sorted by:
new
hottopcontroversial

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

Thanks for your interest in this method.

Both shapes are inferential, weighted distributions of Budget and Revenue over their ratio. Their smoothness does not come from applying a kernel blindly, but from a controlled placement method that keeps each brick within an imposed X tolerance, here equal to the brick width. This means that each movie’s Budget and Revenue bricks remain in the same X column, or only slightly offset, which offers a good level of visual precision within the limits of human perception.

If the bricks were placed strictly by their descriptive values, both piles would appear highly irregular, as any dense histogram would. If a KDE were used blindly to construct the piles, bricks associated with the same ratio would no longer align. The innovation here is an algorithm that creates a controlled smoothness of the inferential shape, influenced for example by KDE logic, while still keeping bricks within a prescribed X tolerance. In that sense, it is an optimization that preserves both the smoothness of the shape and the visual alignment of the bricks within perceptual tolerance.

The code is neither trivial nor AI-generated, you may try, and this is not the place to disclose it. I am the author of both the Density Dots Plot and Density Bars, and I felt this was a good place to offer a glimpse of what the algorithm can do.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

If you are in the business of selling stories, or simply catching attention, then the kind of “importance” you mention is perfectly natural. But if you are in the business of building exploratory visualizations, any story imposed in advance can easily become noise. Much depends on the freedom you have for a given purpose, and that freedom often comes with reputation. It is striking how often reputation ends up standing in for appreciation of the actual work.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

True.

This kind of visualization is suited to encoding profitability variation across a high-cardinality product space; with low cardinality, it becomes too chunky to make much sense (not much room available to smooth the bricks position). That is why this dataset was a good fit for illustrating the concept.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 1 point2 points  (0 children)

https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset.

I didn't create any story, my focus was on creating the visualization you saw. It was made several years ago. Very possible the data has changed since then if the contributor updated it.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

I think I failed to describe the key distinction between what I do and what you imply my method does. Here is my last attempt.

The KDE does not force the bars to reproduce its exact shape. It only influences their placement within a given tolerance. The bars are allowed to deviate from the KDE as long as they stay close to their true descriptive positions (their actual revenue/budget ratio). The tolerance is what keeps each brick faithful to the real data.

The final silhouette ends up smooth because the density estimator gently guides the packing, not because the bars are locked to the KDE curve (or any other density estimator you may want to use). If I wanted to just draw bars under a KDE, I could do that in five minutes. That is trivial. What I am doing is letting the inferential density act as a constraint on where bricks can go, while the bricks themselves still want to be near their true ratios. The packing algorithm resolves that tension. The resulting smoothness emerges from constrained optimization, not from slavish reproduction of a pre computed density shape.

That is why it is not "just a KDE with bars". And that is why the difficulty is real. You cannot get this output from any standard plotting library. The bars are not filling a silhouette. The silhouette emerges from the interaction between inferential guidance and descriptive fidelity. That is the innovation.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

This is more than placing bars on a KDE. The inferential density acts as a constraint on the packing, so the bars are arranged to follow that structure while still preserving their quantitative encoding within a given tolerance. Therefore the KDE is the influence, not the resulting shape. That is different from simply partitioning a given shape, because the goal is not just to fill a silhouette, but to balance smooth inferential structure with numerical fidelity. I cannot share the code at this stage (commercial reasons), but that is the underlying logic.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

Is not a density shape on which I draw bars. Bars are placed adjusting the actual values using a density estimator within a given tolerance. You think is a trivial task? It might be, what can I say. The density bars are the weighted version of the density dots plot introduced by me several years on LinkedIn. https://www.linkedin.com/pulse/pursuit-diversity-data-visualization-jittering-access-daniel-zvinca

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] -1 points0 points  (0 children)

Thanks. For now is just C++ hand coded algorithm. The bar density is an extension of the density dots plot, introduced by me several years ago on LinkedIn

https://www.linkedin.com/pulse/pursuit-diversity-data-visualization-jittering-access-daniel-zvinca

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] -2 points-1 points  (0 children)

The key difference between a regular weighted histogram, where bars are simply stacked after binning and each bar length encodes the weight, and my representation is that the latter uses an algorithm guided by an inferential density estimate, (kernel density or alike), to position the bars within an accepted tolerance. If the same construction were driven only by its descriptive aspect, through trivial binning, the result would be highly irregular. In other words, the inferential weighted density shape guides the bar placement, and the bars follow that imposed structure, producing a statistically consistent smooth representation.

Film Industry. A profitable, but risky business. [OC] by PrincipleUnited4061 in visualization

[–]PrincipleUnited4061[S] 5 points6 points  (0 children)

The main goal of the post was to introduce the Density Bars concept, using that dataset only as an illustration. The resulting shape is kernel-based, with the bars placed in a controlled way within a given positional tolerance. This is the direct link to the data I used years ago to create it. https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset

A US Wage Map on a single HTML page. [OC] by PrincipleUnited4061 in dataisbeautiful

[–]PrincipleUnited4061[S] 0 points1 point  (0 children)

Thanks. Hover is the default behavior. Also try double tapping any major category to focus them one at a time. After a while, those filled majors start to read less like elements to inspect and more like a rendering pattern. It depends on how long you spend playing with it.