Spatial indexes are essential tools for efficiently organizing and analyzing large sets of geospatial data, with systems like H3, S2, Geohash, and Hexbin offering unique features for different use cases. H3, developed by Uber, stands out due to its hexagonal hierarchical grid structure, which enhances multi-scale analysis, accurate representation of distances and areas, and efficient data joining, making it particularly suitable for applying machine learning to geospatial data. S2, created by Google, uses square cells for global coverage and is efficient for point-in-polygon operations, while Geohash provides a simple string-based encoding system for hierarchically organized square grids. Hexbin, although not a spatial index, aggregates data into hexagonal cells for visualization purposes, and administrative boundaries are often used for aggregating spatial data despite their irregular shapes. Platforms like Felt facilitate the use of H3 by providing user-friendly interfaces that allow users to visualize data with H3 hexagons, view spatial patterns at various scales, and combine H3 analysis with other geospatial data layers, thereby broadening access to advanced geospatial analysis techniques.