Table 1.

Goodness-of-fit metrics.

Estimate	Prediction	Bias	Imprecision	Inaccuracy	R2	95% CI
Population totals	In-sample	13.81 (–0.02)	165.50 (0.41)	100.17 (0.27)	0.81	92.49%
Population totals	Out-of-sample	13.43 (–0.03)	173.28 (0.44)	105.61 (0.29)	0.79	90.50%
Population densities	In-sample	–13.96 (–0.02)	441.66 (0.41)	266.32 (0.27)	0.52	92.04%
Population densities	Out-of-sample	–15.20 (–0.03)	464.69 (0.44)	282.90 (0.29)	0.47	90.06%
Age and sex proportions	In-sample	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	1.00	100.00%
Age and sex proportions	Out-of-sample	0.00 (0.00)	0.00 (0.00)	0.00 (0.00)	1.00	100.00%

Analysis of residuals for the estimated population totals (people), population densities (people/building footprint ha), and province-level age and sex proportions for in-sample and out-of-sample posterior predictions. Bias represents the mean of the residuals, imprecision the standard deviation of residuals, inaccuracy the mean of absolute residuals, R² the squared Pearson correlation coefficient among the residuals, and the percentage of observations falling within the 95% credible intervals. Values in parentheses are computed using scaled residuals (residuals/predictions). Out-of-sample predictions are carried out using 10-fold cross-validation, where the model is fit ten times, each time withholding a random 10% of microcensus clusters until all is held out once.