Data Race

Methodology

How we process and visualize data

Transparency is core to our mission. This page explains how we collect, process, and present global statistics.

Data Collection

We collect data programmatically from source APIs:

  1. Fetch raw data from data providers (World Bank, FAO, etc.)
  2. Validate data completeness and format
  3. Store data in structured format for processing

Data Processing

Raw data undergoes several processing steps:

  1. Country code standardization
  2. Missing value exclusion (no interpolation)
  3. Regional classification assignment
  4. Rank calculation (global and regional)

Ranking Calculation

Rankings are calculated for each year independently:

  • Global Rank: Position among all countries with data for that year
  • Regional Rank: Position within the assigned region
  • Rank Change: Difference from previous year's rank position
  • Year-over-Year Change: Percentage change in value from previous year

Visualization

Our visualizations are built with performance and accessibility in mind:

  • Bar Chart: Animated rankings with smooth transitions
  • Line Chart: Historical trends with interactive tooltips
  • Pie Chart: Market share and composition analysis
  • Responsive design for all screen sizes

Data Quality

We take several measures to ensure data quality:

  • Prioritize reliable, well-documented data sources
  • Preserve original values without modification
  • Missing data is excluded rather than estimated
  • Clearly indicate data gaps and limitations

Known Limitations

Users should be aware of these limitations:

  • Data availability varies by country and year
  • Some countries may have incomplete historical data
  • Methodological changes by source organizations may affect comparability
  • Economic data may be revised by source organizations
  • Small countries or territories may lack data