2019 Ridgecrest Earthquakes

2019 Ridgecrest Earthquakes

This visualization displays earthquakes from the 2019 Ridgecrest sequence as a scatter plot, with magnitude on the x-axis, depth on the y-axis, and point size also encoding magnitude, while color indicates magnitude type. The data is filtered to include only events with low RMS error, and is loaded using d3.csv from a Gist. The chart is built with the vega-lite-api, which wraps vega-lite and vega, and uses vega-tooltip for interactive hover details on each earthquake.

97% match

2019 Ridgecrest Earthquakes

This visualization plots 2019 Ridgecrest earthquakes as a scatterplot, with time on the x-axis, root-mean-square error on the y-axis, and point size encoding magnitude. Tooltips reveal date, magnitude, and depth for individual quakes. Built with vega-lite-api, it registers Vega and Vega-Lite and uses d3.csv to load data. The visualization renders as SVG, uses a custom configuration for axis styling, and fits its dimensions to the viewport.

96% match

2019 Ridgecrest Earthquakes

This visualization plots over 1,500 earthquakes from the 2019 Ridgecrest sequence, showing magnitude on the x-axis versus depth on the y-axis. Each semitransparent black circle represents one quake, with tooltips revealing the exact date, magnitude, and depth. The code uses the vega-lite-api to construct the mark-circle encoding, with d3.csv for data loading. The dark-themed config customizes axis and label styling, while vega-tooltip provides interactive hover details.

95% match

2019 Ridgecrest Earthquakes

This visualization displays earthquakes from the July 2019 Ridgecrest sequence, filtering data to a specific time window and geographic region. Each circle represents an earthquake, with position encoding time and latitude, size encoding magnitude, and color encoding magnitude type. The code uses the vega-lite-api library to construct the specification, registers it with Vega-Lite and Vega, and loads filtered data via d3.csv. Data comes from a GitHub Gist, with RMS error, depth, and custom date parsing applied. Tooltips show date, magnitude, and depth values when hovering.

2019 Ridgecrest Earthquakes

This visualization shows earthquakes from the 2019 Ridgecrest sequence as a scatterplot of time against root-mean-square error, with circle size encoding magnitude and color encoding magnitude type. It filters the data to events with low RMS and a date before the main shock, then renders the marks using the vega-lite-api, which builds a specification executed by Vega-Lite and rendered as SVG via Vega. The code loads data with d3.csv, applies custom axis and label styling through a config object, and includes a tooltip for displaying date, RMS, and depth on hover.

2019 Ridgecrest Earthquakes

This visualization maps the 2019 Ridgecrest earthquake sequence as a scatter plot, showing the spatial distribution of epicenters with longitude and latitude on the axes. The size of each circle encodes earthquake magnitude, while its color indicates the magnitude type (e.g., ml, mw). Tooltips provide the date, magnitude, and depth of each event. The visualization is built using the Vega-Lite API, an abstraction layer over Vega and Vega-Lite, with D3 for data loading, and includes custom configuration for axis styling and tooltip appearance.

2019 Ridgecrest Earthquakes

A scatterplot visualizes over a year of earthquakes in Ridgecrest, California, with time on the x-axis, depth on the y-axis, and point size encoding magnitude. The visualization uses the vega-lite-api to build the chart, which is rendered with vega and vega-lite, while d3.csv loads the data. Points are semi-transparent to show density, and tooltips display date, magnitude, and depth details.

2019 Ridgecrest Earthquakes

This interactive visualization displays earthquake activity from the 2019 Ridgecrest sequence, plotting each event by time and latitude while encoding magnitude through circle size and fault type via color. Built with the Vega-Lite API, it filters the USGS-derived dataset to include only earthquakes with low RMS values and specific geographic boundaries, rendering them as semi-transparent circles. The visualization uses d3 to fetch and parse the CSV data, while Vega-Lite's declarative encoding handles the mapping of data fields to visual channels, with tooltips revealing depth and magnitude details.