I need to write a program that does the following:

  • Take an input list of objects whose properties include latitude and longitude to, say, 5 decimal places
  • Store them in a data structure once
  • Provide a "nearby" lookup function that can efficiently return the N closest objects for a given lat/long

Currently, I'm doing the following, which is suboptimal:

  • Store all objects in a hash, with array keys like [integer_latitude, integer_longitude]
  • At search time, find all objects in an arbitrary-sized circle around the target. Eg, if the search is at [0,0], I can get all objects within 1 degree by pulling [-1,0], then [0,0], then [1,0], then [0,-1], etc.
  • Order the found objects by actual distance to the target and take the top N

This is obviously inefficient, because often there are many more matches than N.

One improvement could be to examine locations in concentric squares outward from the center: all points 0 degrees from the center, all points 1 degree from the center, 2 degrees, etc, and stop after the first square when I have at least the number of objects needed. Then I could sort those by actual, fine-grained distance and take the top N.

Is there some well-established way of doing this search efficiently?

  • $\begingroup$ Another idea would be to use a tree of geohashes, where the geohash gets longer as you go toward the leaves. You could move to a parent to find "near" objects. But I think this fails when two objects are very close together but just opposite a boundary like the equator. $\endgroup$ Commented Mar 2, 2015 at 22:33
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    $\begingroup$ Have you read this: en.wikipedia.org/wiki/Nearest_neighbor_search ? $\endgroup$
    – HEKTO
    Commented Mar 2, 2015 at 22:47
  • $\begingroup$ How about dictionary? Or 4 directional linked lists? $\endgroup$
    – S.Dan
    Commented Mar 3, 2015 at 4:37
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    $\begingroup$ Closely related question; the comment applies. $\endgroup$
    – Raphael
    Commented Mar 3, 2015 at 7:37
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    $\begingroup$ I think quadtrees might give you a first approximation. They are usually good to explore some part of the plane, as a 2-dimensional counterpart to binary-trees. But I am no expert on that. $\endgroup$
    – babou
    Commented Mar 3, 2015 at 8:28

3 Answers 3


Yes, you could you could do better than a naive approach - you could use a k-d tree data structure.

Clever implementations, such as sklearn k-d tree do the nearest neighbour lookup in O(log(n)) which, unless you have a high dimensional and sparse dataset, will work significantly faster than a naive O(n) approach.


As well as k-d trees, it's worth knowing about R-trees, as these are arguably the most popular data structure for indexing in the GIS world.

R-trees are to k-d trees as B-trees are to binary search trees. Like B-trees, R-trees tend to be very cache efficient, which makes them extremely useful for on-disk data structures as you tend to find in databases.


There are obviously many different data structures out there - you could also use a cover tree


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