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POWhat spatial indexing algorithm should I use?
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2012-10-01T18:43:16.023
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I want to implement some king of spatial indexing data structure for my <code>MKAnnotations</code>. Currently it's horribly slow when I try to filter them based on distance criteria ( 3-4k of locations, currently extremely slow with a simple double <code>for</code> ... ). I'd like to create clusters of <code>MKAnnotations</code>, to decide if it is close to another. Also, these locations are in a somewhat (creation) order and a "previous"/"next" functionality would be needed to "jump" between (this is not a must). I've read about <code>kd-tree</code> and <code>r-tree</code> structures and they both seem to meet the fast distance/neighbor obtaining option for filtering/clustering, but I'm not sure which is the best for me or if there are other options too. What algorithm/data structure should I use ? Update: I store these locations in a Core Data database, they represent a path. When the map is opened they are fetched into an array and then I just use that array for distance calculations and annotation creation. When the user moves/zooms the map, I loop through them and decide what needs to be changed on map, so it is kinda static the whole stuff. As I understood, if I'd be using a tree, I could store the locations there and when a zoom/move happens I just search through it and obtain the ones in the new region. Is this true ? Even in the dynamic case, when I can add new locations to this array, it would be a single insertion and it's happening rarely.
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<ios><algorithm><mkannotation><kdtree><spatial-index>
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What spatial indexing algorithm should I use?
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