--- authors: - admin categories: - GEE - Remote Sensing - Interactive Dashboard draft: false featured: false date: "2025-03-14T00:00:00Z" external_link: "" image: caption: "" focal_point: Smart links: - icon: open-data icon_pack: ai name: "[GEE] Google Earth Engine App" url: https://carlos-mendez.projects.earthengine.app/view/dynamicsegdpv2 - icon: markdown icon_pack: fab name: "MD version" url: https://raw.githubusercontent.com/cmg777/starter-academic-v501/master/content/post/gee_egdp_dynamics/index.md slides: summary: "An interactive exploration of the space-time dynamics of luminosity-based GDP over the 1992-2019 period." tags: - spatial - gee - regional - remote sensing title: "Regional dynamics of luminosity-based GDP 1992-2019" url_code: "" url_pdf: "" url_slides: "" url_video: "" --- {{% callout note %}} When the sun goes down and the lights turn on, [there’s still a lot to explore.](https://earth.app.goo.gl/oZzBfT)
Let's study regional development from outer space!
{{% /callout %}} **📊 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](https://doi.org/10.1038/s41597-022-01322-5) --- ### **💡 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](https://doi.org/10.1038/s41597-022-01322-5). - **GEE dataset Access:** [Awesomme GEE community catalog](https://gee-community-catalog.org/projects/elc_gdp/?h=gdp) - **Exploratory Tool:** [GEE web app by Carlos Mendez](https://carlos-mendez.projects.earthengine.app/view/dynamicsegdpv2) ---

See app in [full screen HERE](https://carlos-mendez.projects.earthengine.app/view/dynamicsegdpv2)