Unequal World

§ Methodology

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Johnny Miller / Unequal Scenes

Compare inflections

Assemble 2–4 turning points — sourced policy stories, global events, or algorithmically-detected bends — and read what they share. Every point traces back to its Data360 indicator.

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Compare countries on World Bank Data360

Pick up to four economies. Each cell is verifiable against its WDI indicator on Data360.

Δ shows the difference vs. the leftmost country. Greener = the indicator favours that country, redder = it favours the leftmost.

What's Your Brain Age?

Your environment shapes your brain more than you think.
Enter your city to discover how where you live affects your brain's biological age.

Refine Your Profile

These questions help estimate how your personal situation modifies your city's baseline exposome.

Based on environmental data from the Global Human Settlement Layer (EU JRC), World Bank, and Legaz et al. (2026) Nature Medicine. This is a risk estimate, not a clinical diagnosis. Individual brain aging varies based on genetics, lifestyle, and factors not captured here.

Inequality Stories

Immersive scrollytelling built on real Google Earth Engine satellite data, paired with original aerial photography.

Detroit · United States
The Line That Was Drawn
A 1939 redlining map, a half-mile concrete wall, and the income line that is still measurable 85 years on.
Lima · Peru
The Wall of Shame
A 10 km wall divides Las Casuarinas from Pamplona Alta. INEI's block-level income strata jump across it.
Bali · Indonesia
The Rice Fields Are Disappearing
Bali loses ~1,254 hectares of rice paddy a year to villas and sprawl, and the satellites show it getting less green.
Cape Town · South Africa
The Cliff
Measured household income falls off a cliff along the apartheid line: ~R820k to ~R25k over 12 km.
Buenos Aires · Argentina
The Exposome Divide
Villa 31 and Puerto Madero sit 800 m apart. The exposome explains why one neighbourhood ages faster.
Lagos · Nigeria
Unequal Expansion
How Africa's largest city grew from 3M to 15M — and who got left behind, told through five epochs of satellite data.
Johannesburg · South Africa
The Wall Between Worlds
Primrose and Makause share a boundary. They share nothing else.
Global · North & South
The Reverse Flow
Aid, remittances, FDI and debt: which way does the money really flow? Real World Bank flow data, animated across the globe.

About Unequal World

Unequal World is a living atlas of global inequality that holds three things as equals: original Unequal Scenes aerial photography, frontier neuroscience on how environments shape brain aging, and authoritative global data. The ambition is one place to see, compare and verify how unequal the world has become, and to feel it.

The photography is Johnny Miller's Unequal Scenes: the aerial archive that makes spatial inequality impossible to look away from, here geolocated to the cities and the data it portrays. The science is Brain age, our estimate of how a place's environment (air, water, green space, inequality, infrastructure) is associated with accelerated brain aging, grounded in Legaz et al. (2026, Nature Medicine) and developed as a research collaboration (not a formal endorsement). It is a directional, population-level index, not a diagnosis; the full disclaimer and institutional partners are set out on the Methodology page.

The data is drawn from the World Bank (Data360 / WDI), IMF and UNICEF, ESA and NASA satellites, and national census offices across 20 countries; every country indicator is geolocated, time-aware, and one click from its authoritative source. On top of it we build city-level maps ourselves: 100 m "development burden" grids and 1930s redlining boundaries laid over today's census income. The whole platform was built end-to-end with AI.

The four lenses

  • Health: leads with Brain age, our derived estimate of environmental brain aging (Legaz et al. 2026, Nature Medicine), plus life expectancy, water, literacy.
  • Economy: Gini, poverty, GDP/capita, internet; toggle financial flows for animated ODA / remittances / FDI / climate-debt arcs.
  • Urbanization: urban population, slum share, electricity, internet access.
  • Planet: CO₂ per capita, forest area, renewable electricity.

How the analysis works (and where AI fits)

  • Algorithmic cross-referencing: deterministic statistics (not machine learning) that read across the World Bank series to surface source-sealed comparisons a single-indicator chart can't: peer anomalies (click any country → "how it defies its income + region peers", robust median + MAD, sample size disclosed) and divergences (teal "↑↓" pins on the time-slider globe, where two indicators that usually move together broke apart; click one to graph both series). Correlation, not causation; every claim carries a Data360 verify chip.
  • Inflection detection: an algorithm scans every World Bank time-series for statistically significant trend reversals (5-year rolling slope change > 1.5σ). Detected bends are colour-coded improvement / decline / catastrophe, and 187 carry a hand-researched, source-linked cause.
  • Generative AI: used to build the tool: one person assembled it with generative AI as the primary engine, from satellite data, national census offices across 20 countries, and the World Bank API. The runtime analytics above are deterministic, not AI.

Aligned to Data360's five focus areas

Data360 organises its thousands of indicators into five areas. Our tabs map directly onto them, so the platform reads as a focused lens on the World Bank's own taxonomy:

  • People → our Health tab (life expectancy, water, literacy, brain age).
  • Prosperity → our Economy tab (Gini, poverty, GDP/capita, flows).
  • Infrastructure → our Urbanization tab (urban & slum share, electricity).
  • Planet → our Planet tab (CO₂, forest, renewable electricity).
  • Digital → partially covered today (internet access on Economy & Urbanization); a dedicated Digital tab (broadband, mobile) is the next tab on the roadmap.

Data & provenance

  • World Bank Data360 / WDI: every country indicator; one-click "Verify on Data360" on each value.
  • Federated Data360 sources: beyond WDI, the country panel pulls real values from IMF (World Economic Outlook, current account) and UNICEF (child underweight), proving cross-database reach. Each carries its own Data360 verify chip.
  • Satellite: ESA Sentinel-2 (vegetation), Sentinel-5P (NO₂), NASA VIIRS (night lights), EU JRC GHSL (built-up + population).
  • Neighbourhood wealth: national census & statistics offices across 20 countries (IBGE census sector, US ACS block group, StatsSA Small Area Layer, INEI Peru, CONAPO Mexico, DANE Colombia, etc.).
  • Future: IIASA Shared Socioeconomic Pathways (post-2024 projections).

How to trust each number

Every value is tiered so you know what you are reading: measured data is shown as-is and source-linked, anything we derive (the development-burden composite, Brain age) is labelled derived, and known measurement artifacts are flagged or suppressed rather than shown as events.

The tier system, the comparability rules, the burden formula, satellite biases and every known limitation are documented on the Methodology page.

Free for newsrooms & non-profits (commercial project). Hosted on Cloudflare Pages. Photography © Johnny Miller / Unequal Scenes. All rights reserved; used in Unequal World only and not licensed for any other use. Built with Claude Code. Full methodology: read the methodology page (how every number is sourced, tiered, derived and flagged).
Guide · Methodology · Data World Bank Data360, EU JRC GHSL, Google Earth Engine, ESA Sentinel, NASA VIIRS, SEDAC GRDI, Meta RWI · Science Legaz et al. 2026, IIASA SSP · Photography © Johnny Miller / Unequal Scenes. All rights reserved · Built with Claude Code