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Open AccessArticle

Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China

by Ge Qiu 1,2, Yuhai Bao 1, Xuchao Yang 3,4, Chen Wang 5,6, Tingting Ye 3, Alfred Stein 2 and Peng Jia 2,7,8,*
1
College of Geographic Science, Inner Mongolia Normal University, Huhhot 010022, China
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 Enschede, The Netherlands
3
Ocean College, Zhejiang University, Zhoushan 316021, China
4
Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA
5
Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China
6
State Environmental Protection Key Laboratory of Satellite Remote Sensing, Beijing 100094, China
7
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
8
International Initiative on Spatial Lifecourse Epidemiology (ISLE), Hong Kong, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1618; https://doi.org/10.3390/rs12101618
Received: 1 April 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density. View Full-Text
Keywords: population distribution; random forest; remote sensing; social sensing; point-of-interest; building footprint population distribution; random forest; remote sensing; social sensing; point-of-interest; building footprint
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Qiu, G.; Bao, Y.; Yang, X.; Wang, C.; Ye, T.; Stein, A.; Jia, P. Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China. Remote Sens. 2020, 12, 1618.

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