## 2 Study Area and Data
### Data Preparation  
Scholars have made many attempts and explorations to enrich data sources and optimize data processing on population spatialization. The data sources mainly include land use and land cover data [18], terrain data [43], vegetation index data [44], nighttime light data [45], social perception big data such as point of interest (POI) [46], as well as building height data [47]. The data sources show a trend of diversification and refinement. The population spatialization-related data collected for this study primarily include the population census data of Sichuan Province in 2020, basic geographic information data, natural environmental data, building height data, nighttime light (NTL) data, and the social perception data. The details and sources of the data used in this study are presented in Table 1.  
The demographic data is the seventh national census data in 2020, which mainly includes the population data of 183 county-level administrative divisions and the population data for a partial township-level administrative division in Sichuan Province, totaling \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Name** & **Time** & **Spatial Scale** & **Format** & **Source** \\ \hline \hline \multirow{3}{*}{(a) Demographic data} & \multirow{3}{*}{Census data} & \multirow{3}{*}{2020} & county-level and township-level & \multirow{3}{*}{Table (csv)} & http://www. \\  & & & & & citypopulation.de/ \\  & & & & & (accessed on 21 February \\  & & & & & 2022) \\ \hline \hline \multirow{6}{*}{(b) Basic geographic information data} & \multirow{3}{*}{Road network} & \multirow{3}{*}{2021 Version} & \multirow{3}{*}{1:100 w} & \multirow{3}{*}{Vector (Polygon)} & https: \\  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 19 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & & (accessed on 23 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & & (accessed on 23 October \\  & & & & & & 2022) \\ \cline{1-1}  & & & & & & (accessed on 23 October \\  & & & & & & 2022) \\ \cline{1-1}  & & & & & & (accessed on 23 October \\  & & & & & & 2022) \\ \hline \hline \multirow{6}{*}{(c) Natural environmental data} & \multirow{3}{*}{Global 30} & \multirow{3}{*}{2020} & \multirow{3}{*}{30 m} & \multirow{3}{*}{Raster} & https: \\  & & & & & & (accessed on 7 April 2022) \\ \cline{1-1}  & & & & & & [[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)) \\ \cline{1-1}  & & & & & (accessed on 20 October \\  & & & & & 2022) \\ \hline \hline \multirow{6}{*}{(d) Building height data} & \multirow{3}{*}{CNBH-10m} & \multirow{3}{*}{2020} & \multirow{3}{*}{10 m} & \multirow{3}{*}{Raster} & https: \\  & & & & & (accessed on 6 June 2023) \\ \hline \hline \multirow{6}{*}{(e) Nighttime light (NTL) data} & \multirow{3}{*}{NPP-VIIRS v2.1} & \multirow{3}{*}{2020} & \multirow{3}{*}{500 m} & \multirow{3}{*}{Raster} & https: \\  & & & & & (accessed on 23 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & (accessed on 23 October \\  & & & & & 2022) \\ \cline{1-1}  & & & & & & (accessed on 23 October \\  & & & & & 2022) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of data used in this study. 2122 units. This data is primarily obtained from the official announcements released by local governments regarding the Seventh National Population Census. Census agencies ensure the representativeness of the data through sampling and statistical methods, ensuring that the data accurately reflects the characteristics and distribution of the entire population.