Hypothesis 1B The built environment will improve as the neighborhood

The second hypothesis tested states that the built environment will improve as the neighborhood disadvantage scale decreases at the county level. Much like the racially-based hypothesis above, it implies that income will vary significantly from county to county, and that the variation will be associated with the built environment outlets available to the residents who live there. To test this hypothesis, I conducted the same analyses as above, descriptive analyses, hot-spot analyses, 2SFCA method analyses, and linear and spatial regression analyses.

The first analyses were descriptive analyses of median household income and

neighborhood disadvantage index. The results show that there were distinct differences in both median household income and the neighborhood disadvantage index. For median household income, the higher income areas were essentially located in and around Atlanta, as well as the

159 coastal counties and the suburban areas of the other major cities in Georgia. The lowest income areas are in the swath of counties earlier designated as Georgia’s diabetes belt. These are the counties ranging from southwest Georgia through the central-eastern section. The neighborhood disadvantage index matches up nicely with the income map. As a reminder, the neighborhood disadvantage index measures the unemployment rate of a county, the education level, and income below $30,000 a year. The areas with the lowest neighborhood disadvantage are the areas around Atlanta and the coastal counties, in general. The areas of highest disadvantage follow the same swath as the lowest income counties. These results show support for the

hypothesis by showing that there are significant differences in median household income and in neighborhood disadvantage scores.

The second analyses build upon the first by adding built environment outlets to the income distribution maps. The results of these are even starker than those for race above. The built environment outlets are almost all in areas of higher income. The lowest income areas have hardly any built environment outlets at all. This is especially true for positive food outlets, healthcare facilities, and physical activity facilities, arguably the most important variables for good health. The clear majority of all built environment outlets are in the Atlanta area, which is also the highest income section of Georgia. These results help support the hypothesis.

The next analyses are the hot-spot analyses. These help show definitively that there are significant differences in income in areas throughout Georgia. The income hot-spots are in and around Atlanta and the Savannah areas. The neighborhood disadvantage cold spots (indicating low disadvantage) are in the same areas. The cold spots for income are in the same areas as the hot-spots for neighborhood disadvantage. The next part of these analyses was overlaying the

160 built environment outlets over the hot-spot analyses. The results of these show unequivocal evidence that built environment outlets are in areas of high income and low neighborhood disadvantage. The areas of lowest income and highest disadvantage have hardly any built environment outlets available to the residents of those counties. These results corroborate the previous data and support the hypothesis being tested.

The GB2SFCA method results are next. Interestingly, although built environment outlets are plentiful in higher income areas and lesser in lower income, there does not seem to be an association between accessibility to built environment outlets and income. The only exception is healthcare facilities, which are more accessible in higher income areas. This, so far, is the only analysis that does not fully support the hypothesis.

The next two analyses are the linear and spatial regression analyses. While the 2SFCA method analyses were inconclusive, the linear regression analyses were perfectly clear in terms of neighborhood disadvantage. The neighborhood disadvantage variable was significantly

associated with all the built environment outlets, except for the positive food environment. These results show that for every increase in the neighborhood disadvantage scale, there was a

significant decrease in the number of negative food environment outlets, healthcare facilities, physical activity facilities, and public administration facilities available to the residents in the area. The spatial regression analysis adds the spatial weight to the model. Increases in

neighborhood disadvantage scale are associated with decreases in the built environment outlets that are available in these areas. These results support the hypothesis.

Overall, I must conclude that the hypothesis that the built environment will improve as the neighborhood disadvantage index decreases at the county level. Although accessibility did

161 not appear to be particularly associated with income at the county level, the other results showed that there were significant differences in income between counties. Further, these differences were associated with differences in availability of each built environment outlet. Therefore, I must reject the null hypothesis and conclude that there are significant differences in the

neighborhood disadvantage scale which lead to significant differences in the built environment.

8.1.1.3 Hypothesis 1C - The built environment will be better in urban areas and