## 2 Materials and Methods  
### Data Sources and Preparation  
Population spatial distribution is influenced by a variety of cultural and natural factors. Therefore, this study employs multiple geospatial datasets for population spatialization modeling, including Luojia-1 (LJ1-01) nighttime light data, water area data, land use data, DEM data, and POI data. The model's estimation accuracy is verified using 2018 statistical data from street (town) administrative units, as well as the WorldPop dataset. The main data sources are shown in Table 1.  
POI data were obtained from the AutoNavi Map Open Platform in Shanghai using Python. To address the excessive variety in raw POI data and the limited representation of certain POI types, we conducted systematic data processing, including screening, removal, reclassification, and consolidation according to China's national standard GB/T 35648-2017 [32] for classification and coding of geographic information points of interest. This procedure resulted in 17 dominant POI categories, each constituting more than 0.5% of the total dataset, ultimately yielding over 800,000 pieces of information. These categoriesincluded catering services, scenic spots, public facilities, enterprises, shop services, traffic services, address information, financial services, transportation facilities, education and cultural services, life services, dwellings, access facilities, relaxation services, hospital services, government agencies, social organizations, and hostel services. Using the geographical coordinates from the POI data, the tabular data was converted into vector point files, yielding a total of 1,236,242 POI data points. Building upon this framework, we performed localized kernel density estimation (KDE) for each individual POI category as well as their aggregated totality using the Silverman kernel function with a 400 m search radius (equivalent to 4 grid cells). The analysis produced density raster maps (units: features\(/\)km\({}^{2}\)) for each POI type, which were then spatially joined to 100 m \(\times\) 100 m grid cells using GIS spatial linkage tools. This process ultimately generated an 18-dimensional feature set comprising both individual POI category densities and their combined total density values. The 2018 nighttime light data for Shanghai, captured by the LJ-01 satellite, were obtained through downloading, stitching, and cropping. The data had an image digital number (DN) value range from 0 to 1,087,800 and a spatial resolution of 130 m. To ensure consistency with other datasets, the nighttime light data were resampled to 100 m resolution, transformed from the original CGCS2000 coordinate system to WGS-84, and radiometrically calibrated using the official formula provided by the data source website. The DN values, radiant brightness values, and binary processing results for light brightness were extracted to derive the three-dimensional features of the nighttime light data, which were then incorporated into the 100 m geographic grid. \[L=DN^{\frac{3}{2}}\times 10^{-10} \tag{1}\]  
where \(L\) represents the radiance value after absolute radiometric correction, while \(DN\) denotes the grayscale value of LJ-01 nighttime light data. The DEM data were also referenced to the WGS 84 coordinate system, with a spatial resolution of 30 m, which was resampled to 100 m for consistency. Using Shanghai's administrative boundaries, the DEM image was stitched and cropped to extract the relevant elevation data. From this processed DEM, four dimensional features were derived: altitude, slope, aspect, and terrain undulation, and their corresponding values were then extracted and integrated into the 100 m geographic grid.