Deltagraph ternary plots4/29/2023 *Ternary plots only work if you are visualizing three variables that represent proportions that add up to 100%*. This is a direct result of the fact we are looking at a partition of value into three different quantities, and this is a requirement for ternary plots to make sense. A more math-y way to say this is it lies on the 2-simplex in R3. If you take a look at the plot above you’ll notice the points don’t fill the 3D space, they all lie on a single 2D plane embedded in the 3D space. Note this isn’t the actual data, but rather a reasonable facsimile of it as we didn’t want to include RiskRecon data in an embedded plot.įigure 3 Ternary data plotted in 3 dimensional space Here is that same data in 3 dimensions (courtesy of plotly). What we really need is a three dimensional plot of this data. Perhaps it’s a bit more dense towards the lower right (many low value hosts), but it’s difficult to tell. Unfortunately because of the density of points there isn’t much we can say about the distribution of host value here. Finally, we note that the color smoothly follows from dark purple (0% High value hosts) to yellow moving away from that boundary line.Anything above that would imply that the sum of a firm’s value proportions exceeds 100%, something that isn’t possible. This boundary represents organizations have that have all Low or Medium value hosts. No organization lies above the diagonal of the line bisected from (0,1) to (1,0).They are only members of the rationals not the reals for you math folks. These emerge because in our calculation of fractions like 1/2 and 1/3 that emerge because of how proportions of calculated out of a total number. There are some interesting patterns within the points.Let’s start by making a scatterplot and use color as our 3rd dimension.Įach organization here is a point, its percentage of “low” valued hosts is on the horizontal axis, its percentage of “medium” value on the vertical, and it is colored by the percentage of “high” value hosts. This leaves us with three variables to try to visualize. Each organization then has some fraction of each. RiskRecon categorizes hosts into 3 different value categories: Low, Medium and High. Our goal is to understand how the value of internet facing hosts spread out across organizations. We are going to build up to the chart above, so don’t spend too much time pouring over it yet. In fact, we employ these in our upcoming RiskRecon Risk Surface Report, specifically Figure 13 which appears below. There is however one way to visualize a specific type of three dimensional data called ternary plots. Therefor, unless there is a clear trend and relatively sparse data, often including size or color variations doesn’t add much to the chart. Of course color and size variations for things like scatterplots can come close, but the eye isn’t as good at understanding those patterns as it is understanding spatial patterns. This is frequently true in security where we often have categorical ratings like Low, Medium and High. ![]() Often I find myself trying to understand the relationship between 3 or more variables, and just wishing there were good ways to really see the underlying relationships. You should go read it right now! But here I want to talk about one of the more compelling charts in the report, ternary plots. The report lays out risk landscape across a number of different dimensions. This is the first report in a series covering organizational and third (and fourth and…) party risk. This week Cyentia partnered with RiskRecon to cover the Risk Surface Report.
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