Research on the relationship and prediction model between nighttime lighting data, pm2.5 data, and urban GDP.

With the discovery of electricity and the widespread adoption of lighting technology, the extensive application of electricity has greatly increased productivity, making night-time factory production possible. At the same time, the rapid expansion of factories has led to a significant increase in particulate matter 2.5 (PM2.5) in the air. However, economic development heavily relies on lighting and factory production. To address this issue, researchers have focused on predicting urban gross domestic product (GDP) through night-time lights and PM2.5, but current studies often focus on the impact of a single factor on GDP, leaving room for improvement in model accuracy. In response to this problem, this article proposes the Relationship and Prediction Model between Night Light Data, PM2.5, and Urban GDP (R&P-NLPG model). Firstly, night light data, PM2.5 data, and GDP data are collected and preprocessed. Secondly, correlation analysis is conducted to analyze the correlation between data features. Then, data fusion methods are used to integrate features between night-time data and PM2.5 data, forming the third data features. Next, a neural network is constructed to establish a functional relationship between features and GDP. Finally, the trained neural network model is used to predict GDP. The experimental results demonstrate that the predictive capability of the R&P-NLPG model outperforms GDP prediction models constructed with single-feature input and existing multi-feature input.