Geospatial data is essential for uncovering patterns and relationships critical for urban planning, public health, and resource management. However, many key datasets such as socio-economic indicators, population density, or store locations—available for such analysis lack geographic references, making it difficult for geospatial analysis. This creates a significant challenge, as these datasets need to be transformed into formats compatible with Geographic Information Systems (GIS). While researchers have demonstrated the benefits of integrating data into GIS, there’s still no clear standard for handling incomplete, mismatched data or for standardization. This study addresses these challenges by exploring methods like geocoding, spatial interpolation, data fusion etc. to convert non-spatial data into usable geospatial formats. These techniques make it possible to visualize patterns, such as the connection between store accessibility and socio-economic factors. By offering practical tools and methodologies, this research bridges a critical gap in current practices, making it easier to incorporate diverse datasets into geospatial analysis. This helps improve decision-making in key areas, including urban planning and public health.