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

Qian Chen, Tingting Ye, Naizhuo Zhao, Mingjun Ding, Zutao Ouyang, Peng Jia, Wenze Yue, Xuchao Yang*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)1876-1894
Number of pages19
JournalEnvironment and Planning B: Urban Analytics and City Science
Volume48
Issue number7
Early online date27 Aug 2020
DOIs
Publication statusPublished - 1 Sep 2021

Keywords

  • China
  • GDP
  • points-of-interest
  • random forest
  • remote sensing
  • ITC-ISI-JOURNAL-ARTICLE
  • UT-Hybrid-D

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