Using the Gini Index to quantify urban green inequality: A systematic review and recommended reporting standards
Bibliographic record
Abstract
Access to parks, ecosystem services, and urban trees support healthy people and communities. Unfortunately, access is often unequally distributed, leading to differential outcomes. Measuring the within-city distributional equality and comparing between cities can be facilitated by the Gini Index, a measure originally developed for economic disparities. To examine its applications in urban forestry and urban greening, a systematic review was conducted across 5 databases and 10 journals. Forty-one English, peer-reviewed articles were identified that used the Gini Index to measure urban green inequality, increasing exponentially since the first urban greening-related use of the Gini Index in 2011. Most studies were from China (n = 22, 54 %) and the United States (n = 10, 24 %). A Gini Index equation was reported in 27 studies (66 %) with 10 different variations used. Lorenz curves were included in 18 papers (44 %). The Gini Index was used to assess the distribution of parks and greenspaces (n = 28, 68 %), ecosystem disservices and services (n = 8, 20 %), and trees and street greenery (n = 7, 17 %). Fifteen papers (37 %) used multiple points in time to measure changes in inequality, including modeling future inequalities under different management scenarios. The Gini Index provides a quantitative measure of distributional inequality that facilitates comparisons between cities. The application of the Gini Index can help improve global comparative analyses, but only with consistent reporting of methods and findings. We provide recommended reporting procedures for researchers using the Gini Index, including 1) report the Gini Index equation, 2) visualize the Gini Index using a Lorenz curve, and 3) report the variable inputs. Greenspace research should also clearly define the inclusion/exclusion criteria of greenspace, differentiating parks versus green cover.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.145 | 0.436 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.046 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".