I used Epi Info to make a map of some statistical information from Indiana for 1998. I made another map of the same data for 1999, and so on through 2007. I treated the maps as still photos and made a video of them, which is now available on YouTube. This post explains what the video shows.
The video shows a map of Indiana and its counties over a ten-year period, from 1998 through 2007 inclusive. The counties are represented in various colors. The colors show whether counties fared well or poorly during each of those years. The best outcomes are in deep green. Nearly neutral outcomes are on the boundary between light green and yellow. The worst outcomes range from yellow through orange to red.
The calculations behind these maps begin with year-by-year data from the U.S. Census Bureau.
The specific data sources used are described in
the other post.
These maps show the relationship between two streams of county-by-county data. One is the per capita income, adjusted for inflation. The other is the number of people in poverty.
For both of these data streams, I calculated rates of change. So, for example, the video begins with 1998. Warren County, at the left edge of the state, appears in red. Red indicates an extreme divergence between changes in per capita income and in poverty rates. In most if not all cases, moreover, red indicates that the divergence is undesirable.
In the case of Warren County in 1998, the situation is as follows. Per capita income dropped very slightly (i.e., by only 0.2%), from $23,577 in 1997 to $23,527 in 1998. Unfortunately, the number of persons in poverty rose 13.5%, from 644 in 1997 to 731 in 1998. So there was not a general recession or other drop in earnings shared equally by everyone. Indeed, the stable per capita income raises the question of how many people actually experienced an increase in income.
Warren County stands out, in 1998, because the ratio of its change in poverty rate to its change in per capita income was greater than 60:1. It stands out in red because that change signals bad news for poor people. It would have stood out in green if the ratio had been 60:1 in poor people’s favor – if, that is, there had been a slight increase in income and a dramatic decrease in poverty. In that case, it would seem that the county channeled much of its additional prosperity, that year, into an improvement in the conditions of the poor.
The color scheme used on these maps, then, ranges from red down through orange to yellow, as the bad news for poor people becomes progressively less bad, and from yellowish green up through deep green as the good news for poor people becomes progressively better. The color gradations go in steps of twenty: that is, red is for a ratio of worse than (negative) 60:1; a dark shade of orange accounts for ratios between 40:1 and 60:1; a lighter shade of orange represents ratios between 20:1 and 40:1; and so on down to zero and then up through the deepest green at 60:1. There is nothing magical about those particular gradations. They were chosen for simplicity. The seas of yellow and green that appear in a few years depicted in the video suggest that closer gradations might have provided more information.
One step I took that now appears to have been a mistake was to eliminate a half-dozen extreme values that I considered outliers. Had I not done that, there would have been a handful of additional counties shown in the deepest reds and greens throughout this ten-year period.
This presentation does not purport to be definitive, or even scholarly. Along the lines suggested in the refinements just mentioned, a high-quality product would call for manual analysis of a number of counties, like the analysis of Warren County provided above, so as to insure that representative and appropriate colors were used for all counties. Data and calculations used here have not been carefully proofread. The spot checks that I have done do seem to indicate accuracy in the basic calculations.
One technical refinement that will become more feasible in future years, as data become more readily available, could involve a finer-grained analysis by zip code and/or census tract. Another refinement worth considering would be to overlay an indication of population centers. Also, if the video were converted to, say, a PDF, it would also be possible to create links or tooltips for each county, so that mousing over or clicking on a county would bring up or lead to the underlying data.
The video suggests some areas for further inquiry. It appears, in my review thus far, that a number of the most extreme contrasts appear in counties in the regions of Chicago, Evansville, Indianapolis, and Louisville – and also around West Lafayette. Also, it seems that some counties tend to experience the same trends: they are the same colors as one or more of their neighbors in most if not all of the years depicted. There also appear to be years of greater and lesser homogeneity among the counties – such as the contrast between 1998 and 1999. Closer investigation of sharp divergences among neighboring counties (such as in 2004) could also lead to indicia of balkanization, where large employers or governmental policies yield marked departures from (and possibly distortions in) the general tendency in the state for the year.
As noted in the other post, there were some technical difficulties in the preparation of this video. It was, nonetheless, an interesting project. I hope the links provided in these posts, and the techniques used in the video, lead me and/or others to undertake further analyses of this kind.
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