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The Spatial GINI Coefficient
Dr. Paul C. Sutton Department of Geography University of Denver Population Assocation of America Conference San Francisco May 3rd, 2012
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Outline Lorenz Curve and the GINI Coefficient as a measure of distribution of wealth Global gridded representations of Nighttime Satellite Imagery and Population Density as an alternative source for developing a spatially derived Lorenz Curve. The Human Development Index (HDI)
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Nighttime Lights of the World
Data Products Used Nighttime Lights of the World DMSP OLS (NGDC) LandScan Global Grid of Population
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Scatterplots of Light vs Population for Pixels in China (left) and U.S. (right)
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The Spatial GINI Coefficient I
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The Spatial GINI Coefficient II (also referred to as the “Lumen GINI”)
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The “Real” Income GINI Coefficient
Note: Research suggests the United States is on track to pass Mexico
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Highly variable ‘freshness’ of Data
Due to the lack of a systematic international data collection system, the data used to calculate income Gini coefficients span nearly two decades
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The Global Spatial GINI
The Lorenz curve for the global Lumen Gini Coefficient, formed by combining the data from all countries.
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Lorenz curves and Lumen Gini coefficients for six countries
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Map of national Spatial GINI Coefficients
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Map of Subnational Spatial GINI Coefficients
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Map of Spatial GINI Coefficients at a 0.25 degree grid
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Spatial GINI vs. Income GINI
National level Lumen Gini versus Income Gini coefficients. Note the poor correlation, indicating that the two Gini’s are measuring very different phenomena.
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Spatial GINI vs Per Capita Energy Consumption and Electrification Rates
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Spatial GINI vs. Human Development Index
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Spatial GINI vs. Human Security Index
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Spatial GINI vs. Ecological Footprint
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Discussion & Conclusions
The Spatial GINI or Lumen GINI does not measure distribution of wealth in any manner similar to the Income GINI. The Spatial GINI correlates strongly and significantly with both the Human Development Index (HDI) and The Human Security Index (HSI). Simple, empirical measures such as the Spatial GINI as presented here present an opportunity for improved spatially explicit characterizations of the human condition.
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