Simple Scatter Plot of CEI and GCI

CISSM_CyberAttacks (Fork of Simple Scatter Plot)

This scatter plot visualizes CISSM cyber attack records, plotting event dates on the x-axis against attacker actors on the y-axis. Each circle is color-coded by the affected industry using D3’s `scaleOrdinal` and `schemeCategory10`. The visualization uses D3’s `scalePoint` for both axes and renders axis labels via the `@python-monty/axis_labels` library. The underlying data comes from the CISSM-EDA dataset, and the code forks an existing example to replace the sample data with cyber attack records.

71% match

Interactive Color Legend on Scatter Plot CISSM_CyberAttacks

This interactive scatter plot visualizes cyber attack events from the CISSM Cyber Attacks dataset, mapping event dates on the x-axis and attack actors on the y-axis. Each circle is color-coded by industry type using d3.scaleOrdinal with schemeCategory10, and the color legend displays the industry categories. The visualization uses d3.scalePoint for both axes, with axis labels generated through custom helper functions. Data comes from the @python-monty/cyber_attacksv2 package, and the chart includes an inner rectangle background to delineate the plotting area.

69% match

CISSM_CyberAttacks - Iterated

This example uses an iterated scatter plot to visualize cyber attack events from the CISSM Cyber Attacks dataset, plotting event date along the x-axis against the responsible actor on the y-axis. Each circle is colored by the industry targeted, using the `scaleOrdinal` and `schemeCategory10` schemes. The visualization leverages D3's `scalePoint` for both axes and renders data from the `@python-monty/cyber_attacksv2` package, with axis labels and a background rect for clarity. The code filters the data through custom `viz` and `axis_labels` modules built on D3 7.8.5.

67% match

CyberAttacks ScatterPlot - Final Project Draft v1

This interactive scatter plot visualizes cyber attack event types against years, with points colored by attacking actor. Users can brush over the plot to highlight specific data points. The visualization uses D3 scales (scaleOrdinal, scalePoint), axes, a color legend, and brush behavior to enable selection. Data comes from the University of Maryland CISSM Cyber Attacks Database, filtered by industry code for display.

65% match

Scatter Plot Cyber attacks

This scatter plot visualizes cyber attack data by mapping packet length on the x-axis against anomaly scores on the y-axis using D3.js v6. The code loads CSV data from a GitHub gist, parses numeric fields, and renders semi-transparent blue circles with an SVG canvas. Linear scales define the plot’s domains and ranges, while axes are generated with `axisLeft` and `axisBottom`. Labels for both axes are manually positioned, and the visualization fills the browser window with margins adjusted for tick readability.

AAkhil Charugulla

3D Scatter Plot - Bonus ICE

A 3D scatter plot displays economic indicators for G20 countries from a CSV dataset, plotting inflation rate, population in millions, and GDP in billions. Each point represents a country, with hover text revealing its name, and duplicate marker traces render the same data with distinct visual styling. The visualization uses Plotly’s `scatter3d` type via `Plotly.newPlot`, while D3’s `csv` function loads and parses the data, with axes explicitly labeled through the layout’s scene configuration.

KKevin Raj

CyberAttacks ScatterPlot - Final Project Draft v1

This scatter plot visualizes a subset of cyber attack records from the University of Maryland CISSM Cyber Attacks Database, filtering the data by a specific industry code and plotting event date against attack subtype, with points colored by industry type. It uses D3.js APIs including scalePoint for axes, scaleOrdinal with schemeCategory10 for the color scale, and custom axis and color legend modules. The visualization renders circular marks for each attack record within a fixed rectangular inner region, with the legend positioned in the lower right corner.

ICE-6 Scatter Plot

This scatter plot visualizes total emissions per country from 2000 to 2001, using a global dataset loaded via `d3.csv`. Each circle represents a country, with positions set by `d3.scaleLinear` for both axes, and colors assigned by a country-specific marker function. The visualization is rendered as an SVG appended directly to the body, with axes created using `d3.axisLeft` and `d3.axisBottom`. The chart highlights trends in emissions for select nations, with most points clustered near the origin.

Nneffer77