Crisis Type Determines Response Delay , Conflict Shows Bimodal Pattern, Displacement Skews Right Figure 32.1 , Crisis Type Determines Response Delay

A density plot (formally a Kernel Density Estimate , or KDE) visualises the distribution of a continuous variable by placing a smooth kernel function over each data point and summing the result across the range. The perceptual mechanism exploited is position along a common scale , the curve's height at any x-value encodes the probability density there. Peaks reveal where values concentrate. Troughs reveal gaps. Shape , symmetric, skewed, bimodal , is the primary message.

The data is three sets of continuous observations , response times in days , where the shape of each distribution is the message, not individual values. The conflict distribution is bimodal: fast-strike responses and protracted negotiations produce two distinct clusters. A histogram would either smooth that bimodality away (too few bins) or introduce false peaks from bin boundary placement (too many bins). A density plot eliminates the bin count decision entirely by using a continuous kernel , producing a shape that is stable under reasonable bandwidth choices.

A histogram's shape changes with bin width and bin origin , two parameters with no principled optimal value. The same data can appear unimodal at 5 bins and bimodal at 15. For the conflict distribution here, the bimodal structure at days 2, 3 and days 12, 15 would be visible with narrow bins but destroyed with wide ones.

The density plot trades the histogram's bin-width problem for a bandwidth (smoothing window) problem , but the tradeoff is better. Bandwidth affects smoothness, not shape topology. The bw − / bw + buttons on slide 1 let you tune bandwidth in real time. The optimal bandwidth for this dataset is 2.5 (set in data.json ). Increasing it to 5.0 would merge the conflict bimodal peaks. Decreasing to 1.0 would fragment the natural disaster distribution into false peaks.

Paste this into Claude Code to generate a working version of this chart, plus its data file. The result will not be a perfect replica , the goal is that the reader can run the prompt, get a chart of this type, and read its source. Generate a complete, self-contained density plot in D3 v7. Two files:

The ideas in this chapter didn't appear from nowhere. Karl Pearson coined the word "histogram" in 1891 and developed the system of probability distributions, skewness, and kurtosis that lets us reason about smooth density curves. His statistical contributions are foundational; his entanglement with eugenics is dark. Karl Pearson, circa 1910. AI-generated portrait based on a public domain photograph (Wikimedia Commons).

The ideas in this chapter didn't appear from nowhere. Karl Pearson coined the word "histogram" in 1891 and developed the system of probability distributions, skewness, and kurtosis that lets us reason about smooth density curves. His statistical contributions are foundational; his entanglement with eugenics is dark. Karl Pearson, circa 1910. AI-generated portrait based on a public domain photograph (Wikimedia Commons).