In fact, just having been born here makes me an atypical New Yorker. Of the approximately 8.3 million people who live in the city today, just under half were born in New York State. Eleven percent come from other US states and 40% from the rest of the world. So we’re not wrong to associate New York with immigration—the average New Yorker comes from somewhere else.
I got these numbers from the US Census Bureau, who do their best to estimate not just how many people live in each county, but how they got there: by birth, by migrating from another country, or by migrating from elsewhere in the US. When you take away the people who died, moved abroad, or moved domestically, you’re left with each of these three streams’ net effect on the population that year. Those are the numbers that will show us whether it’s unusual to move away:
1. What was the most surprising thing you’ve learned? Choose a text passage, and explain how it challenged something you assumed. (Type up the text passage / phrase, and tell us on which page we can find it!)
2. Select one of your favorite data visualizations. Is it working well becauseof a principle that Tufte explained? Or do you appreciate something about it although it goes against Tufte’s principles? – give us a link to the data vis! If you need to upload something, but it on https://imgur.com/ or Twitter.
3. Having read the book, what will you do differently the next time you design a chart?
But here’s what I only started to understand last week (and I was kind of mind-blown by that, so I’m thrilled to share it with you): Our life expectancy increases with every minute we live. When I turned 30, my life expectancy got up to 90.37 years. Once I turn 80, it’ll be 93.76 years.
But when looking at data visualizations, I noticed that the decision of which color scale to use is often not as obvious as many of these data vis books make us believe. Some data visualizations are using sequential color palettes, although they’re visualizing categories. Or the same data is visualized with a diverging color scale in one publication and with a sequential one in the next. And sometimes with classed and other times with unclassed gradients.
What are the rules, the challenges, and the trade-offs?
Let’s find out.
The next three parts of this series provide you with a “decision tree” – a Choose Your Own Adventure of data vis – by asking three questions:
Part 2: When should you use a qualitative and when a quantitative color scale?
Part 3: If you decided to use a quantitative color scale – when should you use a sequential and when a diverging one?
Part 4: If you decided to use a quantitative color scale – when should you use a classed and when an unclassed one?