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Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

Author

Listed:
  • Fahao Wang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Weidong Lu

    (Center for Historical Geographical Studies, Fudan University, Shanghai 200433, China)

  • Jingyun Zheng

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shicheng Li

    (Department of Land Resource Management, School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Xuezhen Zhang

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures ( Fu , in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient ( R 2 ) of 0.82, a positive reduction of error ( RE , 0.72) and a coefficient of efficiency ( CE ) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was ~9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.

Suggested Citation

  • Fahao Wang & Weidong Lu & Jingyun Zheng & Shicheng Li & Xuezhen Zhang, 2020. "Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1231-:d:318097
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    References listed on IDEAS

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    Cited by:

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    2. Mazhar Javed Awan & Umar Farooq & Hafiz Muhammad Aqeel Babar & Awais Yasin & Haitham Nobanee & Muzammil Hussain & Owais Hakeem & Azlan Mohd Zain, 2021. "Real-Time DDoS Attack Detection System Using Big Data Approach," Sustainability, MDPI, vol. 13(19), pages 1-19, September.

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