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A vector data provides a way to represent features in a GIS environment. A feature is anything which is visible on a landscape. A vector data can be defined as an abstraction of the real world where positional data is represented in the form of coordinates.In vector data, the basic units of spatial information are points, lines or polyline and polygon. 
A vector feature has its shape represented using geometry. The geometry is
made up of one or more interconnected vertices. A vertex describes a position in space using an x, y and optionally z axis. Geometries which have vertices with a z axis are often referred to as 2.5D since they describe height or depth at each vertex, but not both. A point consists of only a single vertex, Where the geometry consists of two or more vertices and the first and last vertex are not equal, a polyline feature is formed . Where four or more vertices are present, and the last vertex is equal to the first, an enclosed polygon feature is formed.Vector features have attributes, which consist of text or numerical information that describe the features.

Advantages: Data can be represented at its original resolution and form without generalization. Graphic output is usually more aesthetically pleasing (traditional cartographic representation); Since most data, e.g. hard copy maps, is in vector form no data conversion is required. Accurate geographic location of data is maintained. Allows for efficient encoding of topology, and as a result more efficient operations that require topological information, e.g. proximity, network analysis.

Disadvantages: The location of each vertex needs to be stored explicitly. For effective analysis, vector data must be converted into a topological structure. This is often processing intensive and usually requires extensive data cleaning. As well, topology is static, and any updating or editing of the vector data requires re-building of the topology. Algorithms for manipulative and analysis functions are complex and may be processing intensive. Often, this inherently limits the functionality for large data sets, e.g. a large number of features. Continuous data, such as elevation data, is not effectively represented in vector form. Usually substantial data generalization or interpolation is required for these data layers. Spatial analysis and filtering within polygons is impossible.

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