We use &quot;data cubes&quot;, as per Jim Gray&#x27;s paper, <a href="http:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;cs345d-01&#x2F;rl&#x2F;olap.pdf" rel="nofollow">http:&#x2F;&#x2F;web.stanford.edu&#x2F;class&#x2F;cs345d-01&#x2F;rl&#x2F;olap.pdf</a>.<p>When I say &quot;screen&quot;, I mean (e.g.) your laptop&#x27;s screen resolution. If you&#x27;re going to display a heatmap on a screen with p pixels, we (roughly) touch only O(p) memory cells on query time, independently of the dataset size.<p>When you say &quot;you don&#x27;t see the uniqueness&quot;, I&#x27;m not sure what you&#x27;re comparing it against, so I can&#x27;t say anything more. You mentioned gkd-trees earlier: is that the comparison you mean? In that case, these are two completely different data structures. For example, (at least as described on the 2009 siggraph paper), you can&#x27;t subset on a categorical dimension of the pixels. In the case of the data structure we created, we can report (for example) a heatmap of all geolocated tweets generated by an iPhone, or all geolocated tweets generated by a windows phone, or all geolocated tweets irrespective of device, <i>without having to scan the 200M tweet database</i>.