## 3 Results
### Spatial Decomposition Results Based on Different Regression Models
#### 3.2.1 Model Training Results for Population Density Regression on Subdistrict Scale
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Regression Coefficients**} \\ \hline \multirow{5}{*}{OLS} & Intercept: 1901.1, X18: \(-\)2701.682, X19: 3361.864, X14: \(-\)18,485.94, X16: \(-\)2380.934, X10: \(-\)64,862.639, X2: 41,694.214, X11: \(-\)4371.515, X13: \\  & \(-\)1373.629, X6: \(-\)11,883.214, X5: \(-\)5818.896, X7: 350.947, X3: 62,982.759, X9: 38,206.661, X8: 1205.293, X4: \(-\)5115.524, X1: 4267.389, X12: 8741.203, X17: \\  & 23,840.905, X20: 326.614 \\ \hline \multirow{5}{*}{BSR} & Intercept: \(-\)3403.922, X18: 5337.635, X19: \(-\)93,394.656, X14: 53,891.134, X16: \\  & 7504.77, X10: \(-\)25,863.081, X2: 86,852.708, X11: 20,344.519, X13: 21,736.836 \\ \hline \multirow{5}{*}{LARS} & Intercept: \(-\)2900.887, X18: 0.0, X19: 0.0, X14: 0.0, X16: 0.0, X10: 0.0, X2: \\  & 30,624.587, X11: 0.0, X13: 0.0, X6: 0.0, X5: 0.0, X7: 0.0, X3: 7234.966, X9: 0.0, X8: 0.0, X4: 0.0, X1: 42,087.766, X12: 0.0, X17: 8925.469, X20: 0.0 \\ \hline \multirow{5}{*}{RANSAC} & Intercept: 3153.065, X18: \(-\)2329.204, X19: \(-\)2244.842, X14: \(-\)15,134.735, \\  & \(-\)80,564. X10: \(-\)684.416, X26: 52: 630.3948, X11: \(\-\)8446.039, X13: \\  & \(-\)32,618.755, X6: \(-\)1115.962, X5: \(-\)70,222.187, X1347.96, X314.151.086, \\  & X9: 67,667.638, X8: 32,527.374, X4: 3245.049, X1: \(-\)9210.031, X12: 39,307.589, \\  & X17: 4264.226, X20: 656.059 \\ \hline \multirow{5}{*}{PCR} & Intercept: 21,141.643, PCA\_comp\_1: 22.4538, 862, PCA\_comp\_2 13,210.932, \\  & PCA\_comp\_3: 34,922.859, PCA\_comp\_4: 4447.51, PCA\_comp\_5: \\  & \(-\)18,119.79, PCA\_comp\_6: 21368.82 \\ \hline \multirow{5}{*}{PLS} & Intercept: 21,141.643, X18: 2373.471, X19: \(-\)2654.313, X14: \(-\)9015.102, X16: \\  & \(-\)448.029, X10: \(-\)846.882, X2: 32,589.97, X11: \(-\)12,449.821, X13: \(-\)2399.885, \\  & X6: 3084.427, X5: \(-\)7366.366, X7: 10,634.243, X32: 21,308.299, X9: 48,67.91, X8: 6529.518, X4: 16,855.52, X12: 22,41.48, X12: \(-\)5143.745, X17: 18,142.841, \\  & X20: \(-\)7780.463 \\ \hline \multirow{5}{*}{Lasso} & Intercept: 1964.62, X19: \(-\)1589.42, X14: 192.919, X16: \(-\)17,277.211, X10: 0.0, \\  & X2: \(-\)422,749.926, X11: 39,859.867, X13: \(-\)6965.692, X6: 0.0, X5: 0.0, X7: \\  & \(-\)3148.53, X3: 0.0, X9: 53,982.023, X8: 18,387.694, X4: 0.0, X1: \(-\)1408.461, \\  & X12: 10.624.522, X17: 551.24 \\ \hline \multirow{5}{*}{Ridge} & Intercept: \(-\)564.43, X18: \(-\)463.238, X19: 6.035, X14: \(-\)11,254.556, X16: \\  & 294.123, X10: \(-\)12,849.21, X2: 27,474.343, X11: \(-\)10,941.451, X13: \(-\)971.068, \\  & X6: 3010.897, X5: \(-\)520.741, X7: 8376.518, X3: 18,889.473, X9: 5893.067, X8: \\  & 9164.593, X4: 657.182, X1: 29,686.551, X12: 117.75, X17: 18,537.403, \\  & X20: \(-\)3996.295 \\ \hline \multirow{5}{*}{Elastic Net} & Intercept: \(-\)2646.624, X18: 75.337, X19: \(-\)523.813, X14: \(-\)6518.326, X16: \\  & 1903.378, X10: \(-\)3420.06, X2: 18,940.878, X11: \(-\)6626.514, X13: 1211.34, X6: \\  & 3634.379, X5: \(-\)2755.817, X7: 835.229, X3: 13,091.079, X9: 557.228, X8: \\  & 7356.989, X4: 11,183.943, X1: 23,092.917, X12: 655.476, X17: 14,025.249, \\  & X20: \(-\)2551.125 \\ \hline SVR & Non-parametric \\ \hline KNN & Non-parametric \\ \hline RF & Non-parametric \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fitted models for population density regression in the subdistrict scale. simplest model, with six independent variables. However, the interpretability of the PCR model is too weak.