Hexagons offer significant advantages in geospatial analysis due to their ability to model relationships efficiently while maintaining isotropy, minimizing edge effects, and improving computational efficiency. While circles are ideal for proximity-based modeling, they do not tessellate. Only three geometric shapes—rectangles, triangles, and hexagons—can fully cover a plane without gaps. Among these, hexagons provide superior data storage, visualization, and analysis benefits. However, hexagons have been underutilized due to historical inertia in geographical information systems (GIS), which favor grid-based Cartesian representations. Additionally, the absence of a standardized global hexagonal framework has hindered data comparison and integration. With increasing computational power and growing global-scale research needs, hexagons are gaining traction in geospatial analysis. Their inherent geometric strengths make them a compelling alternative for modeling spatial relationships, and their adoption is expected to rise significantly within the geospatial community.