I know how to implement n log n closest pair of points algorithm (Shamos and Hoey) for 2D cases (x and y). However for a problem where latitude and longitude are given this approach cannot be used. The distance between two points is calculated using the haversine formula.
I would like to know if there is some way to convert these latitudes and longitudes to their respective x and y coordinates and find the closest pair of points, or if there is another technique that can be used to do it.
I would translate them to three dimensional coordinates and then use the divide and conquer approach using a plane rather than a line. This will definitely work correctly. We can be assured of this because when only examining points on the sphere, the two closest points by arc distance (distance walking over the surface) will also be the two closest by 3-d Cartesian distance. This will have running time O(nlogn).
To translate to 3-d coordinates, the easiest way is to make (0,0,0) the center of the earth and then your coordinates are (cos(lat)*cos(lon),cos(lat)*sin(lan),sin(lat)). For those purposes I'm using a scale for which the radius of the Earth is 1 in order to simplify calculations. If you want distance in some other unit, just multiply all quantities by the radius of the Earth when measured in that unit.
I should note that all this assumes that the earth is a sphere. It's not exactly one and points may actually have altitude as well, so these answers won't really be completely exact, but they will be very close to correct in almost every case.
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