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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 153-161     DOI: 10.6046/gtzyyg.2020193
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Research on the spatialization of population on the Belt and Road based on optimization of multi-divisional modeling indexes
XU Tianyu(), ZHAO Xuesheng(), CHEN Fangxin, YANG Yi
College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China
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Abstract  

At present, the commonly used partition modeling of population can reflect the spatial differences and dynamic changes of population distribution. Nevertheless, due to the limitations of methods and data, the population distribution indicators in multi-partition also need to be specifically optimized according to regional characteristics to improve the accuracy of population spatialization. Based on the geographical characteristics of the developing countries along the “Belt and Road”, the authors proposed four geographic partition of high-light plain area, high-light hilly area, low-light plain area and low-light hilly area, and optimized the modeling index of multi-divisional partition through the adjustment of population distribution indicators, fusion of functional area population index and some other means. Finally, Tajikistan was used as the study area to draw a 30 m population distribution map (TJK_POP), and TJK_POP was compared with modeling results of using a single index for each district (NTL_POP and HSI_POP) for verification. The results show that the mean relative error (MRE) of TJK_POP is 22.57%, of which the MRE of the four partition are 28.01%, 19.33%, 17.99%, and 24.97%, respectively. The accuracy is better than that of NTL_POP and HSI_POP. At the same time, TJK_POP reduces the interference of the flowing population of commercial land such as airports and factories on the actual population distribution. The optimization of population distribution indicators for multi-divisional partition in this paper also provides a reference for the study of population spatialization in other similar areas along the “Belt and Road”.
Keywords Belt and Road      multiple partition      index optimization      functional zone population index      nighttime light      human settlement index     
ZTFLH:  TP79  
Corresponding Authors: ZHAO Xuesheng     E-mail: xutianyu03@163.com;zxs@cumtb.edu.cn
Issue Date: 21 July 2021
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Tianyu XU
Xuesheng ZHAO
Fangxin CHEN
Yi YANG
Cite this article:   
Tianyu XU,Xuesheng ZHAO,Fangxin CHEN, et al. Research on the spatialization of population on the Belt and Road based on optimization of multi-divisional modeling indexes[J]. Remote Sensing for Land & Resources, 2021, 33(2): 153-161.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020193     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/153
Fig.1  Modeling process
Fig.1  Modeling process
Fig.2  Administrative divisions of the Republic of Tajikistan
Fig.2  Administrative divisions of the Republic of Tajikistan
数据 数据来源 数据年份 分辨率/
比例尺
人口统计数据 《2016年塔吉克斯坦统计年鉴》 2015年 —
行政区划 GADM(https://www.gadm.org/) 2015年 1∶10万
地表覆盖数据 国家地理信息局(http://www.ngcc.cn/ngcc/) 2015年 10 m
夜间灯光数据 NGDC(http://ngdc.noaa.gov) 2015年 500 m
坡度数据 地理空间数据云(http://www.gscloud.cn/) 2015年 30 m
EVI数据 美国地质勘查局(http://glovis.usgs.gov/) 2015年 250 m
Tab.1  List of data sources
数据 数据来源 数据年份 分辨率/
比例尺
人口统计数据 《2016年塔吉克斯坦统计年鉴》 2015年 —
行政区划 GADM(https://www.gadm.org/) 2015年 1∶10万
地表覆盖数据 国家地理信息局(http://www.ngcc.cn/ngcc/) 2015年 10 m
夜间灯光数据 NGDC(http://ngdc.noaa.gov) 2015年 500 m
坡度数据 地理空间数据云(http://www.gscloud.cn/) 2015年 30 m
EVI数据 美国地质勘查局(http://glovis.usgs.gov/) 2015年 250 m
Tab.1  List of data sources
功能区 居住区 工业区 交通 其他
人口 0.785 0.157 0.253 0.135
Tab.2  Functional zone population index
功能区 居住区 工业区 交通 其他
人口 0.785 0.157 0.253 0.135
Tab.2  Functional zone population index
分区 行政单
元数/
个 		建模结果 R2
高光平
原区 		A1 9 poplp_A1=1.376 ∑ j = 1 4 NTLj·λj 0.736
高光丘
陵区 		A2 7 poplh_A2=19.482 ∑ j = 1 4 SNTLIj·λj 0.772
弱光平
原区 		A3 14 popnp_A3=9.191 ∑ j = 1 4 HSIj·λj 0.911
A4 9 popnp_A4=6.016 ∑ j = 1 4 HSIj·λj 0.878
弱光丘
陵区 		A5 6 popnh_A5=6.853 ∑ j = 1 4 SAHSIj·λj 0.956
A6 14 popnh_A6=15.768 ∑ j = 1 4 SAHSIj·λj 0.960
Tab.3  Modeling results
分区 行政单
元数/
个 		建模结果 R2
高光平
原区 		A1 9 poplp_A1=1.376 ∑ j = 1 4 NTLj·λj 0.736
高光丘
陵区 		A2 7 poplh_A2=19.482 ∑ j = 1 4 SNTLIj·λj 0.772
弱光平
原区 		A3 14 popnp_A3=9.191 ∑ j = 1 4 HSIj·λj 0.911
A4 9 popnp_A4=6.016 ∑ j = 1 4 HSIj·λj 0.878
弱光丘
陵区 		A5 6 popnh_A5=6.853 ∑ j = 1 4 SAHSIj·λj 0.956
A6 14 popnh_A6=15.768 ∑ j = 1 4 SAHSIj·λj 0.960
Tab.3  Modeling results
Fig.3  Tajikistan 30 m population distribution map(TJK_POP)
Fig.3  Tajikistan 30 m population distribution map(TJK_POP)
Fig.4  Regional population distribution map
Fig.4  Regional population distribution map
Fig.5  Absolute value of relative error distribution
Fig.5  Absolute value of relative error distribution
Fig.6  The comparison chart of the absolute value of relative errors of the four types of partitions
Fig.6  The comparison chart of the absolute value of relative errors of the four types of partitions
Fig.7  Comparison chart of functional area index optimization
Fig.7  Comparison chart of functional area index optimization
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