Gist 10926745

Gist 10926650

This map animation shows weather anomaly outliers from 1964, with markers transitioning between white and red based on average temperature type values over 1024 time frames. The Torque visualization library (carto.css) animates the data chronologically using the date_str attribute, while marker size and opacity trail behind each frame through frame-offset styling. Data aggregation applies linear interpolation to smooth the temporal transitions across the 120-second animation duration.

77% match

Gist ae4b814c65267e5d9299fbb279a83176

This visualization combines a geographical forecast with spatial clustering and outlier detection, showing how raw location data is grouped and analyzed on a map. It uses a custom D3 force simulation to arrange clusters, while the geographic backdrop is rendered through `d3.geoPath` with map projections. The clustering logic relies on KMeans and spatial clustering algorithms from an external library, and outlier detection flags anomalous points via canvas `getImageData` pixel analysis. The code integrates Three.js for enhanced 3D terrain rendering, with transitions driven by `transition.attrTween` to animate cluster movements over time. The data source appears to be simulated forecast points, processed through the clustering pipeline.

HHugoberry

75% match

Gist 798b9a5ccb654ceebb5b29ce680c5707

This visualization presents a week of daily weather data for a single location, using a circular timeline to encode conditions such as temperature, humidity, wind, and cloud cover. Each day is a concentric ring, with hourly metrics plotted as colored arcs or bars. The code relies on D3.js for layout and rendering, specifically using `d3.forceSimulation` to position labels and `d3.scaleTime` for temporal axes. Data comes from a local JSON file containing Dark Sky API-style forecast fields, rendered in an SVG canvas.

MMbrownshoes

69% match

Gist ddc6d50c313ebe6edb45519f43358c6c

The visualization displays monthly and seasonal global temperature anomalies from 1880 onward, using data from the `temperatures.csv` file. It builds a compact heatmap-style or sparkline-based grid, with each row representing a year and each cell showing the temperature deviation. The implementation uses D3.js to parse the CSV data and render the graphic, leveraging SVG or canvas primitives to represent the numeric values across the various time columns. The code processes the tabular data directly, mapping the values to visual elements through D3’s data-binding and layout functions.

Mmbostock

Gist fe5834a6c79855e23c3c7e3ae7e7f048

This example visualizes the distribution of a dataset through an interactive scatterplot, encoding three variables on the x- and y-axes and point size. The visualization uses D3.js with `d3.forceSimulation` to arrange overlapping points into a readable layout, while `d3.scaleLinear` and `d3.axisBottom` handle axis generation. Rendering is done via SVG circles, and transitions are applied using `transition.attrTween` to smoothly update positions when data changes. The underlying data originates from a static JSON array embedded in the gist, with no external data source, and the code includes hover tooltips via `d3.select().on("mouseover")` for individual point inspection.

AAlexDaGr8

Gist 954622

This visualization maps a network of species occurrence records and their associated ecoregion polygons. It shows how occurrence sets cluster together through shared taxonomic and geographic references, using an interactive force-directed layout. The code relies on d3.forceSimulation to position nodes representing occurrence sets and polygons, while d3.geoPath renders the underlying geographic boundaries from the stored polygon data. Data is pulled from Google App Engine’s datastore models, including OccurrenceSet and OccurrencePolygon, with the visualization highlighting relationships between species names, sources, and ecoregion types.

Gist 894879

This example builds an interactive map visualization using the D3.js library, displaying geographic data over a rendered world map. It applies a force-directed simulation with `d3.forceSimulation` to position nodes, while `d3.geoPath` generates the map outlines and `d3.geoProjection` handles coordinate transformations. The interface includes customizable control panels styled via CSS, allowing users to adjust map layers and layout dynamically. The visualization renders data from a gist source, combining geographic boundaries with overlaid points that respond to simulated forces for a dynamic, data-driven presentation.

Gist 956273

A graph visualization maps ecological occurrence sets and multipolygon indexes through their shared terms, using a force-directed layout to reveal clusters of related species and regions. The code builds the network from App Engine datastore models, connecting occurrences to their parent sets via reference properties. It renders nodes and edges using d3.forceSimulation, with spatial positioning driven by simulated physics rather than geographic projection, while styling reflects term rank and type metadata from the stored entities.