I'm a bit stuck with a problem involving the normalisation of location input data from the user (which comes from a third party).


To logically breakdown and interpret the user location input field and understand if it is listing single or multiple locations, from one or more countries.

The Problem

The type of data that I am receiving from the user input is messy and has no logical structure or consistency as show below. Daily geocodes are limited from google, so I have to use them sparingly. I want to efficiently process the location input from the user and send the correct geocoding query to google to get the right result.

Data Sources

Data is irrational and irregular and could be supplied in some of the following formats:

London, UK                              Alternative format
England, London                         Reversed order
London                                  Generic location            
London Sheffield, Newcastle             Three separate locations in the same country without consistent commas
London, Sales, Sales Assistant          Non location content inserted
London [NOT SPECIFIED]                  Other non location content inserted with non alphabet chars not separate with commas
London, Washington, Brazil, England     Mix of unrelated locations, including cities and countries
Washington, London, Kent                Mix of places within a single country   

Proposed Solution

Step 1: Breakdown data

  • Put each separated word in an array

Step 2: Sanitize data

  • Strip out invalid chars, commas, additional spaces etc
  • Strip out any words against a stoplist.txt (like job, sales, in, at, etc)

Step 3: Deterimine if valid location

  • See if each individual array item has been geocoded before, if not, geocode and store
  • Log any words which have been geocoded with no result – add these into the stoplist file to avoid pointless geocodes

Step 4: Interlink values

  • Compare if a places coordinate value falls within the range of another array item. If it does, we know they are parents and we treat them as a single item
  • London + England -> London coordinates falls within the co-ordinate range of England so we know it is a single location, not two separate ones.


Issue 1: Kent, London, Sussex Technically, there is a Kent in the USA, and it is the first one that comes up when you type it into google maps. However since all the results are in England it is extremely unlikely that the result we want is the USA one

Issue 2: England, Washington, New York There is a Washington in England, but this doesnt seem likely to be the one in England


Is my proposed solution of breaking the words down into separate entities and relinking them the most logical solution? Any help or advice would be much appreciated, I know it's not any easy problem.

  • 2
    For example, issue 2: not even a human can decide for sure which Washington is meant, so don't expect a program to make a better decision. And to your question: this depends heavily on the overall quality of your data. Best thing IMHO is to make a sample of >1000 locations and see how it works.
    – Doc Brown
    Aug 16, 2013 at 7:55
  • Well I guess it's about taking an informed guess. I think it is more likely it is the larger Washington in USA especially since New York was mentioned. I guess it is about generating a good guess via some AI.
    – J.Zil
    Aug 16, 2013 at 17:12
  • 1
    It's actually better served with a re-vamped UI on the input end. However, I recognize that this is out of your control. Sep 24, 2013 at 18:25

1 Answer 1


I will make an attempt to answer the question. :)

I think you need a system that can:

  1. Learn about locations as it grows.
  2. Improve itself overtime.

Assumptions: Assuming that you don't need any real time geo code analyses. The below solution will work in back ground and will continue to improve automatically as it grows older.

You should be having a giant repository of countries states cities etc. in your database. This is the only way you can track whether some address is related

Some pseudo algorithm which might help:

  1. You need to group that array by some groupId
  2. Check whether the individual item in the array is already a known country, state, city or even already geo coded.
  3. If the above step yields nothing then Google it, there might be some websites that provide such kind of stuff. You can do Web crawling to get there.

  4. From the above step you will get further stop words to add in Your stop list.

  5. You are ready to geocode as it is easy now to know country, state, city.

  6. You should save the geocoding results so that it may be of use later.

Note: You might also consider having a revalute policy of the stop list. Some time after which you may reconsider the words as allowed words. The world is growing who know they might have a new location with a name that is in Your stop list :)

Disclaimer: I have never worked on geocodes and have not researched much about it. I am giving the solution as a conceptual answer.

Hope it helps

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