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Mapping fine-scale socioeconomic inequality using machine learning and remotely sensed data.

Nabin Pradhan1, Arun Agrawal2

  • 1School for Environment and Sustainability, University of Michigan, 440 Church Street, Ann Arbor, MI 48109, USA.

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Summary

This study introduces a new method combining satellite data and machine learning to accurately estimate socioeconomic inequality in India. This approach addresses data gaps, aiding efforts to reduce inequality globally.

Keywords:
inequalitymachine learningremote sensing

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Area of Science:

  • Socioeconomic research
  • Geospatial analysis
  • Data science

Background:

  • Estimating socioeconomic inequality at fine scales is challenging due to limited data, particularly in lower- and middle-income countries.
  • Existing methods struggle with spatiotemporal consistency and jurisdictional granularity.

Purpose of the Study:

  • To develop and validate a novel data harmonization method for generating fine-scale socioeconomic inequality estimates.
  • To improve the accuracy and granularity of inequality measurement in India.

Main Methods:

  • Combined household survey data with freely available remote sensing data (including nighttime luminosity).
  • Employed machine learning techniques and a novel data harmonization approach.
  • Utilized a spatially cross-validated machine learning model with Demographic and Health Survey data.

Main Results:

  • Achieved >84% prediction accuracy in estimating socioeconomic inequality measures.
  • Identified key remote sensing datasets that enhance predictive power for inequality estimation.
  • Demonstrated a reliable method for harmonizing asset and sociodemographic information.

Conclusions:

  • The replicable approach effectively addresses data gaps in socioeconomic inequality at subnational levels.
  • This method has the potential to enhance global inequality data for research and policy.
  • Supports Sustainable Development Goals initiatives focused on reducing socioeconomic disparities.