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Optimized Model Based on Random Forest Application on Population Density Dataset: Taking Shijiazhuang as an Example (2007)

LI Lingling1LIU Jinsong*1,2,3,4LI Zhi1,2,3,4WEN Peizhang1LI Yancheng1LIU Yi1
1 School of Geographical Sciences,Hebei Normal University,Shijiazhuang 050024,China2 Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change,Shijiazhuang 050024,China3 GeoComputation and Planning Center of Hebei Normal University,Shijiazhuang 050024,China4 Hebei Key Laboratory of Environmental Change and Ecological Construction,Shijiazhuang 050024,China


Published:May 2024

Visitors:933       Data Files Downloaded:34      
Data Downloaded:4496.79 MB      Citations:

Key Words:

population density,random forest model,Shijiazhuang,grid


Based on the aggregated registered population data of Shijiazhuang as of 24:00 on April 30, 2007, the authors applied the optimized random forest model for population density to obtain the population density grid dataset for Shijiazhuang (SJZ_POP_2007). The SJZ_POP_2007 dataset includes the following data from 8 groups: (1) predicted population density data for Shijiazhuang (Predict_Data); (2) population density data for Shijiazhuang (Result_Data). Validity testing was conducted at the township level, and the goodness of fit (R2) of the result data reached 0.967. The SJZ_POP_2007 dataset has a spatial resolution of 100 m. The dataset is archived in .tif format, and consists of 64 data files with data size of 279 MB (compressed into one file, 132 MB). The related paper was published in Acta Geographica Sinica, Vol. 78, No. 5, 2023.

Foundation Item:

National Natural Science Foundation of China (42071167, 42201197, 40871073); Natural Science Foundation of Hebei Province (D2007000272); Hebei Normal University (L2024ZD07)

Data Citation:

LI Lingling, LIU Jinsong*, LI Zhi, WEN Peizhang, LI Yancheng, LIU Yi. Optimized Model Based on Random Forest Application on Population Density Dataset: Taking Shijiazhuang as an Example (2007)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2024.


     [1] Zhang, C. X. Using latitude and longitude grid cells to compile a population density map: Taking the Beijing-Tianjin-Tangshan area as an example [J]. Areal Research and Development, 1985, 4(2): 57-66.
     [2] Hu, H. Y. The distribution, regionalization and prospect of China's population [J]. Acta Geographica Sinica, 1990, 45 (2): 139-145.
     [3] Wu, J. G. Landscape Ecology: Pattern Process Scale and Hierarchy (2nd Edition) [M]. Beijing: Higher Education Press, 2007: 147-154.
     [4] Zheng, D., Ou Y., Zhou, C. H. Understanding of and thinking over geographical regionalization methodology [J]. Acta Geographica Sinica, 2008, 63(6): 563-573.
     [5] Hebei Population and Family Planning Commission. Research Report on the Functional Area of Population Development in Hebei Province [M]. Shijiazhuang: Hebei People's Publishing House, 2009.
     [6] Liu, J. S. The geographical meaning about the modifiable areal unit problem in the population density scaling [D]. Shijiazhuang: Hebei Normal University, 2009.
     [7] 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.
     [8] Gaughan, A. E., Stevens, F. R., Huang, Z. J., et al. Spatiotemporal patterns of population in mainland China, 1990 to 2010 [J]. Scientific Data, 2016, 3: 160005.
     [9] Tatem, A. J. WorldPop, open data for spatial demography [J]. Scientific Data, 2017, 4: 170004.
     [10] Tan, M., Liu, K., Liu, Lin., et al. Spatialization of population in the Pearl River Delta in 30 m grids using random forest model [J]. Progress in Geography, 2017, 36(10): 1304-1312.
     [11] Feng, X, T. Social Research Methods (5th Edition) [M]. Beijing: China Renmin University Press, 2018: 75-78.
     [12] Wardrop, N. A., Jochem, W. C., Bird, T. J., et al. Spatially disaggregated population estimates in the absence of national population and housing census data [J]. PNAS, 2018, 115(14): 3529-3537.
     [13] Wang, C., Kan, A. K., Zeng, Y. L., et al. Population distribution pattern and influencing factors in Tibet based on random forest model [J]. Acta Geographica Sinica, 2019, 74(4): 664-680.
     [14] Wang, Z., Xia, H. B., Tian, Y., et al. Big data analysis on the existence of Hu Huanyong Line: Ecological and new economic geography understanding of China's population distribution characteristics [J]. Acta Ecologica Sinica, 2019, 39(14): 5166-5177.
     [15] Ye, T. T., Zhao, N. Z., Yang, X. C., et al. Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model [J]. Science of the Total Environment, 2019, 658: 936-946.
     [16] Leyk, S., Gaughan, A. E., Adamo, S. B., et al. The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use [J]. Earth System Science Data, 2019, 11(3): 1385-1409.
     [17] Liu, Y., Yang, X. J., Liu, J. S. Experimental study on optimization of population density models based on random forest [J]. Global Change Research Data Publishing & Repository, 2020, 4(4): 402-416.
     [18] Li, L. L., Liu, J. S., Li, Z., et al. Experimental study of population density using an optimized random forest model [J]. Acta Geographica Sinica, 2023, 78(5): 1304-1320.

Data Product:

ID Data Name Data Size Operation
1 SJZ_POP_2007.rar 135432.61KB