## 4 Discussion and Conclusions
### Conclusions  
This paper compares the use of twelve machine learning regression algorithms in gridded population mapping of Guangzhou city, China. Various spatial data (such as night light images, land use, POI, roads, and so on) from different sources are considered in the study. To evaluate the performance of the twelve regression algorithms, several metrics were involved in this study. The results indicate that the proposed method has great potential in fine-scale gridded population mapping. The conclusions can be summarized as follows. 1. The R\({}^{2}\) values of the twelve regression algorithms discussed in this paper varied between 0.193 and 0.758. It can be said that besides the OLS algorithm, all the algorithms produced acceptable decomposition results by taking the R\({}^{2}\) as the only evaluation metric. When all the algorithms were evaluated with the metric of MAPE, it was observed that the MAPE values of these models varied between 78.58% and 174.37%. That is, it can be concluded that all the decomposition results can be considered \"reasonable\", apart from those of the OLS algorithm. Both the regularization method and the subsetting method can effectively alleviate overfitting in the OLS model. For the spatial decomposition of demographic data, the regularization method is better than the subsetting method in alleviating overfitting. 2. According to the model evaluation results, it can be seen that nonlinear regression algorithms offer greater accuracy than linear regression algorithms. Among the three nonlinear regression algorithms discussed in this study, the RF algorithm and the KNN algorithm both produced better results than the SVR algorithm, especially the KNN algorithm. Therefore, the KNN algorithm was recognized as a more suitable algorithm for this study. However, the accuracy of the KNN algorithm in other areasstill needs to be evaluated. In addition, because the KNN algorithm does not provide a parameterized regression model, the interpretability of the decomposition model is very poor. Conceptualization, G.Z. and M.Y. ; methodology, G.Z. and M.Y. ; software, G.Z. and Z.L. ; validation, G.Z. and Z.L. ; formal analysis, G.Z. and Z.L. ; investigation, G.Z. and Z.L. ; resources, G.Z. and M.Y. ; data curation, G.Z. and Z.L. ; writing--original draft preparation, G.Z. and M.Y. ; writing--review and editing, G.Z. and M.Y. ; visualization, G.Z. and M.Y. ; supervision, M.Y. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Natural Science Foundation of Guangdong Province, China: Grant Number 2017A030313240; the Philosophy and Social Science Research Program of Guangzhou city, Guangdong Province, China: Grant Number 2020 GZGJ183; and the Guangzhou Science and Technology Plan Project--Joint Project funded by City and University: Grant Number 202102010413. Not applicable. The authors would like to thank the editors and the anonymous reviewers for their insightful suggestions and comments. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References  
References  
[PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data. _Remote Sens. Environ._**2013**, _130_, 219-232. [CrossRef]  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Determining Global Population Distribution: Methods, Applications and Data. In _Advances in Parasitology_; [PERSON], [PERSON], [PERSON]; Academic Press: London, UK, 2006; Volume 62, pp. 119-156. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Census-independent population mapping in northern Nigeria. _Remote Sens. Environ._**2018**, _204_, 786-798. [CrossRef]  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Global demographic trends and future carbon emissions. _Proc. Natl.