We geo code all addresses then use a radius as the first filter then apply other heuristics to filter after that.

Geo coding raises other issues. For example, some geo coding engines return multiple lat/long pairs (for one address).

We deal with only about 2,500 addresses from 4 data sources with quarterly updates and about a 10% turnover rate. We use human review. We set a strict "similarity" level then generate a list of match candidates. Humans review this list and evaluate as a match or non-match. We then loosen the similarity requirement and generate another candidate list (but filter any non-matches found by the first filter). That candidate list is evaluated by humans. Loosen, (rinse) and repeat.

After matching comes more fun, merging. Put another way, once you have a match that is not exact, how do you reconcile the differences? Given two differences, which is right?

In reply to Re: Efficient Fuzzy Matching Of An Address by jimX11
in thread Efficient Fuzzy Matching Of An Address by Limbic~Region

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