Hey everyone, Friday’s here and with it, a new DataViz Weekly article is out! Look at some of the best data visualization projects we have come across out there this week:
- Exploring all attacks on journalists worldwide in 2020 — Geoff McGhee for CPJ
- Visualizing the scale of 500,000 COVID-19 deaths in the United States — Sam Hart, Reuters
- Simulating the spread of infection for different immunity scenarios — Thomas Wilburn, NPR
- Mapping every vote in the 2020 U.S. election — Kenneth Field
For us humans, data is usually easier to explore and analyze when it’s properly visualized. If you are looking for some good examples, you’ve come to the right place at the right time! DataViz Weekly is here to let you know about new great information visualizations.
Today on DataViz Weekly:
- Coronavirus vaccination pace, goals, and challenge — The Washington Post
- Coronavirus mutations and variants — The New York Times
- Distance to the nearest MiLB team in 2021 — Axios
- Age of buildings in Providence, RI — Chris Sarli
Before we start, let’s remember how Pareto charts look and what their purpose is, just to make sure we are on the same page. A Pareto chart, also a Pareto diagram, is a combination of vertical bars (columns) and a line graph. Columns are used to depict values and are displayed in descending order, left to right. The line in a Pareto chart shows the cumulative total in percentages. Such a visualization helps data scientists and analysts quickly identify the most important among a set of factors, i.e. those characterized by the largest values and therefore making the most significant contribution to the total across all the represented factors.
In this JS Pareto chart tutorial, we’ll be visualizing statistics for the leading causes of death in the United States in 2019 and find out what claimed the most American lives during that year according to official data.
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“Numbers have an important story to tell. They rely on you to give them a clear and convincing voice,” Stephen Few once said. That actually is the purpose of data visualization. On DataViz Weekly, we show you how this works in reality. Welcome to our new roundup of the most interesting data visualization projects we’ve recently found!
- Comparing live and studio versions of songs — The Pudding
- Historical wildfires in the U.S. West — Reuters
- Boston’s most desirable streets — MIT Senseable City Lab
- Inequality and COVID-19 vaccine allocation in America — GHJP Yale & C4SR Columbia
Data visualization is our passion and we are glad to high-five all who share it! Welcome to DataViz Weekly! For all of you guys, we have curated another four new awesome interactive data visualization projects worth checking out. Glance at their list below and then take a closer look:
- WorldTour transfers during the 2020-2021 offseason — Carrie Bennette
- Precinct-level map of the 2020 U.S. election — The Upshot
- Representation of age generations in the U.S. Senate over time — wcd.fyi
- Healthy Streets Index for London — _STREETS
An Angular Gauge, also known as a Circular Gauge, is a type of gauge chart with a radial scale. Such visualizations can nicely show a value within a range and are widely used in various dashboards.
The recent good news of vaccines feels like music to our ears. So, I thought why not take some interesting music data for visualization in this tutorial! The 63rd annual Grammy Awards ceremony will be held in March 2021, and when I looked through the list of the Record of the Year nominees, I wondered how popular each of these songs is. To find out, I decided to look at the number of their streams on Spotify, one of the world’s leading music streaming platforms, and thought that a Solid Angular Gauge could work well in such a visual analysis. It also resembles a vinyl record, which makes it an especially interesting chart type to opt for when representing such data!
So, along the tutorial, I will be visualizing Spotify stream counts for each 2021 GRAMMYs Record of the Year nominee song in a JS Angular Gauge chart. That is going to be entertaining! All aboard!
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