Regional Development

Accuracy of “representative population grid dataset” in arid areas: A case of Gansu-Ningxia-Qinghai region

  • Dongsheng XIAO ,
  • Ning WANG ,
  • Zhicheng LIU
Expand
  • 1. School of Civil Engineering and Surveying, Southwest Petroleum University, Chengdu 610500, Sichuan, China
    2. Disaster Prevention and Emergency Response Research Center of Southwest Petroleum University, Chengdu 610500, Sichuan, China

Received date: 2022-07-14

  Revised date: 2022-09-07

  Online published: 2023-03-31

Abstract

High-accuracy population grid datasets are of great value in the fields of risk assessment, disaster emergency response, ecological environment protection, and regional development. The characteristics and advantages of the datasets vary because of the different input data accuracy and model selection. Therefore, it is of great significance to evaluate the accuracy of datasets and analyze the applicable conditions of datasets. To this end, this study evaluated the accuracy of the WorldPop and GPWv4 datasets in the arid areas of the Gansu Province, Ningxia Hui Autonomous Region, and the Qinghai Province of northwest China. The accuracy of each dataset was quantitatively evaluated by calculating the spatial distribution of statistical and relative errors. Taking the best available unit of census data of China (township administrative division) as the research unit, the correlation analysis was conducted between the WorldPop and GPWv4 datasets and the seventh census data in 2020. The spatial distribution of statistical and relative errors is obtained through correlation analysis to quantitatively evaluate the accuracy of each dataset. Furthermore, the mapping performance of the dataset was qualitatively analyzed by visual estimation. Finally, the error sources of the dataset are discussed. The statistical error results show that WorldPop has higher accuracy than GPWv4. The correlation coefficient (r), root mean square error, average absolute error, and average absolute percentage error of WorldPop are 0.76, 23016, 0.73, and 0.60, respectively, while those of GPWv4 are 0.70, 22297, 0.75, and 0.58, respectively. Concurrently, according to the spatial distribution of the relative error, WorldPop accurately estimates the population of more areas. The visual estimation results show that the mapping performance of the two population grid datasets is similar, with the characteristics of a dense and sparse population in the east and west of the study area, respectively. This study on the accuracy of population grid datasets in arid areas is conducive to analyzing the error sources of datasets and guiding the rational use of datasets. In future research, it would be a beneficial direction to use the auxiliary data of human life to generate a unique population distribution pattern in an arid area to improve the accuracy of population datasets in the northwest arid area of China.

Cite this article

Dongsheng XIAO , Ning WANG , Zhicheng LIU . Accuracy of “representative population grid dataset” in arid areas: A case of Gansu-Ningxia-Qinghai region[J]. Arid Land Geography, 2023 , 46(3) : 505 -514 . DOI: 10.12118/j.issn.1000-6060.2022.351

