Define Vector data structure.

The vector data structure is a method of representing spatial data in Geographic Information Systems (GIS) using discrete geometric objects with associated attribute information. This data structure is particularly suitable for representing features with well-defined boundaries and discrete locations on the Earth's surface. Key components of the vector data structure include points, lines, and polygons, each serving as a fundamental building block for spatial representation.

1. Points:

   • Points represent individual, discrete locations on the Earth's surface and are defined by a pair of coordinates (x, y) in a two-dimensional space. Each point can be associated with attributes, such as temperature, elevation, or the name of a city.

2. Lines:

   • Lines, also known as polylines or arcs, are sequences of connected points that form linear features. These linear features can represent roads, rivers, or any other features that can be modeled as continuous paths. Lines are defined by a series of vertices (points) and may have associated attributes.

3. Polygons:

   • Polygons represent enclosed areas on the Earth's surface with well-defined boundaries. They are defined by a closed loop of connected points and can represent features such as land parcels, administrative boundaries, or lakes. Polygons have both spatial and attribute components.

4. Attributes:

   • Each vector feature (point, line, or polygon) in the dataset can be associated with attribute data. Attributes are non-spatial information linked to the geographic features, providing additional details such as population, land use category, or any other relevant information.

5. Topology:

   • Vector data inherently captures topological relationships between features. Topology refers to the spatial relationships between features, including adjacency, connectivity, and containment. This makes vector data suitable for complex spatial analyses involving network connectivity and proximity.

6. Data Structure:

   • Vector datasets are organized as collections of feature classes, each containing points, lines, or polygons. Feature classes are further organized into layers, representing different thematic categories within the dataset.

7. Storage Efficiency:

   • Vector data structures are generally more storage-efficient than raster data structures, especially for representing discrete features and well-defined boundaries. However, storage efficiency can be influenced by the complexity and density of the vector dataset.

The vector data structure provides a flexible and efficient way to represent diverse spatial features in GIS. It is well-suited for applications that involve discrete objects, such as infrastructure mapping, land parcel delineation, and network analysis. The ability to capture topological relationships makes vector data particularly valuable for spatial analyses that require understanding connectivity and spatial adjacency between features.