding BigDataCloud's 'Confidence Value' for IP Geolocation Accuracy
The confidence value is BigDataCloud's measure of how reliable a geolocation result is for a given IP address. It's particularly relevant for dynamically assigned IPs, where a single point estimate can be misleading.
How it works
Two independent processes run in parallel for every IP address:
- Service area estimation — Using our patented technology, we determine the likely geographic region (the confidence area) within which the IP may be assigned, based on how ISPs allocate addresses across their network.
- Field evidence verification — We compare that estimate against real-world observations: actual locations where the IP address has been detected in the field.
The confidence value reflects how well these two independent results agree with each other.
Confidence levels
- Low — One or both processes produced no definite result, or the results conflict. The geolocation may still be correct, but treat it with caution.
- Medium — Both processes agree, but one is less precise. The result is reasonable but not strongly supported.
- High — Both processes independently produce consistent, precise results. This is the most reliable outcome.
| Confidence level | What it means |
|---|---|
| Low | Uncertain or conflicting results — interpret with caution |
| Medium | Processes agree, but precision varies |
| High | Strong agreement and high precision — most reliable |
When it matters
For most use cases — content localisation, currency detection, broad analytics — medium or high confidence is sufficient. For fraud detection, risk scoring, or compliance decisions where location accuracy is critical, filtering on high confidence results will reduce false positives.
The confidence value works alongside the confidence area polygon, which shows the geographic boundary the estimate falls within. Together they give you both a quality signal and a spatial bound on the uncertainty.