Examples include buffering, intersection, union, difference and symmetrical difference.

Topological operations: These operations involve analyzing the relationships between different vector features, such as determining if one feature is contained within another or if two features share a common boundary.

These are some examples of the operations that are best suited to Vector data.

Surface analysis: This operation involves analyzing the surface characteristics of a raster dataset, such as identifying the local maxima or minima, or detecting the presence of specific patterns on a surface.

These calculations are often used in fields such as hydrology and civil engineering.

Slope and aspect calculations: These operations involve calculating the slope and aspect (direction of maximum downhill slope) of a raster surface, such as a digital elevation model (DEM).

For example, you could reclassify a raster layer of land use data, where each cell has a value that corresponds to a specific land use type, to create a new layer where cells with the same land use type have the same new value.

Reclassification: This operation involves reassigning new values to cells based on their original values.

Examples include calculating the mean, median, or standard deviation of the cells within a specified neighborhood, or identifying the cells that fall within a particular range of values.

Neighborhood operations: These operations involve analyzing the values of cells in the vicinity of a particular cell.

These are some examples of the operations that are best suited to raster data. More information on the fundamentals for raster data here Spatial Processes for Raster and Vector Typical Raster Spatial Processes Depending on the phenomenon being represented the cell value can either represent the central value or represent the entire area covered by the cell. The cell value can either be a positive or a negative integer, a floating point value, or a NoData value to represent the absence of information. Each cell stores a value representing the information being portrayed by the raster data. Raster data on the other hand represents a phenomenon as a collection of pixels in rows and columns. On the other hand, a polygon is formed if the last vertex and the first vertex occupy the same location. A polyline consists of several lines connected. Polylines are formed where the last vertex and the first vertex are not occupying the same location. A line is formed by two vertexes connected. Two or more vertexes representing a specific position in space using coordinates are combined to form geometry.Ī vertex existing alone represents a point, while two or several vertexes connected form either a polyline or a polygon feature. Vector data represent features using geometry(Geometry according to the oxford dictionary is a shape and relative arrangement of parts of something). What is the Difference Between Vector and Raster Data Formats?

If there is anything the maps above should tell you is that both data types do a good job of representing geographical data.

The second one is a raster satellite image representation of the same area with additional labels.

The first map is composed of vector layers representing different features put together to form a map with labels. The figures below show a representation of the same geographical location. The two data types are very different in their internal representation, the operations you can do on them as well as their look and feel. Geospatial data can be represented using either vector data type or raster. In this article, we will cover the fundamental differences between raster and vector data. The Difference between Vector and Raster Data in GIS

Vector vs raster sai