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Global inequality remotely sensed.

M Usman Mirza1,2, Chi Xu3, Bas van Bavel4

  • 1School of Business and Economics, Maastricht University, 6211 LM, Maastricht, The Netherlands; m.mirza@maastrichtuniversity.nl marten.scheffer@wur.nl.

Proceedings of the National Academy of Sciences of the United States of America
|April 27, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel method to measure economic inequality using nighttime light data. This light-based inequality indicator correlates with traditional income inequality estimates, offering a new tool for global inequality assessment.

Keywords:
inequalitynighttime lightremote sensing

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Area of Science:

  • Socioeconomic studies
  • Geospatial analysis
  • Remote sensing applications

Background:

  • Quantifying global economic inequality is challenging due to missing household income data.
  • Existing methods rely on self-reported income, which can be incomplete or inaccurate.
  • Spatial segregation of households by income suggests potential for indirect measurement.

Purpose of the Study:

  • To investigate the potential of remotely sensed nighttime light data as a proxy for economic inequality.
  • To develop and validate a light-based indicator for measuring inequality.
  • To explore the application of this indicator for global inequality mapping.

Main Methods:

  • Quantified Gini coefficients from the spatial variation of nighttime light emission per person.
  • Correlated light-based Gini coefficients with established net income inequality estimates.
  • Validated the method across countries and within the United States at the state level.

Main Results:

  • A significant relationship was found between the light-based inequality indicator and traditional income inequality estimates.
  • The correlation held true at both international and sub-national (US states) scales.
  • The remotely sensed nature allows for high-resolution, independent inequality mapping.

Conclusions:

  • Remotely sensed nighttime light data can serve as a valuable proxy for economic inequality.
  • This light-based Gini coefficient offers an independent measure that can complement traditional data.
  • The method enables inequality estimation in data-scarce regions and enhances global inequality monitoring.