References

[1] Fang J Y, Sun S, Shi P J, et al. Assessment and mapping of potential storm surge impacts on global population and economy[J]. International Journal of Disaster Risk Science, 2014, 5(4): 323-331.
[2] Wu X, Yang J, Zhang H. Analyzing spatial autocorrelation of population distribution in different spatial weights: A case of China[J]. Geomatics World, 2017, 24(2): 32-38.
[3] 高义, 王辉, 王培涛, 等. 基于人口普查与多源夜间灯光数据的海岸带人口空间化分析[J]. 资源科学, 2013, 35(12): 2517-2523.
[3] [ Gao Yi, Wang Hui, Wang Peitao, et al. Population spatial processing for Chinese coastal zones based on census and multiple night light data[J]. Resources Science, 2013, 35(12): 2517-2523. ]
[4] 杨小唤, 王乃斌, 江东, 等. 基于空间分析方法的人口空间分布区划[J]. 地理学报, 2002, 57(增刊): 76-81.
[4] [ Yang Xiaohuan, Wang Naibin, Jiang Dong, et al. Regionalization of population distribution based on spatial analysis[J]. Acta Geographica Sinica, 2002, 57(Suppl.): 76-81. ]
[5] 郭洪旭, 黄莹, 赵黛青. 城市居住人口空间分布的模拟研究——以广州市天河区为例[J]. 热带地理, 2013, 33(1): 81-87.
[5] [ Guo Hongxu, Huang Ying, Zhao Daiqing. A simulation study on the spatial distribution of urban residents: A case study of Tianhe district, Guangzhou[J]. Tropical Geography, 2013, 33(1): 81-87. ]
[6] Bhaduri B, Brigh E, Coleman P, et al. LandScan USA: A high-resolution geospatial and temporal modeling approach for population distribution and dynamics[J]. GeoJournal, 2007, 69: 103-117.
[7] 江东, 杨小唤, 王乃斌, 等. 基于RS、GIS的人口空间分布研究[J]. 地球科学进展, 2002, 17(5): 734-738.
[7] [ Jiang Dong, Yang Xiaohuan, Wang Naibin, et al. Study on spatial distribution of population based on remote sensing and GIS[J]. Advances in Earth Science, 2002, 17(5): 734-738. ]
[8] UNEP. Global resource information database[DB/OL]. [2022-09-24]. http://na.unep.net/siouxfalls/datasets/datalist.php.
[9] Center for International Earth Science Information Net-work. Global Rural-Urban Mapping Project (GRUMP), alpha version: Urban extents[R]. New York: Center for International Earth Science Information Network (CIE-SIN), Columbia University of Chicago Magazine, 2004.
[10] Balk D L, Deichmann U, Yetman G, et al. Determining global population distribution: Methods, applications and data[J]. Advances in Parasitology, 2006, 62: 119-156.
[11] OpenGMS. Open data[DB/OL]. [2022-09-24]. https://geomodeling.njnu.edu.cn/dataItem/5cd547056af4560e7433dd2e.
[12] Nieves J J. Modelling global human settlement to better inform annual population modelling[D]. Southampton, United Kingdom: University of Southampton, 2020.
[13] Linard C, Alegana V A, Noor A M, et al. A high-resolution spatial population database of Somalia for disease risk mapping[J]. International Journal of Health Geographics, 2010, 9(1): 1-13.
[14] 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.
[14] [ Bai Zhongqiang, Wang Juanle, Yang Fei. A summary of the research on population data spatialization[J]. Progress in Geography, 2013, 32(11): 1692-1702. ]
[15] 董南, 杨小唤, 蔡红艳. 人口数据空间化研究进展[J]. 地球信息科学学报, 2016, 18(10): 1295-1304.
[15] [ Dong Nan, Yang Xiaohuan, Cai Hongyan. Research progress and perspective on the spatialization of population data[J]. Journal of Geo-information Science, 2016, 18(10): 1295-1304. ]
[16] 林丽洁, 林广发, 颜小霞, 等. 人口统计数据空间化模型综述[J]. 亚热带资源与环境学报, 2010, 5(4): 10-16.
[16] [ Lin Lijie, Lin Guangfa, Yan Xiaoxia, et al. Spatialization models of census data: A review[J]. Journal of Subtropical Resources and Environment, 2010, 5(4): 10-16. ]
[17] Bai Z Q, Wang J L, Wang M M, et al. Accuracy assessment of multi-source gridded population distribution datasets in China[J]. Sustainability, 2018, 10(5): 1363, doi: 10.3390/su10051363.
[18] Hall O, Stroh E, Paya F. From census to grids: Comparing gridded population of the world with Swedish census records[J]. The Open Geography Journal, 2012, 5(1): 1-5.
[19] Stevens F R, Gaughan A E, Linard C, et al. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data[J]. PLoS One, 2015, 10(2): e0107042, doi: 10.1371/journal.pone.0107042.
[20] Tatem A J, Campiz N, Gething P W, et al. The effects of spatial population dataset choice on estimates of population at risk of disease[J]. Population Health Metrics, 2011, 9(1): 1-14.
[21] 王雪梅, 李新, 马明国. 基于遥感和GIS的人口数据空间化研究进展及案例分析[J]. 遥感技术与应用, 2004, 19(5): 320-327.
[21] [ Wang Xuemei, Li Xin, Ma Mingguo. Research progress and case analysis of population data spatialization based on remote sensing and GIS[J]. Remote Sensing Technology and Application, 2004, 19(5): 320-327. ]
[22] Xu Y, Ho H C, Knudby A, et al. Comparative assessment of gridded population data sets for complex topography: A study of southwest China[J]. Population and Environment, 2021, 42(3): 360-378.
[23] 林丹淳, 谭敏, 刘凯, 等. 代表性人口空间分布数据集的精度评价——以2010年广东省为例[J]. 热带地理, 2020, 40(2): 346-356.
[23] [ Lin Danchun, Tan Min, Liu Kai, et al. Accuracy comparison of four gridded population datasets in Guangdong Province[J]. China Tropical Geography, 2020, 40(2): 346-356. ]
[24] 石英, 米瑞华. 陕西省人口空间分异研究[J]. 干旱区地理, 2015, 38(2): 368-376.
[24] [ Shi Ying, Mi Ruihua. Differentiation of population spatial distribution in Shaanxi Province[J]. Arid Land Geography, 2015, 38(2): 368-376. ]
[25] 王丰龙, 刘云刚. 准行政区划的理论框架与研究展望[J]. 地理科学, 2021, 41(7): 1149-1157.
[25] Wang Fenglong, Liu Yungang. Theoretical framework and prospect on quasi-administrative division[J]. Scientia Geographica Sinica, 2021, 41(7): 1149-1157.
[26] 米瑞华, 高向东. 陕西省人口分布影响因素的空间计量分析[J]. 干旱区地理, 2020, 43(2): 491-498.
[26] [ Mi Ruihua, Gao Xiangdong. Factors influencing population distribution in Shaanxi Province using spatial econometric analysis[J]. Arid Land Geography, 2020, 43(2): 491-498. ]
[27] Long H L, Li Y R, Liu Y S, et al. Accelerated restructuring in rural China fueled by ‘increasing vs. decreasing balance’land-use policy for dealing with hollowed villages[J]. Land Use Policy, 2012, 29(1): 11-22.
[28] Jiao J Y, Zhang Z G, Bai W J, et al. Assessing the ecological success of restoration by afforestation on the Chinese Loess Plateau[J]. Restoration Ecology, 2012, 20(2): 240-249.
[29] Briggs D J, Gulliver J, Fecht D, et al. Dasymetric modelling of small-area population distribution using land cover and light emissions data[J]. Remote Sensing of Environment, 2007, 108(4): 451-466.
Outlines

/