Transforming spatial data from one scale to another is a challenging task known as a change of support problem (COSP). CARTO's Data Observatory uses quadkeys to create a common grid, which allows for the transformation of data on the finest spatial scale useful for analysis. Statistical methods can be used to enhance the spatial resolution of Census data by selecting a subset of covariates and applying regularization techniques such as LASSO. Transfer learning approaches can also be used to improve model accuracy by adding additional response variables with better predictive skills. The model-based downscaled data can capture spatial clustering and heterogeneity, providing valuable insights for political campaigns and electoral strategy. By using CARTO's statistical downscaling models, users can magnify their analysis and gain a deeper understanding of the relationship between location and business performance.