u/No_Theory6368

We talk a lot about voting blocs as if they're fixed, but the 2024 election showed us that even groups with strong partisan leanings can shift rapidly. Muslim American voters moving away from Democrats, Black Republicans leaving the House, these aren't just individual decisions; they're coordinated movements.

I document how this actually works using Israeli elections as an example (I"When Rigid Blocs Crack: Elite-Coordinated Voter Switching in an Identity-Based Party System" https://www.preprints.org/manuscript/202511.2148) .

The key finding: bloc defection can occur when community elites signal that switching is acceptable or necessary. It's not about individual voters suddenly changing their minds; it's about permission structures and coordination.

For election mappers, this matters because it means we can't just assume historical patterns will hold. When you see a bloc starting to crack, look for the elite-level signals; endorsements, community leader statements, organizational shifts. Those are your leading indicators, not just polling of individual voters.

Happy to share more on the methodology if anyone's working on forecasting models that incorporate these dynamics.

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Boris Gorelik, Data scientist studying AI reasoning, platform dynamics, and quantitative social science

S&P Global is a reputable company, and they recently produced a chart about China's power sector that looks impressive. When I actually tried to read it, though, it turned out to be almost unreadable. So I redesigned it. You can see the data and the code in this gist.

So, what is the problem?

As I argue in "C for Conclusion", the title of a graph should be its conclusion: a short, declarative claim that tells the reader what they are supposed to see. In the S&P chart, the title takes about two minutes to read, and once you're done, it is still completely detached from the graph itself.

A separate, very important problem is the double y-axis. This is a big no in data visualization. For example, the point where the red emissions line crosses the brown coal area visually implies that the two values are equal. They are definitely not. They are on different scales and in different units entirely (TWh vs. GtCO₂). There are many more problems with double y-axes (spurious visual correlations, arbitrary choice of scaling, etc.), but that is the cleanest example in this chart.

What I did

The key was to use the title as the guiding force towards the visualization.

1. I asked Claude to restate the title, making it much shorter, so its meaning is obvious in one read.
2. Then I asked Claude to digitize the data from the chart pixels. I admit digitization probably introduced some errors (~±3–5%).
3. Then I asked it to create a better version following standard dataviz principles. The result was already much better. My prompt was

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this graph is bad in many ways. First - extract the data from the graph, then, shorten the title to still be a conclusion, next - create a static graph that shows the conclusion in the graph, adhering to best data visualization principles. Pay special attention to data-ink ratio and to useful redundancy and information layers

4. Then I asked Claude to simplify it even more. This is what you see right now.

To sum up

A chart's title should be its conclusion, and the chart should back that conclusion up at a glance. If the reader has to squint at a three-line title, reconcile two y-axes in their head, and distinguish between three near-identical shades of blue before they can extract the point — the chart is doing far less work than it should. Fixing this does not require fancy tools. It requires deciding what claim you are making, and then letting the visual serve that claim.

Data source: Digitized from S&P Global, Look Forward: Energy Futures (Feb 2026). Values are approximate (±3–5%) due to pixel-level reading of the original chart. For rigorous use, substitute numbers from Ember's Global Electricity Review or the IEA World Energy Outlook.

Tools: Python, matplotlib, Claude (Anthropic).

Code & data: https://gist.github.com/bgbg/ed7119d41c74332ddc9268e747994ea0

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Boris Gorelik
Data visualization lecturer and consultant