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This example shows a scatterplot matrix comparing six environmental metrics—forest cover, agriculture, wood removal, planted forest, and primary forest—across Brazil, the USA, and China, using data from Env.csv. It displays pairwise relationships between all traits, with points colored by country and a legend identifying each nation. The visualization uses D3 v4 to render SVG shapes, scales, axes, and a brushing interaction that highlights selected data points by hiding others. Each cell is built with the `cross` function, relying on D3 scales and the `d3.brush` API for filtering.

91% match

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This interactive scatterplot matrix visualizes an environmental dataset, with each cell showing the relationship between two variables such as age, income, and education. Users can brush across any axis to filter the data points, which hides or highlights circles across all cells simultaneously using a brush interaction implemented with `d3.brush`. The visualization is rendered as SVG using D3 v4 within a React framework, with axes scaled via `d3.scaleLinear` and colored by categorical country data. A legend identifies the three color-coded countries: China, Brazil, and USA.

86% match

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This example uses a parallel coordinates chart to compare countries across multiple dimensions—population, surface area, and agricultural land percentage—over several decades. Grey background lines provide context, while steel blue foreground lines highlight the highlighted data points. Brushing on any axis filters the displayed lines, showing only those that meet the selected criteria. The visualization is built with D3 v3, using `d3.scale.ordinal` and `d3.scale.linear` for axes, `d3.svg.line` for paths, and `d3.svg.brush` for interactive filtering. Data is loaded from a CSV file.

75% match

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A stacked bar chart displays forest area data for ten countries, with bars divided into colored segments representing different forest types (surface, forest, plantforest, primaryforest, privateforest). Users toggle a checkbox to switch between absolute values and percentages, which rescales the bar segments accordingly. Clicking a legend item filters the chart to highlight a selected forest type, animating the bars with `transition.attrTween`. The visualization uses D3 v4 with SVG rendering, reading from a CSV file, and includes interactive animations for data exploration.

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This is a scatterplot matrix visualizing relationships among six traits from an environmental dataset, with each cell showing a plot for a pair of traits. The data points are colored by eventname using a categorical color scale, and users can click cells to filter points via a brush interaction. The visualization is built with D3 v4, using scales like `d3.scaleLinear` and `d3.scaleOrdinal`, a brush API for filtering, and SVG rendering within a React framework. The data is loaded from a CSV file, and a legend labels the three event categories.

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This visualization combines a scatter plot, a map of US states, and a time-based brush to explore agricultural productivity data. The scatter plot displays each state’s score against its creation time, with circle size reflecting comment count, while the map overlays proportional symbols sized by agricultural productivity. A brush timeline filters the data across all views simultaneously, and hovering over a point or map symbol highlights the corresponding element in the other charts. The code uses d3.svg.brush, d3.geo.albersUsa, d3.geo.path, and d3.svg.axis to render the interactive SVG elements.

Bbl75d

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This visualization displays a scatter plot of environmental data from the Data Canvas Sense Your City dataset, with each point positioned by its geographic coordinates. The plot is rendered over a world map built from the TopoJSON file `countries.gro.json`, using `d3.geoPath` to project and draw the country outlines. Data points are plotted using d3 scales and SVG circles, with the map projection and scatter plot layers combined in a single d3.js visualization. The example leverages d3’s data-binding and geographic projection APIs to overlay sensor readings on a global context.

Mmclee933