Research Methodology

GEIMS Methodology

Our comprehensive approach to measuring global education inequality through advanced data science, rigorous statistical methods, and transparent computational processes.

Step 1
Data Ingestion & Integration
We aggregate eleven core educational indicators (e.g. primary/secondary enrollment, youth literacy, PISA test scores, school infrastructure, student–teacher ratios, gender parity, rural–urban gaps, teacher training) from authoritative sources (World Bank API, DHS API, UNESCO/UIS CSVs, national surveys).
  • All raw CSVs and GeoJSON boundary files are ingested via a Python pipeline (data_loader.py)
  • Automated scripts to fetch or update them on a quarterly schedule
  • Data validation and quality checks ensure consistency across sources
Step 2
Normalization & Sub-Index Computation
Each indicator is min–max normalized to a [0,1] scale using fixed bounds (normalization.py), ensuring comparability across heterogeneous metrics.
  • We define five thematic pillars—Access, Quality, Infrastructure, Teacher Effectiveness, Equity
  • Each computed as the arithmetic mean of its component metrics (index_calculator.py)
  • Robust handling of missing data through imputation and weighting adjustments
Step 3
Composite Index Assembly (sEQI)
The final Scaled Education Quality Index (sEQI) is calculated as the geometric mean of the five sub-indices, preserving proportional relationships and penalizing any zero-value pillar.
  • Geometric aggregation ensures that poor performance in one dimension cannot be fully offset by excellence in another
  • Reflects a balanced view of education systems across all dimensions
  • Continuous validation against known education quality benchmarks
Step 4
Interactive Visualization & AI-Assisted Exploration
We deploy an interactive dashboard built on React+Next.js that renders Leaflet-powered choropleth maps and dynamic charts, allowing dynamic filtering and time-series comparison.
  • Real-time data updates with automatic refresh capabilities
  • Multi-dimensional filtering and comparison tools
  • Export capabilities for research and policy applications
Step 5
Software Engineering & Deployment
The entire codebase is modularized in Python and TypeScript, with strict type-checking, caching decorators, and automated tests for each component.
  • Continuous Integration (GitHub Actions) runs linting, unit tests, and data-validation checks on every commit
  • We containerize the back end (Flask) and host the front end on Vercel
  • Environment-protected API keys and automatic quarterly data refresh jobs

Data Sources & Coverage

GEIMS integrates data from multiple authoritative international sources to ensure comprehensive and reliable education monitoring.

World Bank API
195+ countries
Annual

Key Indicators:

  • Primary/Secondary Enrollment
  • Gender Parity Index
  • Government Education Expenditure
DHS API
90+ countries
Every 3-5 years

Key Indicators:

  • Youth Literacy Rates
  • Rural-Urban Education Gaps
  • Household Education Access
UNESCO/UIS
180+ countries
Annual

Key Indicators:

  • School Infrastructure
  • Teacher Training
  • Basic Facilities Access
PISA/TIMSS
80+ countries
Every 3 years

Key Indicators:

  • Test Scores
  • Learning Outcomes
  • Educational Achievement
Technical Implementation
This methodology ensures that GEIMS delivers transparent, up-to-date, and actionable insights into global education equity, backed by robust data engineering and AI-enhanced user experience.

AI-Powered Analytics

Machine learning algorithms for pattern detection and predictive insights

Automated Pipeline

Continuous data ingestion, validation, and processing with minimal manual intervention

User-Centric Design

Intuitive interfaces designed for policymakers, researchers, and education professionals

Explore the Platform

Experience our methodology in action through the interactive dashboard and comprehensive analytics tools.