But other stuff…? It was just goofing off:
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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当宠物进入运动场景，就不再是额外消费，而成为原有消费场景的自然延伸；品牌也借此打通人与宠物的生活场景，实现更完整的用户价值覆盖。
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