Spatial Gini Concentration Index
Also known as: Locational Gini Coefficient, Spatial Gini Index, Geographic Concentration Index, Gini Index of Spatial Inequality
The spatial (or locational) Gini concentration index adapts the classic Gini coefficient to geography, summarizing in a single number between zero and one how unevenly an activity — an industry, a population group, a resource — is distributed across spatial units relative to a benchmark such as total population or land area. It is the workhorse measure for quantifying geographic concentration and agglomeration in economic geography.
Key highlights
- Reduces a whole distribution of regional shares to one interpretable number on a fixed zero-to-one scale.
- Benchmark-relative, so it isolates concentration beyond what the population or area distribution would produce.
- Comparable across activities, regions, and time, making it ideal for ranking and trend analysis.
- Backed by the well-understood Lorenz-curve apparatus, with clear graphical interpretation.
Intuition
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How it works
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When to use it
Use the spatial Gini concentration index when you need a single, comparable summary of how concentrated or dispersed an activity is across a set of regions, and you have the activity and a benchmark measured for each unit. It is ideal for ranking industries by agglomeration, tracking whether an activity is concentrating or dispersing over time, and comparing concentration across countries or sectors. It is descriptive and aspatial in its classic form — it ignores whether the high-share units are adjacent — so when the geographic arrangement (clustering versus scatter) matters, pair it with a spatial-autocorrelation statistic, and when a formal significance test of concentration is needed, use the Ellison–Glaeser index or a permutation test.
Strengths & limitations
- Reduces a whole distribution of regional shares to one interpretable number on a fixed zero-to-one scale.
- Benchmark-relative, so it isolates concentration beyond what the population or area distribution would produce.
- Comparable across activities, regions, and time, making it ideal for ranking and trend analysis.
- Backed by the well-understood Lorenz-curve apparatus, with clear graphical interpretation.
- Aspatial: the classic locational Gini does not use adjacency, so a checkerboard and a single cluster with the same shares score identically.
- Sensitive to the spatial unit definition and the modifiable areal unit problem — coarser zones lower measured concentration.
- Does not, on its own, provide a statistical test of whether observed concentration exceeds chance.
- Like all single-index summaries it can mask very different distributional shapes that yield the same value.
Common pitfalls
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Applications
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Frequently asked
How does the spatial Gini differ from the ordinary income Gini?
The mechanics are identical — both are twice the area between a Lorenz curve and the diagonal — but the units and the benchmark differ. The income Gini ranks individuals by income against an equal-population diagonal; the spatial Gini ranks regions by their activity-to-benchmark ratio against a proportional-distribution diagonal, so a Gini of zero means the activity is spread in proportion to population or area rather than equally per region.
Why is the locational Gini called 'aspatial'?
Because it depends only on the set of regional shares, not on where those regions sit relative to one another. Re-shuffling the high-share regions to opposite ends of the map leaves the Gini unchanged. To capture whether high values are geographically clustered you need a spatial-autocorrelation statistic such as Moran's I or Getis-Ord, which the Gini does not provide.
How is it related to the location quotient?
The location quotient is the per-region ratio of local activity share to benchmark share — exactly the quantity by which units are ordered to build the spatial Lorenz curve. The spatial Gini then aggregates the whole set of location quotients into one concentration number, so LQs are the regional detail and the Gini is the system-wide summary.
Sources
- 1.Duncan, O. D., & Duncan, B. (1955). A methodological analysis of segregation indexes. American Sociological Review, 20(2), 210–217.
- 2.Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499.
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Cite this page
ScholarGate. (2026, June 22). Spatial Gini Concentration Index. ScholarGate. https://scholargate.app/human-geography/gini-spatial-concentration