The City of Portland has 477 census block groups. Data from the 2015 American Community Survey (ACS) is somewhat incomplete in terms of Median Household Income (MHI), so this analysis will use the 2014 ACS 5-year estimate data on MHI.
Canopy data is accessed via the 2014 Oregon Metro Regional Land Information System.
The data, as with the maps above, are classified into quintiles: 5 ordinal categories containing 20% of the MHI values:
First, a Tukey HSD test is used to assess the statistical significance of differences between groups:
MHI_Quintiles:
diff | lwr | upr | p adj | |
---|---|---|---|---|
2-1 | 0.007363 | -0.04516 | 0.05988 | 0.9909 |
3-1 | 0.04076 | -0.01176 | 0.09328 | 0.08353 |
4-1 | 0.062 | 0.009479 | 0.1145 | 0.001195 |
5-1 | 0.2059 | 0.1536 | 0.2583 | 2.543e-11 |
3-2 | 0.0334 | -0.01912 | 0.08592 | 0.2296 |
4-2 | 0.05463 | 0.002116 | 0.1072 | 0.006416 |
5-2 | 0.1986 | 0.1462 | 0.251 | 2.543e-11 |
4-3 | 0.02123 | -0.03128 | 0.07375 | 0.6767 |
5-3 | 0.1652 | 0.1128 | 0.2176 | 2.547e-11 |
5-4 | 0.1439 | 0.09157 | 0.1963 | 2.548e-11 |
The Tukey HSD analysis reveals the following:
Additionally, a regression model was fitted to the data:
Estimate | Std. Error | t value | Pr(>|t|) | |
---|---|---|---|---|
MHI_Quintiles2 | 0.007363 | 0.01604 | 0.4589 | 0.6465 |
MHI_Quintiles3 | 0.04076 | 0.01604 | 2.541 | 0.01138 |
MHI_Quintiles4 | 0.062 | 0.01604 | 3.864 | 0.000127 |
MHI_Quintiles5 | 0.2059 | 0.016 | 12.87 | 1.08e-32 |
(Intercept) | 0.1999 | 0.01134 | 17.62 | 9.788e-54 |
Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
---|---|---|---|
476 | 0.1106 | 0.3176 | 0.3118 |
This test is also revealing, and shows that as MHI increases relative to Q1, so does the canopy cover (with the exception of Q2). For example: households in the top quintile of income (Q5) can expect to have ~20.6% more canopy cover than Q1
Another way to look at this is through the raw MHI values:
Estimate | Std. Error | t value | Pr(>|t|) | |
---|---|---|---|---|
pdx@data$mhh | 2.658e-06 | 1.74e-07 | 15.28 | 3.807e-43 |
(Intercept) | 0.09927 | 0.01185 | 8.377 | 6.194e-16 |
Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
---|---|---|---|
476 | 0.1092 | 0.33 | 0.3286 |
This model shows that for every additional $10,000 in MHI, we can expect a ~3% increase in canopy cover.
The analysis is repeated for trees that fall only within the Right of Way (ROW; public street tree coverage).
MHI_Quintiles:
diff | lwr | upr | p adj | |
---|---|---|---|---|
2-1 | 0.03971 | -0.008914 | 0.08833 | 0.0595 |
3-1 | 0.04362 | -0.005 | 0.09225 | 0.02855 |
4-1 | 0.06781 | 0.01918 | 0.1164 | 6.24e-05 |
5-1 | 0.2152 | 0.1667 | 0.2637 | 2.814e-11 |
3-2 | 0.003914 | -0.04484 | 0.05266 | 0.9989 |
4-2 | 0.0281 | -0.02065 | 0.07685 | 0.3259 |
5-2 | 0.1755 | 0.1269 | 0.2241 | 2.814e-11 |
4-3 | 0.02419 | -0.02457 | 0.07294 | 0.4827 |
5-3 | 0.1716 | 0.1229 | 0.2202 | 2.815e-11 |
5-4 | 0.1474 | 0.09876 | 0.196 | 2.819e-11 |
Similar results are found, and the biggest takeaway is that group 5 has significantly higher ROW coverage.
Again, similar results are found for ROW coverage. Interestingly, there is a significant difference between each quintile group and ROW canopy cover.
Estimate | Std. Error | t value | Pr(>|t|) | |
---|---|---|---|---|
MHI_Quintiles2 | 0.03971 | 0.01485 | 2.673 | 0.007771 |
MHI_Quintiles3 | 0.04362 | 0.01485 | 2.937 | 0.003478 |
MHI_Quintiles4 | 0.06781 | 0.01485 | 4.565 | 6.379e-06 |
MHI_Quintiles5 | 0.2152 | 0.01481 | 14.53 | 8.836e-40 |
(Intercept) | 0.2315 | 0.01048 | 22.1 | 7.858e-75 |
Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
---|---|---|---|
477 | 0.1026 | 0.347 | 0.3415 |
Estimate | Std. Error | t value | Pr(>|t|) | |
---|---|---|---|---|
pdx@data$mhh | 2.78e-06 | 1.56e-07 | 17.82 | 8.814e-55 |
(Intercept) | 0.1335 | 0.01062 | 12.57 | 1.721e-31 |
Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
---|---|---|---|
477 | 0.09802 | 0.4007 | 0.3995 |
Mean canopy cover (aggregated in census block group):
## [1] 0.2633772
Std. Dev of canopy cover (aggregated in census block group):
## [1] 0.1332839