Article Details

Title · Authors · Abstract · Keywords · DOI · References

Research on Fine-scale Population Spatialization Based on Corrected WorldPop Data

CAO Jun

(Wuhan City Polytechnic, Wuhan 430064, China)

Section：Information Application · Vol. 2026, Issue 3 · Pages：155-158,164 · 2096-4706（2026）03-0155-05 · CLC number：TP39；P208 · Document code：A · DOI：10.19850/j.cnki.2096-4706.2026.03.029

Abstract

In response to the issue where traditional census methods fail to meet the demands of fine-grained urban management, this paper proposes a fine-scale population estimation and spatialization method based on corrected WorldPop data. The method begins with employing national census data to compute partition-specific correction parameters for WorldPop data. Subsequently, based on the partition-corrected WorldPop data, it further estimates the population count for each subdistrict (street) and conducts spatial visualization analysis. Finally, the estimated population is validated and evaluated through regression analysis techniques. Experimental results show that the population for street districts estimated by this proposed method exhibits a high degree of consistency with actual census data, with a correlation coefficient in the regression analysis of 0.91 and the R2 value of 0.84. This verifies the effectiveness of the method in terms of fine-scale population estimation. It holds certain reference value for advancing sustainable urban development in China, optimizing resource allocation, and enhancing urban management standards.

Keywords

WorldPop; census; fine-scale; spatialization

References

  1. [1] Liu Zhenglian Research on Fine Population Spatialization Method Based on Multi source Geographic Data [D]. Wuhan: Wuhan University, 2019
  2. [2] Wu Zhongyuan, Xu Weibei, Hu Zhongmin Fine scale population spatialization based on Tencent location big data: a case study of Moling Street, Jiangning District, Nanjing City [J]. Geography and Geographic Information Science, 2019, 35 (6): 61-65
  3. [3] Gao Xuemei, Yang Xuchao, Chen Bairu, etc Population spatialization simulation in the Bohai Rim region based on random forest model [J]. Journal of Earth Information Science, 2022, 24 (6): 1150-1162
  4. [4] 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.
  5. [5] BHADURI B，BRIGHT E，COLEMAN P，et al. LandScan USA: A High-resolution Geospatial and Temporal Modeling Approach for Population Distribution and Dynamics [J].Oak Ridge National Laboratory，2007，69（1/2）：103-117.
  6. [6] 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/OL].PLOS one，2015，10（2）：e0107042（2015-02-17）[2025-06-14].https://doi.org/10.1371/journal.pone.0107042.
  7. [7] Xu Xinliang Chinese Population Spatial Distribution Kilometer Grid Dataset [DS/OL]. [205-06-14] https://www.resdc.cn/doi/doi.aspx?doiid=32.
  8. [8] Xu Yong, Zheng Zicong, Cao Yongyin, et al. Comparative evaluation of the accuracy of four typical population spatialization products [J]. Journal of Guangzhou University: Natural Science Edition, 2023, 22 (4): 20-28
  9. [9] Qiu Ge High precision population data spatialization based on random forest model [D]. Hohhot: Inner Mongolia Normal University, 2019
  10. [10] Hangzhou Municipal People's Government Notice of Hangzhou Municipal People's Government on Adjusting Some Administrative Divisions of Hangzhou City [EB/OL]. (March 15, 2021) [April 9, 2025] https://www.hangzhou.gov.cn/art/2021/4/9/art_1229063387_1722281.html.
  11. [11] Hangzhou Municipal Bureau of Statistics Resident Population Status of Township level Census Areas in Hangzhou City [EB/OL]. (August 13, 2021) [April 9, 2025] https://tjj.hangzhou.gov.cn/art/2021/8/13/art_1229279240_3924857.html.
  12. [12] Hangzhou Municipal People's Government Notice of Hangzhou Municipal People's Government on Adjusting Some Administrative Divisions of Hangzhou City Hangzhenghan [2021] No. 25 [EB/OL]. (2021-03-15) [2022-04-09] https://zfgb.hangzhou.gov.cn/mobile_detail.shtml?doc_id=518298.
  13. [13] Office of the People's Government of Xihu District, Hangzhou City Historical Evolution [EB/OL]. (2025-01-03) [2025-06-09] https://www.hzxh.gov.cn/art/2025/1/3/art_1177894_59043728.html.

Funding

Wuhan Science and Technology Bureau Knowledge Innovation Special Project (2023020201020480); Wuhan City Vocational College "Artificial Intelligence+Education Teaching" Teaching and Research Project (2025AI015)

About the authors

Cao Jun (1987-), female, Han ethnicity, from Honghu, Hubei Province, lecturer, master's degree, research direction: geographic spatial information, smart cities.