The efficiency depends on the query size relative to the data distribution. A small query in a sparse region prunes almost everything. A query that covers the whole space prunes nothing (because every node overlaps), degenerating to a brute-force scan. The quadtree gives you the most benefit when your queries are spatially local, which is exactly the common case for map applications, game physics, and spatial databases.
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This map illustrates the OsmAnd routing concept. The route starts in the Start Area Cluster (221558), moves to the nearest Border Point, and continues through precomputed Shortcuts across intermediate clusters. It then enters the Finish Area Cluster (221536) via another border point and finishes using local roads. This method speeds up routing by combining local search with efficient inter-cluster shortcuts.
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