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126 A Guidebook for Using American Community Survey Data for Transportation Planning Table 7.6. Calculation of index of dissimilarity with confidence intervals. PUMA a/A SE(a/A) b/B SE(b/B) a/A-b/B SE(a/A-b/B) ID 35.34 3601 0.102 0.0033 0.037 0.0007 0.065 0.0034 3602 0.081 0.0017 0.040 0.0010 0.041 0.0020 SE(ID) 3603 0.078 0.0021 0.063 0.0018 0.016 0.0027 0.51 3604 0.080 0.0017 0.056 0.0016 0.024 0.0024 3605 0.081 0.0015 0.133 0.0036 0.052 0.0040 ME(ID) 3606 0.092 0.0023 0.155 0.0011 0.063 0.0025 0.84 3607 0.034 0.0009 0.177 0.0040 0.144 0.0041 3608 0.097 0.0028 0.077 0.0014 0.020 0.0032 3609 0.079 0.0022 0.033 0.0008 0.046 0.0023 3610 0.098 0.0006 0.012 0.0004 0.086 0.0008 3611 0.078 0.0018 0.041 0.0009 0.037 0.0020 3612 0.025 0.0005 0.120 0.0032 0.095 0.0032 3613 0.075 0.0019 0.056 0.0012 0.019 0.0022 The differences in (ai/A) and (bi/B) are calculated directly. To obtain the standard error of this difference in estimates, apply the following formula (again, from Section 4 and Census Bureau guidance) as follows: In this case, X and Y refer to the (ai/A) and (bi/B) estimates. The differences in (ai/A) and (bi/B) are then summed, divided by two, and multiplied by 100 to obtain the ID. The standard error of this summation can be calculated using an extension of the same formula with 13 addends. The final standard error of the calculation can then be mul- tiplied by 1.65 to obtain the 90 percent confidence level margin of error, so the ID in this case is 35.34 0.84. ( ^ +Y SE X ^ = SE X ) ^ 2 + SE Y ^ 2 ( ) ( ) Calculating a confidence interval on a measure like the ID becomes a useful exercise when the IDs for different geographies (e.g., one county compared to another), areal units (e.g., PUMA-level analysis versus tract-level analysis), or combinations of population groups (non- Hispanic white compared to non-Hispanic black versus non-Hispanic compared to Hispanic) are compared. 7.4 Conclusions from the Case Study This case study has demonstrated how to calculate the ID, which is one application of environ- mental justice analysis. The power of this measure is in its ability to be calculated for specific areal units, then imported into a geographic information system (GIS) for mapping and displaying the uniformity or diversity of a region. The ID plays an important role in estimating impacts within the environmental justice process and is also applicable to specific transportation projects. There are two important notes regarding weaknesses of this measure. The first issue involves the "aspatial" nature of this measure. Although the ID does represent a summary measure of spa- tial "evenness," it does so only in a very simplified, non-spatial way for a particular areal unit.