Mapping Nigeria’s Population Density: Leveraging Meta’s High-Resolution Data for Socio-Economic Insights

Introduction

Population data is the backbone of infrastructure planning, disaster response, business expansion, and socio-economic development. With Meta’s high-density population datasets, we can now analyze population distribution at an unprecedented resolution—down to ward, LGA, and state levels in Nigeria.

But why does this matter? When combined with Terrain, Elevation, Climate, Obstacles, and Population data, population mapping becomes a powerful tool for:
✔ Business intelligence (market potential, logistics optimization)

✔Humanitarian operations (disaster risk assessment, aid distribution)
✔ Urban planning (road networks, housing, utilities)
✔ Public health (disease spread modelling, vaccination campaigns)

This blog merges technical geospatial workflows with real-world applications—showing how we at leelaurels combine population + Terrain (slope, landforms), Elevation (altitude, flood risk), Climate (rainfall, temperature), Obstacles (rivers, forests, infrastructure) to drive Decision-Making

In this blog, we’ll explore:

  1. How we extracted Meta’s population data and mapped them using geospatial tools.
  2. The critical link between population data and other datasets in operational planning.
  3. What we can do with this data in socio-economic and infrastructure projects.

 

Population Data Extraction

Data Source: Meta’s High-Resolution Population Datasets

Meta (formerly Facebook) provides high-resolution population estimates using:

  • Satellite imagery (identifying built-up areas)
  • Machine learning (predicting population density)
  • Census and survey data (calibration)

 

Technical Workflow: Geospatial Extraction & Mapping

To generate ward, LGA, and state-level population maps, we followed these steps:

Step 1: Data Preparation

  • Source Data: Meta’s population raster (~30m resolution).
  • Administrative Boundaries: Nigeria’s wards, LGAs, and states (shapefiles from NBS or GADM).
  • Tools Used: Python (Rasterio, GeoPandas).

 

Step 2: Zonal Statistics Extraction

  • Method: Overlay population raster with administrative boundaries.
  • Process:
    • For each ward/LGA/state, calculate:
      • Total population (sum of pixel values).
      • Population density (people per km²).

 

Step 3: Visualization & Cartography

  • Choropleth Maps: Classify population density into categories (low, medium, high).

   Output:

  • Ward-level maps (hyper-local insights).
  • LGA/State comparisons (policy planning).

See Population maps 

Dashboard

Ways we can help you leverage on Population Intelligence as Competitive Advantage and to drive Decision-Making

In the high-stakes world of oil and gas, Technical, Economic, Commercial, Organizational, and Political-Societal risk factors.  (TECOP) risk factors determine whether projects succeed or fail. But there’s an often-overlooked dimension that directly impacts all five TECOP elements: local population dynamics and socio-economic conditions

How Population Data Strengthens TECOP Analysis

  • Risk Modelling: High population density near facilities increases consequence of failure
  • Cost Forecasting: Local employment expectations affect labour budgets
  • Political Risk: Demographic trends influence government policy shifts
  • Operational Planning: Population growth projections impact long-term infrastructure needs

Population Exposure Audit for existing facilities

Pipeline Routing Optimization

Community Relations Strategy

Security Risk Assessment

OTHER AREAS OF APPLICATION INCLUDE

Humanitarian & Disaster Response

Urban Planning & Infrastructure

Business & Market Analysis

Public Health & Disease Control

Telecom Tower Placement

Conclusion

The extraction and integration of high-resolution population data with TECOP risk analysis creates:

  • Data-driven business strategies
  • better cost forecasting through realistic community cost modelling
  • reduction in surprise delays from political/community factors
  • More sustainable operations through data-driven CSR programs
  • More efficient humanitarian ops

Next Steps for Your Team: We can help you –

  1. Conduct a population exposure audit of existing assets
  2. Develop dynamic risk dashboards combining TECOP + population metrics
  3. Train cross-functional teams in geospatial socio-economic analysis

Need help implementing these solutions? Contact our geospatial risk & decisions specialists for a customized assessment. (info@leelaurelgs.com, +2348062905881, +2348070321929, +234817409633)

References:

Meta High-Resolution Population Maps

IOGP (2023) Guidance on Community Engagement in Oil and Gas Projects

World Bank (2022) Geospatial Analysis for Energy Infrastructure Planning

NNPC (2023) Niger Delta Regional Risk Atlas