Regional dynamics of luminosity-based GDP 1992-2019

📊 Global 1 km × 1 km Gridded Revised Real GDP and Electricity Consumption (1992–2019) 🌍

📌 Introduction

• This study presents a high-resolution (1 km × 1 km) global dataset of real GDP and electricity consumption from 1992 to 2019.
• The dataset is based on nighttime light data, calibrated using a novel Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm.
• The aim is to provide a more accurate and continuous measurement of economic activity worldwide.
• Citation: Jiandong Chen, Ming Gao, Shulei Cheng, Wenxuan Hou, Malin Song, Xin Liu & Yu Liu (2022). Nature Scientific Data

💡 Background & Significance

• 📈 GDP and ⚡ electricity consumption are key indicators of economic development.
• Traditional economic statistics often suffer from inconsistencies, especially in developing countries.
• 🛰️ Nighttime light data from satellites has been widely used to estimate economic output, but previous approaches had limitations in accuracy and continuity.

🗂️ Methodology

📚 Data Sources

• 🛰️ Nighttime Light Data:
  • Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS)
  • National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS)
• 📊 GDP Data: Official GDP statistics from 175 countries, revised using nighttime light data.
• ⚡ Electricity Consumption Data: Collected for 134 countries.

⚙️ Data Processing & Calibration

• 🖥️ Image Unification:
  • Applied PSO-BP algorithm to standardize DMSP/OLS and NPP/VIIRS data.
  • Adjusted for sensor inconsistencies and temporal discontinuities.
• 📍 Grid-Level Estimation:
  • GDP and electricity consumption distributed using a top-down approach.
  • Revised real GDP growth based on a weighted combination of official statistics and nightlight-derived estimates.
• 🛠️ Correction Mechanisms:
  • Eliminated biases in nighttime light intensity.
  • Accounted for regional heterogeneity in economic activities.
  • Applied inter-annual continuous series correction to ensure temporal consistency in nighttime light data.

🔍 PSO-BP Algorithm for Data Calibration

• 🔄 Training Process:

  • Used artificial neural networks to train a model mapping relationships between GDP, electricity consumption, and nighttime light intensity.
  • Divided the data into training (60%) and testing (40%) samples.
  • Applied Particle Swarm Optimization (PSO) to optimize the Back Propagation (BP) neural network.
  • Iterated 50 times with 20 population size to refine model accuracy.

• 📉 Data Matching Across Sensors:

  • Addressed discrepancies between DMSP/OLS (1992–2013) and NPP/VIIRS (2012–2019) by:
    • Applying pixel-level calibration.
    • Ensuring consistency in spatial patterns by matching high/low DN values.
    • Normalizing DN values and applying machine learning for seamless integration.

• 📊 Estimation of GDP and Electricity Consumption:

  • Derived GDP growth rate as a function of official GDP and nighttime light data.
  • Applied weights (ρ = 0.94 for developed countries, ρ = 0.66 for developing countries) to adjust official GDP growth.
  • Estimated electricity consumption growth using a combined function of GDP and light intensity growth.

🔬 Technical Validation

• ✔️ Validity Testing for Nighttime Light Data

  • 🏙️ Urban Built-up Areas Validation: Compared estimated urban built-up areas with official MCD12Q1 land cover data, showing high accuracy.
  • 🌎 Cross-sectional Analysis: Strong correlation (R² ~ 0.87) between sum of DN values and national GDP/electricity consumption.
  • Validated temporal consistency of corrected light data across years.

• 🤖 Validation of PSO-BP Algorithm

  • Trained the PSO-BP model using national GDP, electricity consumption, and nighttime light data.
  • Achieved an R² > 0.99 in training and testing datasets, confirming model robustness.
  • Outperformed previous models with improved spatiotemporal consistency.
  • Compared simulated GDP/electricity consumption with external datasets, showing strong alignment.

📊 Key Findings

• 📈 Improved GDP Estimation:
  • The revised GDP dataset offers better accuracy than official statistics, particularly for developing nations.
  • Provides a more granular view of economic activities at a local level.
• ⚡ Electricity Consumption Trends:
  • The dataset captures industrial and residential electricity use trends.
  • Highlights regional disparities in energy access and usage.
• 📊 Validation Results:
  • High correlation (R² > 0.96) between estimated and actual GDP/electricity consumption values.
  • Comparison with external data sources shows significant improvement over previous models.

🌎 Applications & Implications

• 📊 Economic Research:
  • Enables detailed studies on economic growth patterns.
  • Useful for policy-making in regional development.
• ⚡ Energy Policy & Planning:
  • Helps in assessing energy demand and infrastructure needs.
  • Supports sustainable energy policy formulation.
• 🌪️ Disaster Impact Analysis:
  • Can be used to evaluate economic impacts of natural disasters.
  • Provides data for rapid response planning.

✅ Conclusion & Takeaways

• This dataset provides a valuable tool for researchers, economists, and policymakers.
• The methodology ensures high accuracy and continuity over nearly three decades, offering new insights into global economic trends.
• The dataset enables micro-level analysis, particularly for regions with poor economic statistics.
• The integration of satellite-derived economic indicators with official statistics enhances data reliability.
• Future improvements may include:
  • Integration with additional socioeconomic indicators to enhance model robustness.
  • Refinements in machine learning techniques to further reduce errors in estimation.
  • Expanding coverage to additional datasets that improve understanding of regional economic disparities.

📖 References

• Full dataset and methodology details are available at Nature Scientific Data.
• GEE dataset Access: Awesomme GEE community catalog
• Exploratory Tool: GEE web app by Carlos Mendez

See app in full screen HERE