# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 3 Results and Discussions  
### Establishment of Population Estimation Model  
Subdistrict-scale building classification results are obtained using the _POI_ data, buildings data, and administrative territorial entity data in Wuhan. Residential houses are screened out from the classification results, and they are classified as low-rise, middle-rise, and high-rise. The following key characteristics of buildings in the subdistricts are obtained: (1) total floor area of buildings, (2) total residential area of low-rise buildings, (3) total living area of mid-rise buildings, (4) total living area of high-rise buildings, (5) number of low-rise buildings, (6) number of mid-rise buildings, and (7) number of high-rise buildings. With these seven characteristics as the prediction variables X and the actual population of the subdistrict as the response variable Y, the importance of each variable is calculated, as shown in Table 2. The first five variables are selected in order of the number of high-rise buildings, total area of buildings, total area of middle-rise buildings, number of middle-rise buildings, and total area of high-rise buildings.  
In total, 20 subdistricts of Wuhan are selected as samples, where 10 are selected randomly for the training set and the remaining samples are used as the test set. The training set data are filtered, and fields with the same variable name as the feature selected are used as the independent variables in the regression model. The correlations between multi-source data and population data are analyzed by constructing a multiple regression model. The dependent variable in the model is the resident population number. The independent variables in the model include the number of high-rise buildings, total area of buildings, total area of middle-rise buildings, number of middle-rise buildings, and total area of high-rise buildings. The model obtained in this study is as follows, where Pop denotes the projected population size.  
\[\begin{split} Pop&=296.1796356+0.013153313\text{-} HighbuildingArea-55.40373798\\ &\text{-}HighbuildingNum+0.099569726\text{-}BuildingAreacovered\\ &\text{-}0.043341564\text{-}MidbuildingArea+121.3754518\\ &\text{-}MidbuildingNum\end{split} \tag{6}\]  
Figure 6 shows a scatter plot of the census population versus the estimated population, where the line indicates the ideal fit. The estimated results are generally close to the actual population, thereby demonstrating that the established regression model fitted the training set well. Table 3 compares the error rates for the model fitted to the training set in detail. Most of the error rates of subdistrict populations estimated are less than 25% and the average error is 11.08%, thereby indicating that the model had high accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable Name** & **Importance** \\ \hline HighbuildingNum & 0.231080 \\ BuildingArecovered & 0.160503 \\ MidbuildingArea & 0.158064 \\ MidbuildingNum & 0.156951 \\ HighbuildingArea & 0.144724 \\ LowbuildingNum & 0.100244 \\ LowbuildingArea & 0.048434 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The order of importance for building-related feature variables.