OceeDiTT/economic-inequality-index-estimation

The project aimed to estimate a metric of local inequality derived from openly sourced nightlight and gridded population data. By merging these data, per capita nightlight emissions values were calculated at the grid cell level and used to determine the Gini index of inequality for the 774 LGAs in Nigeria in 2023. # Case Study: Mapping Economic Inequality in Nigeria Using Nighttime Light Data A geospatial data science project that estimates economic inequality across Nigeria’s 774 Local Government Areas (LGAs) using satellite-derived nighttime light (NTL) data, gridded population datasets, and spatial analysis techniques. The project leverages Google Earth Engine (GEE), Python, GIS, and Earth Observation (EO) data to generate fine-grained local inequality estimates (Gini coefficient). --- ## Overview Economic inequality statistics in Nigeria are traditionally available only at national or state levels, limiting the ability to identify local disparities and target interventions effectively. This project introduces a satellite-based approach for estimating economic inequality at the lowest administrative unit level using: - Nighttime Light (NTL) emissions - Population density grids - Geospatial analysis - Gini coefficient modeling By combining satellite illumination data with population datasets, the study generates localized inequality estimates and choropleth maps for all LGAs in Nigeria for 2023. --- ## Objectives - Estimate economic inequality at the LGA level in Nigeria - Develop fine-scale inequality maps using Earth Observation data - Analyze regional inequality patterns across Nigeria - Support SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) - Demonstrate the potential of NTL data for socioeconomic analysis --- ## Methodology ### Data Sources - SNPP-VIIRS Nighttime Light (NTL) data - Global Human Settlement Layer (GHSL) population data - Nigeria LGA boundary datasets - Google Earth Engine (GEE) ### Analytical Workflow 1. Acquire VIIRS NTL and population datasets in GEE 2. Filter and preprocess spatial datasets 3. Compute per-capita nighttime light emissions 4. Apply logarithmic transformation to NTL values 5. Calculate Gini coefficients for each LGA 6. Generate inequality distribution maps 7. Validate results against national inequality statistics …