GWR is a type of spatial statistics tool that quantifies the strength of relationships between target and correlation variables, adding more insight to analysis and helping to ascertain if trends are global or local. It performs a local least squares regression for every input cell in a continuous grid, with weights assigned based on a user-defined kernel. The output is a coefficient variable for each cell, indicating positive or negative relationships between the coefficient and target variables. GWR can be used to analyze various use cases such as traffic accident rates, CPG revenue, and house prices. In this tutorial, GWR is applied to San Francisco's street trees dataset to examine the relationship between demographic variables and tree count. The analysis involves creating a spatial index grid, aggregating demographic data, and running GWR on the compiled data. The results show positive correlations between median income and tree count in certain areas of the city.