Categories: Maps and Cartography

Gini Coefficient and GIS: Mapping Income Disparities

The Gini coefficient is a long-standing measure used by major organizations, such as the UN and government entities, to measure disparities between income and wealth in countries. This provides a measure of how extreme wealth disparities and opportunities are within countries. Usually, metrics are provided at an aggregate level; however, within a country, wealth disparity can be severe, even in the most affluent countries. Increasingly, GIS and spatial measures now allow us to begin to understand where wealth disparity and social inequality can be found to be the greatest at a more micro scale.


In the United Kingdom, during the 1970s, the Gini coefficient was 0.27, where values between 0-1 measure inequality and lower values indicate greater equality. However, since the 1980s and through today, inequality has persistently increased, as the wealthy have accrued more wealth. By 2010, it has reached 0.52. A GIS project at the University of Liverpool has now created a map of residential regions across all of the UK, demonstrating where inequality has increased the most over the last 40 years. Using census data on demographics, wealth, socio-economic status, and overall life quality, including housing, researchers created a map with spatial resolution of 1 x 1 km across the entire country, with each cell categorized based on how much in wealth areas have shifted over 40 years. The results have shown that inequality has, in fact, shifted, with homeowners who are usually older and who worked traditional blue-collar employment experiencing some of the steepest declines in inequality in those 40 years. Interestingly, government policy in the 1990s increased homeownership among working class individuals, but this category has become increasingly unemployed or underemployed due to shifts of the economy away from manufacturing and mining. Neighborhoods with such individuals have become generally more deprived. Areas within the UK are categorized based as blue collar, older striving, families in council rent, struggling, affluent, mixed worker/suburban, thriving suburban, and multicultural urban. [1]

Mapping British Neighborhood Inequality and Trajectory. Source: Carto Blog.

In China, inequality has also become intensely debated, as the Chinese government is considered Communist but has witnessed rapid increases of income inequality as the economy has become more decentralized and internationally oriented. Inequality, as measured using the Gini coefficient, has been measured spatially at city-scales, where it has been demonstrated that wealth has spread from initially the Pearl River Delta in the 1990s, then spread to the Yangtze River Delta in 2000s, and more recently northern China has experienced economic expansion. However, cities in central and western China have increasingly found themselves far poorer than their eastern counter parts.[2]

In the United States, current data do allow expressions of Gini coefficient variation across the United States, although the level of detail is not as high as the UK Liverpool map. Nevertheless, other metrics are provided by the US Census Bureau, including the Theil index, mean logarithmic deviation, and the Atkinson measure. These data are based on household surveys such as the American Community Survey. Overall, in the United States, research has shown increasing income inequality, particularly since the 1970s. The next census, in 2020, could potentially have even greater spatial resolution, allowing a better picture of income inequality across the United States. [3]

While Gini coefficients have traditionally been used to measure forms of social or economic inequality, increasingly researchers see the benefit of using Gini coefficients for measuring disparity within different forms of data. For instance, spatially-explicit Gini coefficient measures show how pollution levels vary spatially and could affect individuals differently in time based on events such as storms. Thus, common pollution metrics may not full represent spatial heterogeneity within exposure of pollution and its distribution, whereas incorporating disparity metrics such as Gini coefficients could aid in showing how pollution spread and concentrates based on different atmospheric events.[4]

Gini coefficients are still mainly used to show income inequality across different countries. While country-level metrics can be useful, more detailed measures, such as the map produced by Liverpool, are helping to show how wider social inequality affects neighbourhoods. Increasingly, communities are changing as some are better able to meet the challenges of globalized economies, while others are less so. Rapidly developing countries such as China are also experience spatially diverse Gini coefficient variation, in particular its cities that are better or less connected with international trade and investment. In the United States, Gini coefficients for income can be measured at scales where household surveys have been collected, but, as of yet, fine-scale studies are not easily accomplished using traditional surveys. The Gini coefficient is a versatile metric, also lending itself well for disparity measures such as in the physical sciences and metrics that measure pollution, among other areas.



[1]    For more on the University of Liverpool project, see:


[2]    For more on inequality in China, see:  Huang, Hao, and Yehua Dennis Wei. “The Spatial–Temporal Hierarchy of Inequality in Urban China: A Prefectural City–Level Study.” The Professional Geographer71, no. 3 (July 3, 2019): 391–407. doi:10.1080/00330124.2019.1578976.

[3]    For more on income inequality in the United States, see:

[4]    For more on spatial heterogeneity and use of Gini coefficient in measuring pollution, particularly nitrous oxide, see:  Saha, Debasish, Armen R. Kemanian, Felipe Montes, Heather Gall, Paul R. Adler, and Benjamin M. Rau. “Lorenz Curve and Gini Coefficient Reveal Hot Spots and Hot Moments for Nitrous Oxide Emissions.” Journal of Geophysical Research: Biogeosciences123, no. 1 (January 2018): 193–206. doi:10.1002/2017JG004041.

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