Accessibility Equity Analysis

Plain accessibility analysis produces a score of how much opportunity is reachable from each location and typically asks how high or low that access is. Accessibility equity analysis takes those same scores but focuses on their distribution across people and groups: it weights by population, draws Lorenz curves, computes Gini or Palma indices, and compares disadvantaged with advantaged groups. The first answers 'how accessible?'; the second answers 'how fairly is accessibility shared, and for whom?'

The Gini coefficient is a well-understood summary of inequality, originally for income, that ranges from 0 (everyone has equal access) to 1 (all access concentrated in one place). Applied to population-weighted accessibility, it condenses the Lorenz curve into one comparable number, letting analysts rank scenarios or cities by how equally reachable opportunity is distributed. It should be paired with group comparisons and maps, because a single index can mask which groups and areas drive the inequality.

You need three things: per-location accessibility scores (from a gravity, cumulative-opportunity, or floating-catchment measure built on a travel-cost matrix), population counts for the same locations, and socioeconomic attributes — income, age, car ownership, ethnicity, disability — for the groups whose equity you care about. Joining accessibility to population and group lets you weight by people and compare groups; without the socioeconomic and population layers you can describe access but not its equity.