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    <p>From a data science perspective what you are doing is not correct. You cant just use the average error distance as a cutoff and think your data will be more correct. The two points you are comparing can have an error more or less than 15 m they can shift toward each other or move away of each other. And if you don't have another exact dataset there is no way of telling what would be the correct point. You can't make this dataset more precise. </p> <p>However I think you goal is to simplify your dataset, not to make it more accurate. For that you can use the <a href="http://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm" rel="nofollow noreferrer">Douglas–Peucker algorithm</a>. I would suggest you load your data in an <a href="http://postgis.refractions.net/" rel="nofollow noreferrer">Postgis</a> enabled database (Postgresql + postgis) and then use the <a href="http://www.bostongis.com/postgis_simplify.snippet" rel="nofollow noreferrer">simplify</a> function. This will require some db setup time, but then it will speed you up greatly. However if you want it in pure python this SO <a href="https://stackoverflow.com/questions/2573997/reduce-number-of-points-in-line">question</a> has a very nice snippet.</p> <p>BTW. If your are doing distance calculations with lat,lon do not use Pythagoras. It is not valid since lat,lon is not Euclidean. Use the <a href="http://en.wikipedia.org/wiki/Haversine_formula" rel="nofollow noreferrer">haversine</a> algorithm.</p>
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    1. POPython, numpy, scipy: How to exclude a location error from GPS coordinates? (averaging lon, lat)
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    1. COIf this idea works for you, you may want to look at the [Shapely](http://pypi.python.org/pypi/Shapely) package, which also has a `simplify` method. From its docstring: 'Returns a simplified geometry produced by the Douglas-Puecker algorithm'.
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    2. COSecond that. Shapely is also a very usable package for these kind of problems.
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    3. CO@RickyA, thanks, for calculation of distance I will use haversine algorithm.
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