Reporting on and visualising the pandemic
summary
Financial Times data team builds the coronavirus trajectory tracker (days since 100th case vs log cumulative cases), adds explanatory titles, annotations and an interactive toggle for log vs linear after user confusion, and runs an explainer video plus a public inbox for feedback. Google Trends searches for log scale spike to a five‑year high around March 22 2020, suggesting the outreach worked. Smooth splines are tested but rejected for clarity; animated ICU and flu comparisons drive major engagement (about 10 million views on Twitter).
AI-written summary · about
chapters (19)
- 0:00Introduction
- 0:45Data visualisation from a mass audience
- 2:09How people consume charts
- 4:20Science
- 7:45Origin story
- 14:07Making the smartest version
- 17:41Twitter feedback
- 18:53My response
- 26:22Information visualization database
- 27:45Interactive graphics
- 28:47Asking for feedback
- 30:51Setting up an inbox
- 32:10The lesson
- 33:17Big learning curve
- 34:46Rolling average method
- 38:39Prioritize ease of understanding
- 39:20Use animation
- 43:44How people read charts
- 45:23Summary
related talks
42mVisualizing COVID-19 Data
01h 05mBuilding a coronaradar
27mBeyond a Machine Learning Model
07mNew York City weathers pandemic with data-first response
01h 24mVisualizing the 2019 Measles Outbreak in NYC (with Python)
58mKeynote: DJ Patil
01h 29mThrowing Graphs at COVID
01h 55mUse your data to tell stories