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”.
徐甜雨, 赵学胜, 陈芳馨, 杨艺. 基于多元分区建模指标优化的“一带一路”人口空间化研究[J]. 国土资源遥感, 2021, 33(2): 153-161.
XU Tianyu, ZHAO Xuesheng, CHEN Fangxin, YANG Yi. Research on the spatialization of population on the Belt and Road based on optimization of multi-divisional modeling indexes. Remote Sensing for Land & Resources, 2021, 33(2): 153-161.
Zhao L X, Wu D. A study on the ecological pressure of population:Concurrent analysis on the regional differences of the relationship between population and environment in China[J]. Population Journal, 2015, 37(6):25-36.
[2]
王培安. 人口新形势与人口研究[J]. 人口研究, 2016, 40(5):3-5.
Wang P A. New population situation and population research[J]. Population Research, 2016, 40(5):3-5.
[3]
Morton T A, Yuan F. Analysis of population dynamics using satellite remote sensing and US census data[J]. Geocarto International, 2009, 24(2):143-163.
doi: 10.1080/10106040802460715
[4]
Zandbergen P A, Ignizio D A. Comparison of dasymetric mapping techniques for small-area population estimates[J]. Cartography and Geographic Information Science, 2010, 37(3):199-214.
doi: 10.1559/152304010792194985
Dong N, Yang X H, Cai H Y. Research progress and perspective on the spatialization of population data[J]. Journal of Geo-Information Sciences, 2016, 18(10):1295-1304.
Bai Z Q, Wang J L, Yang F. Research progress in spatialization of population data[J]. Progress in Geography, 2013, 32(11):1692-1702.
[7]
Dong N, Yang X, Cai H, et al. Research on grid size suitability of gridded population distribution in urban area:A case study in urban area of Xuanzhou District,China[J]. PloSone, 2017, 12(1):e0170830.
doi: 10.1371/journal.pone.0170830
[8]
IIimi A, Ahmed F, Anderson E C, et al. New rural access index:Main determinants and correlation to poverty[M]. World Bank Policy Research Working Paper.Washington:World Bank Group, 2016: 7876.
Tian Y Z, Chen S P, Yue T X, et al. Simulation of Chinese population density based on land use[J]. Acta Geographica Sinica, 2004(2):283-292.
[10]
Wang L, Wang S, Zhou Y, et al. Mapping population density in China between 1990 and 2010 using remote sensing[J]. Remote Sensing of Environment, 2018, 210:269-281.
doi: 10.1016/j.rse.2018.03.007
Wang M M, Wang J L. Spatialization of township-level population based on nighttime light and land use data in Shandong Province[J]. Journal of Geo-Information Science, 2019, 21(5):699-709.
Lu N, Zhang W W, Chen L J, et al. Estimation of large regional urban and rural population density based on the differences of population distribution between urban and rural:Taking Shandong Province as example[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(12):1384-1391.
[13]
Sun W, Zhang X, Wang N, et al. Estimating population density using DMSP-OLS night-time imagery and land cover data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6):2674-2684.
doi: 10.1109/JSTARS.4609443
[14]
Gaughan A E, Stevens F R, Huang Z, et al. Spatiotemporal patterns of population in mainland China,1990 to 2010[J]. Scientific Data, 2016, 3:160005.
doi: 10.1038/sdata.2016.5
pmid: 26881418
Guo X Y, Tang Y Z. Urban population estimation and accuracy analysis of Three Northeastern Provinces based on DMSP/OLS night light data[J]. Territory & Natural Resources Study, 2019(4):44-46.
Xiao D S, Yang S. A review of population spatial distribution based on nighttime 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
[17]
Karunarathne A, Lee G. Estimating hilly areas population using a dasymetric mapping approach:A case of Sri Lanka’s highest mountain range[J]. ISPRS International Journal of Geo-Information, 2019, 8(4):166.
doi: 10.3390/ijgi8040166
Zhuo L, Chen J, Shi P J, et al.Modelling population density of China in 1998 based on DMSP/OLS nighttime light image[J]. Acta Geographica Sinica, 2005(2):266-276.
[19]
Song J, Tong X, Wang L, et al. Monitoring finer-scale population density in urban functional zones:A remote sensing data fusion approach[J]. Landscape and Urban Planning, 2019, 190:103580.
doi: 10.1016/j.landurbplan.2019.05.011
Zhao X, Song Y Q, Liu Y L, et al. Population spatialization based on satellite remote sensing and POI data:Regarding Guangzhou as an example[J]. Tropical Geography, 2020, 40(1):101-109.