The quadtree is the two-dimensional case of a broader family of space-partitioning data structures. Octrees extend the same idea to three dimensions (splitting cubes into eight children), KD-trees use alternating axis-aligned splits (splitting along x, then y, then x again), and R-trees group nearby objects into bounding rectangles. Each variant makes different tradeoffs between construction time, query speed, and update cost.
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There's a tradeoff: a lower capacity means you can skip more space during queries (you zoom in faster), but the tree has more nodes and uses more memory. A higher capacity means fewer nodes but each node requires checking more points linearly. As a starting point, capacities between 4 and 16 are reasonable defaults, though the best value depends on your data distribution and query patterns.