Interpretable contour level selection for heat maps for gridded data

TL;DR

This paper introduces an approximation method for selecting density contour levels in heat maps of gridded data, enhancing interpretability without needing access to original point data.

Contribution

It proposes a novel approximation technique for density contour levels tailored for gridded data, addressing limitations of existing methods that require point data.

Findings

Proposed method improves interpretability of heat maps.

Outperforms existing contour level selection methods.

Effective on both synthetic and real-world data.

Abstract

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.