add column to US County Dataset (and others?) for percentage non-white residents

[flannery-denny]

Reading this article from Rethinking Schools and wondering what we can learn from it.

Adam began by introducing his students to Zip Skinny (www.zip skinny.com), a user-friendly website for finding and comparing data about local communities. Our students live primarily in three San Francisco ZIP codes: the Excelsior, Visitacion Valley, and Bayview/Hunters Point. Along with mining for data in these ZIP codes, Adam selected four other ZIP codes for comparison: the Mission (an eclectic, centrally located neighborhood), the Presidio (one of San Francisco’s wealthiest neighborhoods), and the Outer Sunset and Outer Richmond (two neighborhoods along San Francisco’s Pacific coast). He asked the students to record in a table the following data: median neighborhood income, percentage of high school completion or higher, percentage of bachelor’s completion and higher, unemployment rate, and percentage of nonwhite residency.

The freshmen had to find these data independently using Zip Skinny. Then, in carefully constructed groups, they had to graph two different sets of data on the same coordinate plane in order to discover the relationship between the sets of data. One example of a scatter plot they created was comparing X = median income vs. Y = high school completion; another was X = college completion vs. Y = percentage of nonwhite residency. In this way, students could see what it means for two circumstances to be related or correlated, but not necessarily by cause and effect. They also saw the difference between a weak correlation (the points are spread out) and a strong correlation (the points are almost in a line), as well as the idea of positive correlation (one circumstance increases with the other) and negative correlation (one circumstance decreases as the other increases). As they were learning the mathematical terms for data analysis, they began to discover that math can describe and order their world.

The kids talked about the kinds of specifics that define the character of a neighborhood. For example, they mentioned the number of payday loan stores