The New York Times takes a closer look at the data that apps collect and what they know about you:

At least 75 companies receive anonymous, precise location data from apps whose users enable location services to get local news and weather or other information, The Times found. Several of those businesses claim to track up to 200 million mobile devices in the United States — about half those in use last year. The database reviewed by The Times — a sample of information gathered in 2017 and held by one company — reveals people’s travels in startling detail, accurate to within a few yards and in some cases updated more than 14,000 times a day.

The animated visuals in this piece are nice, strengthening the big numbers with small anecdotes. Because the only way to make people care about data privacy is to be as creepy (but responsible) as possible.

Deliveroo is a service that picks up and delivers food. Data from their delivery riders showed that it was faster to ride a bike than other modes of transportation in cities. Carlton Reid for Forbes:

Smartphone data from riders and drivers schlepping meals for restaurant-to-home courier service Deliveroo shows that bicycles are faster than cars. In towns and cities, bicyclists are also often faster than motorized two-wheelers. Deliveroo works with 30,000 riders and drivers in 13 countries.

That bicyclists are faster in cities will come as no surprise to bicycle advocates who have staged so-called “commuter races” for many years. However, these races – organized to highlight the swiftness of urban cycling – are usually staged in locations and at hours skewed towards bicycle riders. The Deliveroo stats are significant because they have been extracted from millions of actual journeys.

I used to play this game in graduate school often. The bus would get stuck in traffic. I would get off and walk home instead in the most thrilling races the world has ever seen. So for cities, these results make a lot of sense. Maybe not so much for the burbs though.

Based on data from Expedia, this is an interesting one from The Economist. Using polar coordinates, they used angle to represent percentage change in ticket prices and the radius to represent the distance of a flight.

Too bad they couldn’t get more data from Expedia. I would’ve liked to see the price changes for more flights, especially shorter ones to use as a point of comparison.

I’m just gonna put this xkcd comic right here.

You’ve seen the maps of population density. You’ve seen the jokes. But you haven’t seen population at high granularity in a 3-D view. Matt Daniels for The Pudding used a mountain metaphor to show the peaks and valleys of population around the world.

You get more out of the data in this view than you would the overhead, which tends to obscure the variation in large cities. Although if you must, Daniels also provides the typical view to reference.

Mark Hansen for The Upshot describes the search for balance between individual privacy and an accurate 2020 Census count. It turns out to not be that difficult to reconstruct person-level data from publicly available aggregates:

On the face of it, finding a reconstruction that satisfies all of the constraints from all the tables the bureau produces seems impossible. But Mr. Abowd says the problem gets easier when you notice that these tables are full of zeros. Each zero indicates a combination of variables — values for one or more of block, sex, age, race and ethnicity — for which no one exists in the census. We might find, for example, that there is no one below voting age living on a particular block. We can then ignore any reconstructions that include people under 18 living there. This greatly reduces the set of viable reconstructions and makes the problem solvable with off-the-shelf software.

To combat this, the Census is looking into injecting more uncertainty into their published data. The challenge is figuring out how much uncertainty is too much and what level of privacy is enough.

This interactive heatmap by Jonas Schöley shows mortality rates by age. Just use the dropdown menu to see the data for various countries. You can also compare male and female populations and countries.

As you might expect, you can see mortality rates decrease steadily, especially in the younger ages. Spikes or abrupt color changes might indicate war or disease. [via @maartenzam]