Population spatialization based on geographically weighted regression model considering spatial stability of parameters

doi:10.6046/zrzyyg.2020297

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Abstract

The theories on population spatialization tend to be mature in recent years. However, the spatial stability of the variables and parameters used in population spatialization modeling has been scarcely focused on. With the land use data, night-time light data, and demographic data as the data sources, this study proposed a novel precise population spatialization method based on a semi-parametric geographically weighted regression model (S-GWR). Then a permanent population spatialization model on a county scale was built using the method proposed in this study and then was verified using the Sichuan Province as the study area. In this study, the spatial stability of parameters and variables were obtained using the S-GWR model while the characteristics of the variables were analyzed, in order to improve the accuracy of population estimation. Finally, the population spatial distribution map (SDP) with a resolution of 1 km of Sichuan Province in 2010 was formed. The results show that the coefficient of determination coefficient of the S-GWR model was 0.903, which is higher than that of traditional regression models and indicates better fitting effects. The S-GWR model was verified using two commonly used population datasets, and the verification results are as follows. At a county level, the overall average error of the study area and the relative error of each district and county in the SDP all approximated to 0, and thus the SDP was more precise than the other two datasets. At a township level, the mean relative error, mean absolute error, and root mean square error of SDP were 34.54%, 5 715.703, and 12 085.932, respectively, which were all lower than those of the other two datasets. Meanwhile, the SDP showed more favorable dispersion effects than the other datasets. Furthermore, the number of the towns whose population was accurately estimated was 185 in the SDP, which was higher than that in the other two datasets. Therefore, the accuracy of population spatialization can be improved by considering the spatial stability of parameters.

Keywords semi-parametric geographically weighted regression spatial stability night-time light data land use population spatialization

ZTFLH:

TP79

Corresponding Authors: LIAN Hong E-mail: 345083896@qq.com;1845705004@qq.com

Issue Date: 24 September 2021

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	Dongsheng XIAO
	Hong LIAN

Cite this article:

Dongsheng XIAO,Hong LIAN. Population spatialization based on geographically weighted regression model considering spatial stability of parameters[J]. Remote Sensing for Natural Resources, 2021, 33(3): 164-172.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020297 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/164

Fig.1 Night light data map and land use distribution map of Sichuan Province

Tab.1 Data type and source

Fig.2 Flow chart of population

Tab.2 The correlation coefficient between land use types and population data

Tab.3 Parameter estimation and parameter stationarity test of geographically weighted model

Tab.4 Evaluation of goodness of fit of three models

Fig.3 Spatial distribution of population in 2010

Fig.4 Relative error box chart of three kinds of datasets

Tab.5 Accuracy comparison of three datasets

Tab.6 Statistical table of relative error classification in 500 villages and towns(个)

