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Open AccessArticle

A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China

by Junnan Xiong 1,2, Kun Li 1,*, Weiming Cheng 2,3,4, Chongchong Ye 1 and Hao Zhang 1,5
1
School of Civil Engineering and Architecture, Southwest Petroleum University, Chengdu 610500, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
4
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
5
Institute of Mountain Disasters and Environment, CAS, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 495; https://doi.org/10.3390/ijgi8110495
Received: 2 September 2019 / Revised: 29 October 2019 / Accepted: 31 October 2019 / Published: 2 November 2019
Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution.
Keywords: population spatialization; spatial stationarity; geographically weighted regression; DMSP/OLS; land use population spatialization; spatial stationarity; geographically weighted regression; DMSP/OLS; land use
MDPI and ACS Style

Xiong, J.; Li, K.; Cheng, W.; Ye, C.; Zhang, H. A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China. ISPRS Int. J. Geo-Inf. 2019, 8, 495.

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