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This is more of a challenge question than something I urgently need, so don't spend all day on it guys.
To just state the problem, given the following database schema roughly : USER TABLE UserId UserName ZipCode ZIPCODE TABLE ZipCode Latitude Longitude With USER and ZIPCODE being joined on USER.
What approach would you take to answer the following question: What other users live in Zip Codes that are within X miles of a given user's Zip Code.
We used codes soft calculator zipwhich has tables for zip codes and their approximate lattitude and longitude.
We also used the to calculate distances between any two points on a sphere.
One approach the one we used would be to import all the data and calculate the distance FROM every zip code TO every other zip code.
Then you'd store and index the results.
Something click the following article SELECT User.
UserId FROM ZipCode AS MyZipCode INNER JOIN ZipDistance ON MyZipCode.
MyZipCode INNER JOIN ZipCode AS TheirZipCode ON ZipDistance.
ZipCode INNER JOIN User AS User ON TheirZipCode.
It isn't completely unworkable, but it is really big.
Also it requires complete pre-work on the whole data set, which is also not unmanageable, but not necessarily desireable.
Anyway, I was wondering what approach some of you gurus might take on something like this.
Also, I think this is a common issue programmers have to tackle from time to time, especially if you consider problems that are just algorithmically similar.
I'm interested in a thorough solution that includes at least HINTS on all the pieces to do this really quickly end efficiently.
Ok, for starters, you don't really need to use the Haversine formula here.
For large distances where a less accurate formula produces a larger error, your users don't care if the match is plus or minus a few miles, and for closer distances, the error is very small.
There are easier to calculate formulas listed on the Wikipedia article.
Since zip codes are nothing like evenly spaced, any process that partitions them evenly is going to go here mightily in areas where they are clustered tightly east coast near DC being a good example.
If you want a visual comparison, check out and compare the zipcode codes soft calculator zip 89 with 07.
A far better way to deal with indexing this space is to use a data structure like a or an.
This structure allows you to do spatial and distance searches over data which is not evenly spaced.
Here's what an Quadtree looks like: To search over it, you drill down through each larger cell using the index of smaller cells that are within it.
Wikipedia explains it more thoroughly.
Of course, since this is a fairly common thing to do, someone else has already done the hard part for you.
Since you haven't specified what database you're using, the PostgreSQL extension will serve as an example.
PostGIS includes the ability to do R-tree spatial indexes which allow you to do efficient spatial querying.
I found yours to be particularly informative.
Thank you for taking the time out of your day to provide such a thorough explanation.
Glad you liked it.
Definitely did take longer than I originally intended when I started answering the question, but I guess that's how SO works!
I downloaded a zipcode datafile from the web which contained the longitudes and latitudes of all the official US zipcodes in csv format: visit web page Rico","PR","787","Atlantic", 18.
Format "{0} {1} {2}", fromZip.
ZipArea, dist ; sw.
For example if you have a user whose zipcode is 91210 and they want to find people who are within a 10 mile radius of them then you can now simply do the following: select p.
Hope this helps EDIT: extended radius to 100 miles which increased the number of zipcode distances to 32.
Of course the solution I liked above grows with distance at query time, and this one doesn't.
However, how many rows do you wind up if you use say, 100 miles as your outer limit?
I'm not sure what the answer is without running it, but I codes soft calculator zip that it is MUCH bigger than 4M.
I'm also not sure what the practical limit of rows in SQL server is, but I suspect I'd be pushing my luck on keeping excellent performance with that many rows.
The runtime is 0.
They're not nearly as slow as I thought they might be.
Thanks, guys, for going to all that extra effort.
Your answers and comments were super helpful.
You could divide your space into regions of roughly equal size -- for instance, approximate the earth as a buckyball or icosahedron.
The regions could even overlap a bit, if that's easier e.
Record which region s each ZIP code is in.
Now, for any given ZIP code, you can get a list of regions that are definitely within your given range, and a list of regions that cross the border.
For the former, just grab all the ZIP codes.
For the latter, drill down into each border region and calculate against individual ZIP codes.
It's certainly more complex mathematically, and in particular the number of regions would have to be chosen for a good balance between the size of the table vs.
This seems like a REALLY fast way of getting some indexing done, but with a much smaller and therefore more useable indexed data set.
This might turn out faster than the solution I posted below.
I say might because I have not thought it through.
I suspect variation of this can be used to get ZipCodes that codes soft calculator zip known to be within range, and allow me to do a boxed select by Lat and Long, and then user the Haversinse Formula to calculate a much smaller number of distances.
I think there are better solutions for doing this spatial breakdown.
Actually, in my head I was treating ZIP codes as points, but this will work with shaped zipcodes too.
The roughly equally sized "regions" in my answer are meant to contain several zip codes each or parts thereof.
The idea is to do a quick, rough weed-out of regions that are obviously in or obviously out of range, so you only work with the complex, numerous, and now variable-sized!
ZIP code regions when you have to.
With zipcodes, the maximum dataset size means there's probably a reasonable balance with your method, since the dataset is finite.
Quadtrees or R-trees are the "right" way to do it though.
Select zip codes with latitudes in this range.
Longitudes are a little different, because they get smaller as you move closer to the poles.
This gives you all zip codes within a square with lengths of a hundred miles.
If you wanted to be really precise, you could then use the Haversine formula witch you mentioned to in excel bonus pay calculate out zips in the corners of the box, to give you a sphere.
Not every possible pair of zip codes are going to be used.
I would build zipdistance as a 'cache' table.
For each request calculate the distance for that pair and save it in the cache.
When a request for a distance overtime calculator pay and bonuses comes, first look in the cache, then compute if it's not available.
I do not know the intricacies of distance calculations, so I would also check whether computing on the fly is cheaper than looking up also taking into consideration how often you have to compute.
The bit about not calculating all distances is a very good point.
I suspect it'd eventually get pretty big still, with a limit of say, 100 miles.
That is basically a circle between Dallas and Houston, and people on dating sites DEFINATELY drive that far to meet one another.
However, I think I'll give it a whirl codes soft calculator zip see if it makes an improvement or not.
Remember that you need an API key to use this on your own hosting.
I was thinking about this in terms of the old solution instead of just "starting over.
I'll skip the code because I'll provide references to derive the needed formulas, and there is too much to cleanly post here.
This includes a simple WHERE clause with two Between statements limiting by Lat and Long.
Longest result was ~ 0.
Also, as a side note, it was necessary to implement the math in a couple of functions and call them in SQL.
Once I got past a certain distance the matching number of ZipCodes was too large to pass back to SQL and use as an IN statement, so I had to use a temp table and codes soft calculator zip the resulting ZipCodes to User on the ZipCode column.
I suspect that using a ZipDistance table will not provide a long-term performance gain.
The number of rows just gets really big.
If you calculate the distance from every zip to to every other zip code eventually then the resultant row count from 40,000 zip codes would be ~ 1.
This reduces the resultset from 1.
I posted an answer along these lines which might prove of interest.
Provide details and share your research!
To learn more, see our.
Browse other questions tagged or.

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