Predicting Poverty Using Geospatial Data in Thailand

Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

  • Introduction
  • Literature Review
  • Data
  • Methods
  • Analytical Results
  • Conclusion

This page was generated from /publications/predicting-poverty-using-geospatial-data-thailand on 06 June 2024

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