Using Spatial Data Science in Energy-Related Modeling of Terraforming the Martian Atmosphere

This paper proposes a methodology for numerical modeling of terraforming Mars' atmosphere using high-energy asteroid impact and greenhouse gas production processes. The developed simulation model uses a spatial data science approach to analyze the Global Climate Model of Mars and cellular automata to model the changes in Mars' atmospheric parameters. The developed model allows estimating the energy required to raise the planet's temperature by sixty degrees using different variations of the terraforming process. Using a data science approach for spatial big data analysis has enabled successful numerical simulations of global and local atmospheric changes on Mars and an analysis of the energy potential required for this process.