Gaining precision by deleting data

The title is a bit of a paradox, isn't it? When we want more precise knowledge about something, we want to gather more data, at greater granularity. But it's not that simple.

Here is the famous "wind map" by the New York Times (link) showing vote margin shifts in the U.S. Presidential elections from 2020 to 2024, at the county level. A red arrow pointing rightward indicates a county in which the voters shifted toward the Republican candidate (Trump). It paints the red wave story loud and clear.

Even though every county is on the map, this map alone doesn't answer all possible questions about vote margin shift. For example, someone might be interested in the vote margin shift in counties with high Hispanic populations. It's impossible to learn this from the above map, even if one has a list of the names of these counties.

The answer is found in the following map, published by NBC News here:

The story is also very clear. This map can be thought of as the NYT map minus the counties that have negligible Hispanic populations. By deleting all unrelated data, the designer highlights the story about Hispanic voters.

The reader can use the tab up top to see partial shift maps that emphasize different demographic groups. Well done!