The purpose of this project is to match place texts to a database of current and historical standardized places that includes geo-position information.

Try the demo.

The database contains over 400,000 current and historical populated places and higher-level political jurisdictions (districts, counties, provinces, states, etc.). It is based upon the place wiki pages at WeRelate.org. The database includes the place name, type (e.g., city, county, etc.) alternate names, the jurisdictional hierarchy that was in place in the early 1900's, earlier and later jurisdictional hierarchies, and geo-position coordinates.

Of course, the database is a long way from complete. WeRelate contributors continue to improve the database over time. Updates to the places at WeRelate will be added to the database periodically.

The matching algorithm is less than 1000 lines of code. It's written in Java, but could be ported to other languages. It basically tries matching places right to left, looking for sub-jurisdictions of previously-matched levels and skipping intermediate levels if not found. It can match place texts even if the text doesn't include commas between levels. The algorithm is fast, matching about 100K places per second on a single thread.

The algorithm has three modes:

• BEST - get the closest place; if you can't match the left-most level, return the lowest level that you matched,
• REQUIRED - if you can't match the left-most level, don't return anything,
• NEW - if you can't match the next level to the left, return a fake place name containing the unmatched level to the left followed by the matched level to the right.

The last mode is useful for returning places that are potentially missing in the database.

FamilySearch has a similar algorithm, but it is not open-source. In a test of 3736 place texts, both systems standardize just over 50% of texts identically. An analysis of 38 texts that were standardized differently show that both systems made about the same number of mistakes on that small sample. Detailed results are shown here.

Interestingly, this finding is similar to Nature's finding that the community-created Wikipedia and the professionally-managed Encyclopedia Britannica had roughly the same number of errors.

You'll need maven. mvn install creates the normal jar files as well as ones with all dependencies

• AnalyzeMatches.java standardizes a file of place texts and counts the number of matches by country and level.

• AnalyzePlaces.java analyzes a file of place texts and reports various statistics.

• CompareMatches.java compares how this system standardizes a file of place texts to another.

• StandardizePlaces.java standarizes a file of place texts and reports various types of problems in standardization.

• Service module provides a simple REST-based interface to the place standardizer.

The tools (except for the service of course) can be run using mvn exec:java -Dexec.mainClass=org.folg.places.tools.<tool name> -Dexec.args="<args>"

The service module generates a war file that can be run using tomcat, jetty, etc.

The project also includes a list of around 7M place texts extracted from 7000 GEDCOMs submitted to WeRelate.org over the past 5 years. If you want to try developing your own matching algorithm, feel free to use these texts as test data.

Caveat: due to privacy concerns, place texts containing numbers (about 400K) were removed from the data set.

The source code is available under the Apache 2.0 license. Data files in the resources directories are available under a Creative Commons Attribution-ShareAlike license. See the LICENSE file for details.

Support is available via the Google group

There are three ways in which this project could be improved upon:

• Learn weights for scoring ambiguous matches - When a text matches multiple places, which is the most likely? Currently the project includes hand-generated weights to score matching places. Ideally people would label which of the ambiguous places was most likely, and new weights would be learned based upon the labeled data.

• Learn from differences with FamilySearch - As mentioned above, when compared against FamilySearch's place standard, sometimes the place matched by this project is better, sometimes the place matched by FamilySearch is better. Someone could analyze the differences and review through the cases where FamilySearch was better, adding alternate names and new places to the WeRelate place wiki when necessary.

• Investiate frequent place texts that aren't matched - As new GEDCOMs get uploaded to WeRelate, we can track which place texts in those GEDCOMs don't get matched. Someone could review the frequent non-matching place texts and create pages for them on the WeRelate place wiki if they are indeed real places.

• 1.0 - Jan 2012 - initial launch
• 1.1 - 29 Feb 2012 - add sources for places, requires a change in places.csv file format

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