Fig.5 Relative error ratio of villages and towns

[1]	谭敏, 刘凯, 柳林, 等. 基于随机森林模型的珠江三角洲30 m格网人口空间化[J]. 地理科学进展, 2017, 36(10):1304-1312. doi: 10.18306/dlkxjz.2017.10.012
[1]	Tan M, Liu K, Liu L, et al. Spatialization of 30 m grid population in Pearl River Delta based on stochastic forest model[J]. Progress in Geography, 2017, 36(10):1304-1312.
[2]	柏中强, 王卷乐, 杨飞. 人口数据空间化研究综述[J]. 地理科学进展, 2013, 32(11):1692-1702. doi: 10.11820/dlkxjz.2013.11.012
[2]	Bai Z Q, Wang J L, Yang F. Research progress in spatialization of populationdata[J]. Progress in Geography, 2013, 32(11):1692-1702.
[3]	肖东升, 杨松. 基于夜间灯光数据的人口空间分布研究综述[J]. 国土资源遥感, 2019, 31(3):10-19.doi: 10.6046/gtzyyg.2019.03.02. doi: 10.6046/gtzyyg.2019.03.02
[3]	Xiao D S, Yang S. A survey of population spatial distribution based on night light data[J]. Remote Sensing for Land and Resources, 2019, 31(3):10-19.doi: 10.6046/gtzyyg.2019.03.02. doi: 10.6046/gtzyyg.2019.03.02
[4]	Elvidge C D, Baugh K E, Dietz J B, et al. Radiance calibration of DMSP-OLS low-light imaging data of human settlements[J]. Remote Sensing Enviroment, 1999, 68,77-88. doi: 10.1016/S0034-4257(98)00098-4 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425798000984
[5]	Zhang Q L, Seto K C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J]. Remote Sensing of Environment, 2011, 115(9):2320-2329. doi: 10.1016/j.rse.2011.04.032 url: https://linkinghub.elsevier.com/retrieve/pii/S003442571100160X
[6]	陈晴, 侯西勇, 吴莉. 基于土地利用数据和夜间灯光数据的人口空间化模型对比分析——以黄河三角洲高效生态经济区为例[J]. 人文地理, 2014, 29(5):94-100.
[6]	Chen Q, Hou X Y, Wu L. Comparison of population spatialization models based on land use data and DMSP/OLS data respectively:A case study in the efficient ecological economic zone of the Yellow River Delta[J]. Human Geography, 2014, 29(5):94-100.
[7]	杨续超, 高大伟, 丁明军, 等. 基于多源遥感数据及DEM的人口统计数据空间化——以浙江省为例[J]. 长江流域资源与环境, 2013, 22(6):729-734.
[7]	Yang X C, Gao D W, Ding M J, et al. Spatialization of population statistics based on multi-source remote sensing data and DEM:A case study of Zhejiang Province[J]. Resources and Environment in the Yangtze Basin, 2013, 22(6):729-734.
[8]	赵利利, 孟芬, 马才学. 基于多源遥感数据的武汉市人口空间分布格局演化[J]. 地域研究与开发, 2016, 35(3):165-169.
[8]	Zhao L L, Meng F, Ma C X. Spatial distribution pattern evolution of Wuhan population based on multi-source remote sensing data[J]. Areal Research and Development, 2016, 35(3):165-169.
[9]	胡云锋, 赵冠华, 张千力. 基于夜间灯光与LUC数据的川渝地区人口空间化研究[J]. 地球信息科学学报, 2018, 20(1):68-78. doi: 10.12082/dqxxkx.2018.170224
[9]	Hu Y F, Zhao G H, Zhang Q L. Population spatialization in Sichuan and Chongqing based on night lighting and LUC data[J]. Journal of Geo-Information Science, 2018, 20(1):68-78.
[10]	黄杰, 闫庆武, 刘永伟. 基于DMSP/OLS与土地利用的江苏省人口数据空间化研究[J]. 长江流域资源与环境, 2015, 24(5):735-741.
[10]	Huang J, Yan Q W, Liu Y W. Spatial analysis of population data in Jiangsu Province based on DMSP/OLS and land use[J]. Resources and Environment in the Yangtze Basin, 2015, 24(5):735-741.
[11]	丁文秀, 赵伟, 左德霖, 等. 基于土地利用分类模型和重力模型耦合的人口分布模拟——以武汉市人口数据为例[J]. 大地测量与地球动力学, 2011, 31(s1):127-131.
[11]	Ding W X, Zhao W, Zuo D L, et al. Population distribution simulation based on coupling of land use classification model and gravity model:A case study of Wuhan population data[J]. Geodesy and Geodynamics, 2011, 31(s1):127-131.
[12]	Fotheringham A S, Brunsdon C. Local forms of spatial analysis[J]. Geographical Analysis, 2010, 31,340-358. doi: 10.1111/gean.1999.31.issue-4 url: http://doi.wiley.com/10.1111/gean.1999.31.issue-4
[13]	王珂靖, 蔡红艳, 杨小唤. 多元统计回归及地理加权回归方法在多尺度人口空间化研究中的应用[J]. 地理科学进展, 2016, 35(12):1494-1505. doi: 10.18306/dlkxjz.2016.12.006
[13]	Wang K J, Cai H Y, Yang X H. Application of multivariate statistical regression and geographically weighted regression in the study of multi-scale population spatialization[J]. Progress in Geography, 2016, 35(12):1494-1505.
[14]	张建辰, 王艳慧. 基于土地利用类型的村级人口空间分布模拟——以湖北鹤峰县为例[J]. 地球信息科学学报, 2014, 16(3):435-442. doi: 10.3724/SP.J.1047.2014.00435
[14]	Zhang J C, Wang Y H. Simulation of rural population spatial distribution based on land use classification:A case study of Hefeng County,Hubei Province[J]. Journal of Geo-Information Science, 2014, 16(3):435-442.
[15]	陈晴, 侯西勇. 集成土地利用数据和夜间灯光数据优化人口空间化模型[J]. 地球信息科学学报, 2015, 17(11):1370-1377. doi: 10.3724/SP.J.1047.2015.01370
[15]	Chen Q, Hou X Y. Integrating land use data and night light data to optimize population spatialization model[J]. Journal of Geo-Information Science, 2015, 17(11):1370-1377.
[16]	王明明, 王卷乐. 基于夜间灯光与土地利用数据的山东省乡镇级人口数据空间化[J]. 地球信息科学学报, 2019, 21(5):699-709. doi: 10.12082/dqxxkx.2019.180497
[16]	Wang M M, Wang J L. Spatialization of township population data in Shandong Province based on night lighting and land use data[J]. Journal of Geo-Information Science, 2019, 21(5):699-709.
[17]	Dong N, Yang X H, Cai H Y. Research progress and perspective on the spatialization of population data[J]. Journal of Geo-Information Science, 2016, 18:1295-1304.
[18]	四川统计局. 四川统计年鉴[M]. 北京: 中国统计出版社, 2010.
[18]	Statistical Bureau of Sichuan Province. Sichuan statistical yearbook[M]. Beijing: China Statistics Press, 2010.
[19]	杨继瑞, 李月起, 汪锐. 川渝地区: “一带一路”和长江经济带的战略支点[J]. 经济体制改革, 2015(4):58-64.
[19]	Yang J R, Li Y Q, Wang R. Sichuan and Chongqing region:Strategic fulcrum of the Belt and Road initiatives and Yangtze River economic zone[J]. Reform of Economic System, 2015(4):58-64.
[20]	刘纪远, 宁佳, 匡文慧, 等. 2010—2015年中国土地利用变化的时空格局与新特征[J]. 地理学报, 2018, 73(5):789-802. doi: 10.11821/dlxb201805001
[20]	Liu J Y, Ning J, Kuang W H, et al. Spatial and temporal patterns and new characteristics of land use change in China from 2010 to 2015[J]. Acta Geographica Sinica, 2018, 73(5):789-802.

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