In Climbing Income Ladder, Location Matters from The New York Times (July 22, 2013)
This data visualization breaks down, by county, the percentage chance that a child raised in the bottom fifth of the income distribution ladder will rise to the top fifth.
The study’s range of the top fifth: family income of more than $70,000 for the child by age 30 or more than $100,000 by age 45. (Shown in the third and final graph) For the bottom fifth: parents’ income less than $25,000 (top fifth: parents’ income above $107,000).
The study based it’s findings on the possibility of upward mobility in metropolitan areas mainly on education, family structure and economic layout of metro areas. Areas with higher levels of mobility tended to have stronger secondary school systems.
Counties in states in the West, Northeast and Great Plains regions showed the most favorable opportunity for advancement while the Southeast (Mississippi, Georgia and South Carolina) showed the poorest chances.
-The story accompanying the data visualization provides color and depth as it leads with a character and then ends with the same character, spotlighting Atlanta.
-The colors on the first map representing the percentages, I think, work well for navigation and comprehension.
-The second map, which includes a clickable map and search bar that allows you to type in and track, by city, where a child may sit on the income ladder by the age of 30, based off what his/ her parents earned in the late 1990s, is complex in that it provides a lot of information, but it was still kept simple and easy to understand.
-The third graph is a slope graph highlighting the 30 most populous cities from “best to worst” on the left and right by chance of mobility and based off of where the child or children were raised on the income ladder. The goal of the study and data was to target metro areas, so I think the third graph (slope graph) succeeds in accomplishing that while the other two provide more of a broad picture to show what’s happening across the country.
-Shows “correlation not causation.” But, would causation be difficult to show if there was more information about school systems, demographics and where the income lives in the areas?
-missing a few counties, but oh well.
-Despite having a massive amount of information, It would’ve been nice to see a small tidbit somewhere that showed demographics or information about the school systems in the areas–to tap in to the correlation that information would have with income–but with three very interesting, detailed graphs already included, I thought it was fine that the school and demographics information was just left to text.