Data Visualization This visualization shows the distribution of incomes that can be expected for a child raised in the bottom 1% of incomes in Bowman, ND.

Published on July 25th, 2013 | by Travis Korte


Mobility across incomes in the U.S.

The New York Times interactive graphics team took income mobility data from economists at Berkeley and Harvard, and created three visualizations showing America’s mobility landscape arranged by metropolitan areas.

The first shows the relative likelihood of a rags-to-riches scenario (a child raised in the bottom fifth of incomes rises to the top fifth), highlighting particularly difficult cities for the very poor, such as Nashville and Atlanta.

The second lets users pick a region and a parental income, and outputs the distribution of children’s expected incomes. This highlights the fact that mobility is not a single quantity, and affects all income levels differently. ┬áIt also makes for some hard-to-believe observations, such as the fact that a child born into the top 1% of incomes in Gallup, NM will on average make less as an adult than a child born into the bottom 1% of incomes in Bowman, ND.

The third visualization ranks cities according to two factors: the likelihood that a child born into the bottom 5% eventually makes it to the top 5%, and the likelihood that a child born in the top 5% manages to stay there.

Take a look.

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About the Author

Travis Korte is a research analyst at the Center for Data Innovation specializing in data science applications and open data. He has a background in journalism, computer science and statistics. Prior to joining the Center for Data Innovation, he launched the Science vertical of The Huffington Post and served as its Associate Editor, covering a wide range of science and technology topics. He has worked on data science projects with HuffPost and other organizations. Before this, he graduated with highest honors from the University of California, Berkeley, having studied critical theory and completed coursework in computer science and economics. His research interests are in computational social science and using data to engage with complex social systems. You can follow him on Twitter @traviskorte.

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