A Geographic Information System (GIS) requires efficient methods to store, organize, manage, analyze, and retrieve spatial as well as non-spatial data. Different data models have been developed to represent geographic features and their associated attributes. Among the most important are the Georelational Data Model, Object-Based Data Model, and Geodatabase, each offering distinct approaches to managing geographic information according to the complexity and requirements of GIS applications. The Georelational Data Model is one of the earliest and most widely used GIS data models. It separates spatial data from attribute data while maintaining a connection between them through a common identifier. In this model, spatial information is stored in graphic files representing points, lines, and polygons, whereas descriptive information is stored in relational database tables. Each geographic feature is assigned a unique identification number, which links the graphical representation with its corresponding attributes. For example, a road may be represented as a line in the spatial database, while its name, width, surface type, and maintenance status are stored in an attribute table. This model follows the principles of the relational database management system (RDBMS), making it efficient for querying and managing tabular data. Its advantages include simplicity, compatibility with relational databases, ease of updating attribute information, and efficient retrieval of records. However, the separation of spatial and attribute data makes complex spatial analysis slower, and maintaining relationships between multiple datasets can become difficult as database size increases. The Object-Based Data Model represents geographic features as complete objects that integrate geometry, attributes, and behavior into a single entity. Unlike the georelational model, spatial and non-spatial information are