Testing location data with edge cases, not just happy paths
An address in a well mapped city center tells you almost nothing about how your system handles a rural route, a disputed border, or a query near the poles. Test the hard cases deliberately.
How we treat null and low-confidence results
There is a real temptation, in any system that answers questions, to always return something rather than admit a question cannot be answered reliably. For location data specifically, giving in to that temptation is actively harmful, because a plausible-looking but wrong answer is generally far more damaging to whatever decision it feeds into than an honest admission that the data does not support a confident answer.
The reasoning is straightforward once stated directly. A null result, or a result explicitly marked with low confidence, is something a well designed application can detect and handle deliberately, falling back to asking the user for clarification, applying a wider default, or flagging the record for manual review. A manufactured guess that looks like every other confident result, with no visible signal that it was actually a weak or ambiguous match, cannot be detected or handled specially at all, because from the outside it is indistinguishable from a genuinely solid answer, right up until it causes a real, visible problem somewhere downstream that is often difficult to trace back to its actual source.
This is why an ambiguous or unresolvable query should return either no result at all, or a result whose confidence score honestly and clearly reflects the genuine uncertainty involved, rather than the geocoder silently picking one plausible candidate among several and presenting it with the same apparent certainty as an unambiguous match. The same principle applies to precision: a result should never claim a finer precision level than the data actually supports, even under pressure to always return something more specific looking. Reporting city honestly is a better outcome than reporting house on a guess.
For anyone building on top of this kind of data, the practical implication is to design your own application to genuinely expect and gracefully handle null and low-confidence results as a normal, routine part of the response space, not as a rare exceptional case requiring special separate handling bolted on as an afterthought. A form that only has a happy path for high-confidence, fully resolved results, with no considered behavior for anything else, will eventually break in a confusing way when it inevitably encounters a query the data genuinely cannot resolve confidently, which happens more often than most initial designs anticipate.
Checking both precision and confidence on every result, and having a deliberate, considered fallback behavior for when either one falls below whatever threshold your specific use case actually requires, is the difference between an application that degrades gracefully on hard cases and one that quietly propagates bad data because it was never actually designed to expect anything but a clean, unambiguous answer.