Small multiples and predicting scientific success
data / curious
2019.09.16
Hey internet friends.
This week I'm looking at small multiples, the shape of fraud, scientific success, and deploying machine learning using Javascript.
I know, pretty wild. Check it out!
Read_
What are small multiples? Why should I use them?
Pew Research Center discusses how and why they use small multiple charts, along with what makes them so effective for communicating change over time.
What is the shape of fraud?
Check out this interesting write-up from Feedzai on how they use network visualization for detecting financial crime.
Why is D3 so har to learn from Bl.ocks?
Boy can I relate to this one. Sarah Reed explains how bl.ocks has become inaccessible for newcomers to d3. If you're posting code on bl.ocks, do everyone a favor: comment your code. It may be clear to you what each function does, but bl.ocks is meant to help people learn. So help people learn.
Explore_
When is the peak success period for research scientists?
As it turns out, no one really knows. This visualization of research paper success over the course of different scientist's careers shows a random pattern of spikes.
Analyze_
Where are all the protected areas on the planet?
The UN built a database to track protected lands across the world. You can access all the data through this API.
Where are people suffering the most from food insecurity?
The Food Environment Atlas from data.gov contain shapefiles and csv downloads on tons of different food data "such as store/restaurant proximity, food prices, food and nutrition assistance programs, and community characteristics".
Learn_
How can I use machine learning in my front-end data visualization project?
ML isn't just for data scientists. Check out this article on how to use Tensorflow.js to deploy pre-trained models or train your own. All using Javascript.
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