Optimization of Modelling Population Density Estimation Based on Impervious Surfaces

Jinyu Zang, Ting Zhang, Longqian Chen, Long Li, Weiqiang Liu, Lina Yuan, Yu Zhang, Ruiyang Liu, Zhiqiang Wang, Ziqi Yu and Jia Wang
Additional contact information
Jinyu Zang: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Ting Zhang: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Longqian Chen: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Long Li: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Weiqiang Liu: School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Lina Yuan: Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yu Zhang: Department of Land Resource Management, School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China
Ruiyang Liu: School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Zhiqiang Wang: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Ziqi Yu: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
Jia Wang: School of Public Policy and Management, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China

Land, 2021, vol. 10, issue 8, 1-17

Abstract: Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R 2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

Keywords: population estimation; impervious surface; stepwise regression; remote sensing; Hefei (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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