Gridded data formats, where the observed multivariate data are aggregated into grid cells, ensure confidentiality and reduce storage requirements, with the trade-off that access to the underlying point data is lost. Heat maps are a highly pertinent visualisation for gridded data, and heat maps with a small number of well-selected contour levels offer improved interpretability over continuous contour levels. There are many possible contour level choices. Amongst them, density contour levels are highly suitable in many cases. Current methods for computing density contour levels requires access to the observed point data, so they are not applicable to gridded data. To remedy this, we introduce an approximation of density contour levels for gridded data. We then compare our proposed method to existing contour level selection methods, and conclude that our proposal provides improved interpretability for synthetic and experimental gridded data.

翻译：格网数据格式通过将观测到的多变量数据聚合到网格单元中，确保了数据机密性并降低了存储需求，其代价是失去了对底层点数据的访问权限。热力图是格网数据的一种高度相关的可视化方式，而具有少量精心选择的等高线层级的热力图相较于连续等高线层级能提供更好的可解释性。等高线层级存在多种可能的选择方案，其中密度等高线层级在许多情况下尤为适用。当前计算密度等高线层级的方法需要访问观测点数据，因此不适用于格网数据。为解决此问题，我们提出了一种适用于格网数据的密度等高线层级近似方法。随后，我们将所提方法与现有等高线层级选择方法进行比较，并得出结论：我们的方案为合成及实验性格网数据提供了更优的可解释性。