Choropleth maps are an effective visualization tool for illustrating data variations across geographic regions using color and shading, often employed to show normalized data such as rates and percentages instead of raw totals to ensure fair comparisons. These maps are frequently used for visualizing spatial patterns like unemployment rates, disease incidence, and voter turnout, thus revealing trends that might remain hidden in raw data tables or charts. The design choices in choropleth maps, including color scales and classification methods, significantly influence their clarity and credibility, necessitating a thoughtful approach to data association, classification, and normalization. Effective choropleth maps use appropriate color schemes such as sequential or diverging scales to represent data accurately, and they include clear legends and labels to enhance transparency and readability. Accessibility is prioritized by choosing color palettes distinguishable for all viewers, including those with color vision deficiencies. Felt AI offers tools to streamline the creation of these maps by ensuring accurate color scaling, normalization, and geographic projection, making the process more intuitive and collaborative.