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Spatial queries retrieve data based on the spatial relationship between geometric entities, an essential operation in GIS for data analysis and map generation.

A geodatabase is a database or a collection of databases that can store, query, and manage spatial data efficiently, the primary data storage model for ArcGIS.

Normalization organizes data in a database to reduce redundancy and improve data integrity, crucial in GIS databases to ensure consistent, accurate geodata representations.

A data model in GIS defines how geographic data will be stored and accessed, typically vector, raster, or a hybrid model, determining how data is represented and manipulated.

A spatial database stores data that represents objects defined in a geometric space, widely used in GIS to manage spatial data like locations, areas, and geographic features.

Attribute data is the descriptive information of spatial features stored usually in tables, which in GIS may include fields such as name, type, or measurements.

Spatial indexing is a technique to improve the speed of spatial data retrieval, used in GIS to quickly find geometric objects, like a feature within a map.

Query optimization involves rearranging queries to execute more efficiently, essential in GIS for speeding up the retrieval of spatial data from complex queries.

RDBMS is a database management system that is based on the relational model, in GIS it is used to store, retrieve, manage and analyze attribute data that is linked to spatial data.

Transaction Management ensures data integrity and error recovery in databases, in GIS it controls and manages edits to spatial data, ensuring multiple concurrent users do not conflict.

Data Integrity in a GIS context ensures that spatial and attribute data is accurate and consistent over its lifecycle, critical for reliable spatial analysis and decision making.

Indexing creates a data structure that improves the speed of data retrieval operations in a database, used in GIS to accelerate spatial and attribute data queries.

Viewshed analysis determines the visible areas from a given point or set of points, in GIS it is used for planning, line of sight, and landscape analysis, often relying on digital elevation models.

Vector data represents geographic features as points, lines, and polygons, commonly used in GIS for precise data on locations, paths, and areas.