id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-12005	Zhang, Xusong; Rodavia, Maria Rosario	Population Spatialization based on Random Forest Model and Multi-source Geospatial big data	2023	4	.pdf	application/pdf	2547	140	46	By performing a dimensionality reduction process on a large range of population data, we have obtained grid data with a more accurate and semantically rich description of population density in the smallest administrative region, this indicates that the progressive forest model has better population spatialization results compared to traditional linear regression models. The low spatiotemporal resolution of traditional population data makes it unsuitable for spatial analysis, which poses scientific difficulties in understanding the natural and social coupling mechanisms of sustainable development at multiple scales.	cache/fcis-12005.pdf	txt/fcis-12005.txt