## 1 Introduction  
Information about fine-scale population distribution is essential in many areas, including urban planning and management [1], natural disaster response [2], infectious disease prevention and control [3], resource allocation, and environment protection [4]. Accurate population distribution data are fundamental for the achievement of urban sustainable development goals (SDGs) [5, 6]. The census method is the main way to collect population data in varying countries. However, the spatial resolution and update frequency of census data are too low to meet the requirements of modern urban governance. Due to the fact that demographic data is usually collected in subdistrict units, the spatial decomposition of demographic data into gridded population data can show population distribution patterns more accurately [7, 8, 9, 10, 11]. Therefore, fine-scale and accurate population information is essential for exploring the relationship between urban residents and the built environment [1]. Guangzhou, the capital city of Guangdong Province, is facing several challenges, such as a large population and huge pressure on its resources and environment. Therefore, fine-scale gridded population distribution information is particularly critical for improving the quality of urban governance. There have been many achievements in the field of spatial decomposition of demographic data. The spatial decomposition methods of demographic data are mainly divided into spatial interpolation [12; 13; 14; 15] and dasymetric mapping [14; 16; 17]. The gridded population data is usually produced by integrating several auxiliary data and using several interpolation methods, such as inverse distance weighted (IDW), kriging, et. al. Dasymetric mapping is a geographic information technique for disaggregating demographic data into more homogenous units by incorporating additional data [18]. The typical global gridded population datasets mainly include the Gridded Population of the World (GPW) [19], the Global Rural-Urban Mapping Project (GRUMP) [2], LandScan Global [20], Global Human Settlement Population Grid datasets (GHS-POP) [21], and Worldpop [22]. However, most of these datasets are designed for extensive coverage and free accessibility for the undeveloped regions in the world; their accuracy on a local scale, such as that of a city or subdistrict, still need to be analyzed [23; 24]. In fact, the spatial decomposition of demographic data from coarser units into finer unit is a regression process according to the weight layer. Several regression models have been proposed to produce decomposition results, such as ordinary least squares (OLS) regression, random forest (RF) regression [25; 26], and so on. In general, regression models are usually divided into linear and nonlinear models. The OLS model is a classic linear regression algorithm, but it easily causes overfitting problems. Subsetting and regularization are common methods used to correct this. The best subset regression (BSR) is a widely used method for the selection and estimation of the parameters in a linear model, dating back at least as far as [PERSON], [PERSON] and [PERSON] [27; 28; 29], that tries all possible combinations of variables and chooses one that minimizes certain criteria. Least angle regression (LARS) is a method of variable selection proposed by [PERSON] et al., in 2004, which is similar to the form of forward stepwise regression [30]. The commonly used regularization functions include L1 and L2 regularization. The regression methods that use L1 regularization and L2 regularization are called Lasso Regression [31; 32] and Ridge Regression [33; 34], respectively. Elastic Net is a linear regression model that uses both the L1 norm and the L2 norm of the coefficient vector [35; 36; 37]. It is especially suitable for occasions when multiple features are related to each other. As we know, the collinearity of the independent variables is also the main reason for the over-fitting problem of linear regression.