The CGR/USM representation of sequences offers fundamental advantages, related with its scale-independency, that make it particularly suitable to investigate the entropy distributions in nucleotide sequences [15]. That study in particular played a significant role in motivating the density kernel development reported here. It was then observed that using symmetric kernels in the Parzen window method, such as the Gaussian distribution function, to represent density of sequence patterns in iterative maps would be affected by some loss of resolution caused by overlap of memory lengths, e.g. different lengths of the sequence pattern being given the same weight because they were at the same Euclidean distance to an arbitrary position in the map. The artifactual loss of resolution can be graphically understood by noting that the projections of two sequence units can be very close to each other in the sequence map for two reasons, only one of them being directly proportional to sequence similarity described in Figure 1. The other, confounding, possibility is that place two units of distinct sequences are placed at close quarters in the sequence map because they happen to be at opposite ends of adjacent quadrants. This rare but unavoidable occurrence causes a bias in previously proposed distance metrics, including our own [3].