## 3 Results  
Table 1 presents the descriptive statistics of different measures used in the analysis. There are 294 different SA2s in this analysis. The mean IRSAD score is 1028, ranging from 819 to 1160 in different SA2s. Within the Melbourne Metropolitan area, different SA2s have a UGS per capita ranging from 0.4 m\({}^{2}\) to 29,036 m\({}^{2}\) per person with a mean and median of 427 and 55.14 square meters per person, respectively. On average, 4.43% of the total population suffers from CVD. The test results suggest associations between UGS accessibility and density with the SES of areas; however, no association between the UGS per capita of areas and their SES is detected, as demonstrated in Table 2, and Figure 6 shows its spatial disparities. \begin{table}
\begin{tabular}{l c c c c} \hline
**Variable** & **N** & **Mean** & **Min** & **Max** \\ \hline UGS: & & & & \\ Area (Ha) & 294 & 3166 & 148.87 & 86,417.26 \\ Per capita (m\({}^{2}\)) & 294 & 427.28 & 0.40 & 29,036.15 \\ Diseases (\% of Total Pop) & 294 & 4.43 & 1.40 & 7.80 \\ IRSAD Score & 294 & 1028.62 & 819.00 & 1160.00 \\ \hline \end{tabular}
\end{table} 
Table 1: Descriptive statistics of measures by statistical area 2 (SA2). \begin{table}
\begin{tabular}{l c c c} \hline
**Variable** & **N** & **DF** & **X\({}^{2}\)** & _p_**-Value * \\ \hline UGS: & & & \\ Access & 294 & 4 & 17.302 & 0.002 \\ Density & 294 & 4 & 11.105 & 0.025 \\ Per capita & 294 & 4 & 5.210 & 0.266 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of Chi-square test. As Figures 7 and 8 suggest, the majority (59% and 61%, respectively) of quintiles 4 and 5 SA2s have high access to green space, whereas, in the other 3 lower quintiles, the majority of SA2s have low access to green space. We see the same pattern for green space density, wherein in the bottom 3 IRSAD quintiles, the majority of SA2s have low UGS density. Next, a series of one-way ANOVA showed a statistically significant change in the percentage of people having CVD in areas of different UGS access and different density separately F (1, 292) = 3.983, \(p\) = 0.047 and F (1, 292) = 7.462, \(p\) = 0.007. Areas with lower UGS accessibility have a higher proportion of CVD among residents. The same trend was observed with the UGS density indicator as well, represented in Tables 3 and 4. Figure 8: UGS density in different quintiles. Figure 7: UGS access in different quintiles. As discussed previously, different SA2s are grouped based on their UGS accessibility and density, named UGS availability in this paper. A set of Welch ANOVA tests revealed that there is a statistically significant difference in mean IRSAD scores and the proportion of people having heart conditions between these groups (F (3, 111.465) = 9.231, \(p\) < 0.001 and F (3, 126.767) = 5.135, \(p\) = 0.002). Table 5 shows the mean value of IRSAD in these different UGS availabilities. Areas with high access--high density of UGS have the highest SES among other areas. Also, Table 6 shows the average CVD rate in these groups, suggesting that areas with high access--high density of UGS have fewer people with CVD. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**UGS Availability** & **N** & **Disease Mean** & **Std. Dev.** & **Min** & **Max** \\ \hline High Access—High Density & 104 & 4.13 & 0.105 & 1.40 & 6.90 \\ High Access—Low Density & 43 & 4.71 & 0.117 & 2.80 & 6.60 \\ Low Access—High Density & 43 & 4.56 & 0.148 & 2.60 & 7.00 \\ Low Access—Low Density & 104 & 4.43 & 0.122 & 2.20 & 7.80 \\ \hline \hline \end{tabular}  
* Games–Howell Test for multiple comparisons found statistically significant differences between High Access—High Density Areas and High Access—Low Density Areas, \(p\) = 0.043.  
\end{table} 
Table 6: Comparing proportion of CVD in different UGS availability *.