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Case Study:
Waterloo, Iowa
Methodology
Identify Affected Populations
The next step is to estimate the concentration of low-income and minority populations at the census block level. This allows the impacts of emissions and noise to be examined at a high level of spatial detail. Data on race can be obtained at the block level directly from the census. The number of low-income households can be estimated using the methodology described in Box 1.
The data can then be mapped to show either total affected populations or concentrations of minority and low-income populations by block level, in the area that will be affected by the proposed transportation projects. Figure 2 shows the minority population by census block in the study area.
Figure 2. Percent Minority Population
Source: Forkenbrock and Schweitzer (1997).
| Box 1. Estimating Low-Income Population at the Block Level
The census only reports the number of low income households at the block group level or higher. This figure, however, can be estimated at the block level based on other known block-level characteristics. For the Waterloo, Iowan MSA, Forkenbrock and Scheizer (1997) construct a regression equation that predicts population in poverty at the census block level, as follows (where A, B, C, and D are coefficients):
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Percent of persons in households below poverty level = |
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A + B * (median home value) |
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+ C * (percent owner-occupied homes) |
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+ D * (percent population over 65 years old) |
This equation is estimated from block group data for the Waterloo MSA and predicts 65 percent of the variance in poverty levels. The authors stress that the specific equation is only valid for the Waterloo area. For other areas, different variables may be appropriate, and coefficients should be estimated based on local census data. |
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