The problem is that the damage is caused by only a handful of lunatics, yet everyone ends up paying for it, simply because those responsible are so few. Collective decision control is what is missing. Since ever...
I quickly searched for La Mancha. No, is not close to the border. You need to pick another place. Unless you want to travel, of course.
That kind of “consistency” likely comes from job stability across the entire chain of command: no need to upgrade skills, no fear of replacement, deep resistance to change. It becomes an almost monolithic structure, which also means it will most likely collapse one day, and probably all at once.
A far better spread approximation can be achieved with ellipses. However, although the mass distribution is clearly improved, they still introduce a compromise in the name of geometrical simplicity that does not justify the gain. Still, it is interesting to simulate that as well.
Observable is a generous platform, and for exposure it may be worth it. Beyond that, I am not sure this project would gain much from it. Observable comes with its own constraints, conventions, functional patterns, and computational limitations. (project statistical calculation are done in Python).
The previous screenshot shows how the same mass is actually spread along the wage axis: circle versus elongated violin. My point is that GoG is not universally adequate by default, and this case exposes that weakness rather clearly.
Approximating a mass distribution with circles imposes a pseudo-symmetry and a spread around a chosen anchor (median), unrelated to the actual distribution. As my simulation shows, the circle-based packing model is nowhere near the true statistical model. (pause the animation and use hovering)
Designing interactive visualizations that follow the full depth of data complexity, so that anyone can stop at the level of understanding they feel comfortable with, is nearly impossible, given the way knowledge is actually built and layered. Yet is still nice to try, isn't it?
Jokes aside, I truly believe that any creation, visuals included, sells when it speaks the audience’s language. Using dataviz as a bridge that brings people closer to the true structure and behavior of the data is something else entirely. That is more like chasing the unknown. 1/2
What I worked on most was the Guide. With some help from AI, mainly for grammar correction, I rejected countless versions before its current form, one that mirrors quite closely the way I think. Read it and see for yourself how a geek-like mind looks at things. It may well recalibrate your wishes.
The guide is, in many ways, the post itself. It moves through several perspectives: usage, data, modeling, programming, visuals, and reflection. It is a journey through the thoughts that began the moment I first saw beeswarm encoding and realized it was not accurate in any meaningful sense.
Thanks, Jorge.
Having twins may induce a bit of a ... duplication bias, though.
Band size and movement can be adjusted as well (read the guide for interactions). In the global view, try the circle and ellipse simulations. Stop the animation right at the start, then hover and compare. Approximating an occupation with a circle is, statistically speaking, deeply misleading.
Tap, rather than just hover. A tap pins the tooltip so it can be moved and interacted with. Double tap reveals what is going on, whether on a violin or on the axis, each with its own behavior. Right click (or three dots) opens the menu and options.
Beyond #dataviz hobby.
www.linkedin.com/posts/daniel...
It also includes a built-in guide, far larger than a normal article, covering concept, math, dataviz, and development.
Desktop or mobile, with rich interactivity, experimental ideas, and a few easter eggs: center in-out size sorting, light border separation, adaptive/pinned tooltip, focus and zooming concept, unusual stack tracking, adaptive scale, shape morphing, and a nod to the law of large numbers.
Sharing the current stage of an interactive violin visualization project. The concept is already there, even if the implementation is not yet as clean as I’d like. iOS rendering quirks forced heavier drawing logic than expected.
danz68.github.io/visualprojec...
Desktop or mobile, with rich interactivity and a few easter eggs (unusual stack tracking, adaptive scale concept, shape morphing).
Monotonizing data.
Classical PAV: monotone but step-flattened.
TPM: monotone and trend-faithful, maintaining readable dynamics such as endpoints, mass, slope continuity, inflection timing, and relative growth/decay patterns.
Numbers and related fields are just a hobby for me, so hearing that any of my posts are helpful to people in dataviz is honestly very flattering.
CSP is always useful for investigating relationships during the exploratory phase of data analysis. The reason it rarely works as an explanatory solution is that we can’t exactly say: "This hard-to-read graph shows there’s no clear relationship using this method..."
Ha, glad to hear the method's been helpful. Measuring or even just describing relationships between variables has been always a tricky business.
Ah, so that’s why my posts/remarks get nearly no traction...🤭
Are you aware of any data visualization designs that use optical illusions to enhance the intended message?
While traditional beeswarms aim to reduce empty space through tight packing, my method focuses on frequency accuracy, with the space being used incidentally rather than as a packing objective.
Here is the result of my NEW frequency dot plot, an arrangement based on data density calculation using the dot size as resolution (granularity). This result actually validates the basic beeswarm packing for this dataset. Beeswarms are often poor density estimators due to their packing artifacts.
The Point Frequency Histogram (PFH) is a novel (??), simple, and highly effective visual method for accurately estimating the local density of data points.
www.linkedin.com/posts/daniel...
It was an exciting experience meeting so many statistical geeks in one place. Meeting @xangregg.bsky.social in person after more than a decade of social media debates was a particular delight. As he said, we could have talked forever.
Not really a CSP. It is a multi line/dot chart. Time and ages are just shifted variables, no chess player trajectory will have curve twists.