I tend to struggle with reading heat maps, especially tree maps that render multiple boxes of various sizes nested within one-another, all with different levels of saturation. They just don't do it for me.
Recently, however, I came across a blog wherein a heat map was overlaid on top of a storage warehouse with the goal of optimizes the location of items within the warehouse based on frequency of retrieval. The goal of the warehouse analysis was to reduce unnecessary foot traffic to items with higher frequency retrieval; items rarely retrieved should be located farther away from the warehouse entrance.
The use of heat map for this analysis makes sense to me, perhaps in part to the literal representation of the warehouse itself, rather than the abstract construction of some heatmaps that use nested boxes. Perhaps a tweek to this approach might be to use a gray-scale approach, rather than color: i.e., black = highest retrieval frequency, lightgray= least frequency.
Anyway, here are two graphics from the blog that illustrate an optimized warehouse vs the actual heat map of the warehouse.