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Mapping China’s regional economic activity by integrating points-of-interest and remote sensing data with random forest

Author

Listed:
  • Qian Chen
  • Tingting Ye

    (Ocean College, 12377Zhejiang University, China)

  • Naizhuo Zhao

    (5620McGill University, Canada)

  • Mingjun Ding

    (12642Jiangxi Normal University, China)

  • Zutao Ouyang

    (6429Stanford University, USA)

  • Peng Jia

    (3230University of Twente, the Netherlands; International Initiative on Spatial Lifecourse Epidemiology (ISLE), the Netherlands)

  • Wenze Yue

    (12377Zhejiang University, China)

  • Xuchao Yang

Abstract

Nighttime light imageries are widely used for mapping the gross domestic product (GDP) over large areas. However, nighttime light imagery is inappropriate to disaggregate agricultural GDP and inadequate to differentiate the GDP from the secondary and tertiary sectors. Points-of-interest, a kind of geospatial big data with geographic locations and textual descriptions of the category, can effectively distinguish industrial and commercial areas, and therefore have the potential to improve the precise GDP mapping from secondary and tertiary sectors. In this study, a machine learning method, random forest, was used to disaggregate the 2010 county-level census GDP data of mainland China to 1 km × 1 km grids. Six Random Forest models were constructed for different economic sectors to explore the non-linear relationships between various geographic predictors and GDP from different sectors. By fusing points-of-interest of varying categories, the spatial distribution of economic activities from the secondary and tertiary sectors was effectively distinguished. Compared to previous studies, the strategy of developing specific Random Forest models for different sectors generated a more reasonable distribution of GDP. Our results highlight the feasibility of using point-of-interest data in disaggregating non-agricultural GDP by exploiting the complementary features of the different data sources.

Suggested Citation

  • Qian Chen & Tingting Ye & Naizhuo Zhao & Mingjun Ding & Zutao Ouyang & Peng Jia & Wenze Yue & Xuchao Yang, 2021. "Mapping China’s regional economic activity by integrating points-of-interest and remote sensing data with random forest," Environment and Planning B, , vol. 48(7), pages 1876-1894, September.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:7:p:1876-1894
    DOI: 10.1177/2399808320951580
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    References listed on IDEAS

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    Cited by:

    1. Fuliang Deng & Luwei Cao & Fangzhou Li & Lanhui Li & Wang Man & Yijian Chen & Wenfeng Liu & Chaofeng Peng, 2023. "Mapping China’s Changing Gross Domestic Product Distribution Using Remotely Sensed and Point-of-Interest Data with Geographical Random Forest Model," Sustainability, MDPI, vol. 15(10), pages 1-18, May.

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