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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Basically, swap Avatar's Na'vi for beavers, and you've got a decent idea of what Hoppers looks like.
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近期也有多家供应链与渠道人士确认，智能手机存储芯片采购成本较去年同期已上涨超过 80%，且涨势仍未见放缓迹象。，这一点在爱思助手下载最新版本中也有详细论述