# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 3 Results and Discussions
### Evaluation of Model Estimation Results  
Data from 10 subdistricts, including Liangdao Subdistrict, Cuiwei Subdistrict, and Luonan Subdistrict, are used as independent test sets to evaluate the accuracy of the model at predicting the population sizes. The prediction error rates for the subdistrict populations are mostly less than 25% and the overall average is 12.33%. The accuracy of the proposed model is better than those of the current population estimation method based on DMSP/OLS right lighting data (error rate over 25% [36]) and the multi-agent urban fine-scale population estimation model (average relative error of 19% [37]). The results showed that the proposed model obtained good fits when predicting the subdistrict populations. It can be seen from Table 3 that the estimation error of most subdistricts is within 10,000 people, which leads to a large estimation error of subdistricts with fewer actual populations. The scatter plot in Figure 7 illustrates the estimated error rate for each subdistrict. Only two subdistricts had an error rate of more than 25%, and the rest of the subdistricts are pretty much correctly estimated. Comparatively speaking, there are more underestimates of the population in the whole experimental area, including two seriously underestimates and five slightly underestimates. This is mainly caused by the lack of building data in some areas. Figure 6: Residuals of the training set used in the population estimation model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Subdistrict Name** & **Actual Population** & **Predicted Population** & **Estimation Error** & **Error Rate** \\ \hline Liangdao Subdistrict & 64,704 & 67,818 & 3114 & 4.81\% \\ Cuiwei Subdistrict & 44,529 & 38,604 & 5925 & \(-\)13.31\% \\ Luonan Subdistrict & 230,583 & 225,938 & 4645 & \(-\)2.01\% \\ Shuita Subdistrict & 19,426 & 16,859 & 2567 & \(-\)13.21\% \\ Dazhi Subdistrict & 35,087 & 22,799 & 12,288 & \(-\)35.02\% \\ Erqi Subdistrict & 81,312 & 89,207 & 7894 & 9.71\% \\ Huanghelou & 60,909 & 62,573 & 1664 & 2.73\% \\ Subdistrict & 36,387 & 28,539 & 7848 & \(-\)21.57\% \\ Minquan Subdistrict & 32,234 & 30,054 & 2180 & \(-\)6.76\% \\ Ganghuacun & 77,222 & 39,195 & 38,027 & \(-\)49.24\% \\ Subdistrict & & & & \\ \hline \hline \end{tabular}  
Actual total population: 682,393; total error of prediction: 84,165; average error rate: 12.33%.  
\end{table} 
Table 3: Estimated error of the model. In order to confirm whether there is a lack of building data in subdistricts with large population estimation errors, we find the remote sensing data of Gaofen-1 satellite for comparison. Taking Ganghuacun Subdistrict as an example, the original data for the Ganghuacun Subdistrict are visualized in ArcMap. Figure 8 shows that the buildings in the upper and middle left part and the lower right part are clearly missing. The area of this subdistrict is less than 2.5 km\({}^{2}\), and the number of missing buildings is high, thereby greatly impacting the accuracy of the model. Furthermore, we analyze the relationship between the proportion of buildings with different building heights and the error rate. We count the proportion of low-rise, middle-rise, and high-rise buildings in these 10 streets in turn, and conduct correlation analysis with the estimation error. From Figure 9, we can find that the proportion of mid-rise buildings in the streets with an error rate of more than 25% is more than 50%. In China, mid-rise buildings often contain various types of _POI_, serving both commercial and residential functions. This brings interference to the classification of buildings. Figure 8: Buildings in Ganghuacun Subdistrict. Figure 7: The error of the test set of the population estimation model. To sum up, the method in this paper can experiment with fine-scale population estimations with an average error of 12.33%. The error rate of most subdistricts is small, and the estimation error of half subdistricts is less than 5000 people.