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Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data

Author

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  • Yongming Xu

    (School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Yaping Mo

    (School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

  • Shanyou Zhu

    (School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

Accurate information on the spatial distribution of poverty is of great significance to the formulation and implementation of the government’s targeted poverty alleviation policy. Traditional poverty mapping is mainly based on household survey data and statistical data, which cannot describe the spatial distribution of poverty well. This paper presents a study of mapping the integrated poverty index (IPI) in the Dian-Gui-Qian contiguous extremely poor area of southwest China. Based on multiple independent spatial variables extracted from NPP/VIIRS nighttime light (NTL) remote sensing data, digital elevation model (DEM), land cover information, open street map, and city accessibility data, eight algorithms were employed and compared to determine the optimal model for IPI estimation. Among these machine learning algorithms, traditional multiple linear regression had the lowest accuracy compared with the other seven machine learning algorithms and XGBoost showed the best performance. Feature selection was performed to reduce overfitting and five variables were finally selected. The final developed XGBoost model achieved an MAE of 0.0454 and an R2 of 0.68. The IPI map derived from the developed XGBoost model characterized the spatial pattern of poverty in the Dian-Gui-Qian contiguous extremely poor area well, which provided a good reference for the poverty alleviation work and public resources allocation in the study area. This study can also serve as a template for poverty mapping in other areas using remote sensing data.

Suggested Citation

  • Yongming Xu & Yaping Mo & Shanyou Zhu, 2021. "Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8717-:d:608414
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    References listed on IDEAS

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    Cited by:

    1. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    2. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.

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