GIS plays a critical role in climate change research and analysis by providing a spatial framework to understand and address its impacts. GIS integrates diverse data sources like satellite imagery, climate models, land use, population data, and more. This integration helps in visualizing and analyzing complex relationships between various factors influencing climate change. GIS enables the identification and mapping of vulnerable areas susceptible to climate change impacts. It helps in assessing risks related to sea-level rise, extreme weather events, changes in temperature and precipitation patterns, and their potential effects on infrastructure, ecosystems, and communities. GIS facilitates the monitoring of environmental changes over time. It enables the creation of time-series maps that illustrate changes in land cover, temperature, vegetation patterns, or sea levels, providing valuable insights into the trends and magnitude of climate change impacts. Spatial technology assists in managing natural resources more effectively. GIS can identify suitable locations for conservation efforts, optimize land use, and aid in planning for sustainable development or the implementation of renewable energy projects. GIS-based maps and analysis provide evidence for policymakers to understand the spatial distribution of climate change impacts. This information helps in formulating and implementing policies and strategies targeted at specific geographic areas most at risk. GIS-based visualizations and interactive maps are effective tools for communicating complex climate change data to the public. These visual representations help raise awareness and facilitate community engagement by demonstrating the local impacts of climate change.

Creating time series maps depicting variables such as temperature, precipitation, land cover, land use, population (/density), etc. and analysing using a variety of techniques including spatial regression will enable a good understanding of how we got to this point spatially and temporally. This is the first step prior to modeling what the future might look like. One needs to clearly understand multicollinearity among the independent variables as well.