Skip to main content
SimilarExport ZIP
100%

Vibe Transfer Test

✓ Published0🌍 Public
CCurran Kelleher
Last edited Sep 21, 2025
Created on Sep 21, 2025

This visualization plots the relationship between automation probability and employment levels for various occupations in New York, using a bubble chart where point size encodes the estimated impact of automation. The data comes from a CSV file containing SOC codes, occupation titles, probability scores, and job counts. The example demonstrates how AI can transfer visual styling between projects, combining a dark gradient background with glowing cyan points. The implementation uses D3 v7 scales (`scaleLinear`, `scaleLog`, `scaleSqrt`), axis generators, and SVG filters for the glow effect, all managed within a React component.

AI-generated description

A fork of https://vizhub.com/pszyarto/218bf1c722c74b569aaf7670cd8ad4ac with the styles from https://vizhub.com/curran/7f4e2a0c8d0c4041a553d58b1a630779 using AI editing. It's totally possible to take the style from one viz and apply it to another! Just use the "Copy for AI" and paste that into your prompt!

MIT Licensed

Similar vizzes

Loading thumbnail…

Fork of Fork of Fork of Basic Revised Scatter Plot with React & D3 using Global AI Content Data

This scatter plot visualizes the relationship between automation probability and employment numbers for various occupations in New York. Each circle represents an occupation, with position encoding automation probability on the x-axis and log-scaled employment on the y-axis, while circle size reflects the product of both values. The visualization uses React and D3 v7, employing `d3.csv` and `d3.dsv` for data loading, `scaleSqrt` for radius scaling, and `scaleLog` for the logarithmic y-axis. Labels highlight the twelve highest-impact occupations.

Ppszyarto@wpi.edu
75% match
Loading thumbnail…

Fork of Basic Scatter Plot with React & D3 using Global AI Content Data

This scatter plot visualizes the relationship between automation probability (x-axis) and New York employment counts (y-axis) for individual occupations. Built with React and D3 v7, it loads occupation data from a CSV file and renders circles for each role. The visualization uses D3’s scaleLinear for axes, axisBottom and axisLeft for ticks, and extent to compute domains. The React app handles CSV parsing and data cleaning, including normalization of headers and numeric values.

Ppszyarto@wpi.edu
74% match
Loading thumbnail…

Fork of Fork of Basic Scatter Plot with React & D3 using Global AI Content Data

This scatter plot visualizes the probability of automation for various occupations against their employment counts in New York, using global AI content data loaded from a CSV file. Built with React and D3 v7, it renders circles scaled by a fixed radius, with axes, labels, and a linear scale for both dimensions. The app normalizes headers, parses numeric values, and filters incomplete rows before drawing, employing D3’s `scaleLinear`, `axisBottom`, `axisLeft`, and the join operator for efficient SVG updates.

Ppszyarto@wpi.edu
73% match
Loading thumbnail…

Automation and Its Impact on Jobs

This scatterplot visualizes the relationship between median income, probability of automation, and total employment across occupations. Each circle represents an occupation, with position encoding income and automation risk, while circle size reflects total employment and color distinguishes typical education requirements. The visualization uses d3.csv to load data, d3.scale.linear for scales, d3.svg.axis for axes, and includes an interactive tooltip. Data comes from automation.csv, rendered as SVG with hover effects for detailed exploration.

CConnor Rothschild
72% match
Loading thumbnail…

Automation and Its Impact on Jobs

This scatterplot shows the relationship between median income and the probability of automation for various occupations, with circle size representing total employment and color indicating typical education requirements. The visualization uses d3.v3 to load CSV data from a GitHub repository, map values to x and y scales, and render SVG circles with dynamic sizing. Hover interactions appear implemented via a tooltip element, while the chart includes axis labels for median income and automation probability, emphasizing how higher-paying jobs generally face lower automation risk.

CConnor Rothschild
71% match
Loading thumbnail…

AI ONLINE MOCKUPS

This interactive scatter plot displays the average AI adoption level and automation risk for various skills, categorized into low, medium, and high. Each circle represents a skill, positioned by its average values on the axes, colored by skill name, and labeled for identification. Built with D3 v7, the visualization uses `d3.csv` to load data from a gist, `d3.rollups` to aggregate by skill, and `d3.scaleLinear` for axes with ordinal risk labels. SVG circles, text labels, and styled axes render the final chart.

Jjrcuster@dons.usfca.edu
69% match
Loading thumbnail…

Occupation, Salary, and Likelihood of Automation

This bar chart visualizes U.S. occupational data, plotting the median annual wage for each occupation against its likelihood of automation across all 50 states. Users can switch between numeric columns (automation probability, employment, salary) using a dropdown menu, with an option to sort descending. Built with D3.js v7, the visualization uses `d3.scaleBand` and `d3.scaleLinear` to render SVG bars, dynamically detecting CSV columns and coercing string values to numbers for display.

Ppszyarto@wpi.edu
69% match
Loading thumbnail…

Number of Employed of Different Occupations in New York City

This animated bar chart shows how employment levels across 25 occupations in New York City change over time, with bars ranking occupations by workforce size and shifting positions as values fluctuate. The visualization uses D3 v6 with SVG rendering, loading data from a CSV file via `d3.csv` and interpolating between yearly data points using `d3.rollup` and `d3.pairs`. Bars transition smoothly using `transition.attrTween` to update their y-position (rank) and width (employment count), while labels and a ticker display the current year.

Ttracy3057
67% match