Code and data for the paper: Developing an Annual Global Sub-National Scale Economic Data from 1992 to 2021 Using Nighttime Lights and Deep Learning
Creators: name: Zhang, Hang
name: Dong, Guanpeng
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 440603
subject: Economic geography
classificationCode: 370401
subject: Computational modelling and simulation in earth sciences

description: <b>Abstract</b><b>:</b> The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program's Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the complex non-linear relationship between NTL and GDP per capita at sub-national scale, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data.<br>Global_sub-national_GDP-pc_with_input_output.csv contains the inputs, outputs and projections involved in multi-layer perceptron for estimating the global sub-national GDP per capita.Global_Administrative_Units.zip is the sub-national scale administrative unit dataNightime_lights_data_preprocessing_v2.ipynb contains several python codes for the basic processing and calibration of two types of nighttime lights data (DMSP-OLS and NPP-VIIRS)Light_GDP_Models_MLP_LGBM_V2.ipynb contains the model codes (MLP and LightGBM) for integrating two kinds of nighttime light data and estimating per capita GDPPaper_figures_v2.ipynb contains codes for graphing in the article using pythonSupplementary Figures and Tables.docx contains supplementary figures and tables.Hyperparameter_Grid_Search_LightGBM.csv and Hyperparameter_Grid_Search_MLP.csv are the results of the hyperparameter grid search.Modeldata_Extreme_Heat_On_Economic_Growth.csv is a panel data to evaluate the impact of extreme heat on economic growthheatwavesMeth _Extreme_Heat_On_Economic_Growth.Rmd is the R code to evaluate the impact of extreme heat on economic growth<br>