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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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Жители Санкт-Петербурга устроили «крысогон»17:52
大胆预测，2026 年 PPT 将不再是打工人的噩梦。但随之而来的新问题是：当 AI 能够完美代劳时，我们该如何使用 AI 生成更好的 PPT，避免自己沦为 AI 的「念稿机器」？