AUTHOR=Liu Lingling , Cheng Gang , Yang Jie , Cheng Yushu 

TITLE=Population spatialization in Zhengzhou city based on multi-source data and random forest model

JOURNAL=Frontiers in Earth Science

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2023.1092664

DOI=10.3389/feart.2023.1092664

ISSN=2296-6463

ABSTRACT=<p>Fine-scale population map plays an essential role in numerous fields, including resource allocation, urban planning, disaster prevention and response. Point of Interest (POI) data is widely used for population spatialization, but the types of POI are ignored. Since different types of POI data have different impacts on population distribution, this paper used typed POI data and other multi-source data to map population distributions at fine scales. At the township level, three random forest models were used to generate the population maps of 150 m, 300 m, and 500 m in 2020, enabling the downscaling of county-level population distribution to the grid level. The main influencing factors of population distribution were extracted and analyzed based on the feature importance output from the model. Zhengzhou city was used as a case for study. The experiments show the results of population spatialization for all three scales in this study have better fitting accuracy than that of the GPWv4 and LandScan datasets. The coefficient of determination (<inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>) is 0.8333 for 150 m gridded population, 0.8295 for 300 m, and 0.8224 for 500 m; POI types related to residence information have greater contributions to population spatialization than other features; typed POI data are more conducive to population spatialization.</p>