# Difference Between Raster And Vector Data

## What is Raster Data?

Raster data provides a representation of the world as a surface divided up into a regular grid array, or cells, where each of these cells has an associated value. In an alternate sense, we can consider a digital photograph as an example of a raster dataset. Here each cell, which in this instance is referred to as a pixel, corresponds to a particular color value.

When transferred into a GIS setting, the cells in a raster grid can potentially represent other data values, such as temperature, rainfall or elevation. The main point of difference between the digital photograph and the GIS representation is that in the GIS there is accompanying data detailing where the cells can be found on a globe and how big these cells can be.

• Raster data can represent continuous phenomena, such as elevation, temperature, and precipitation, with a high level of detail.
• Raster datasets are relatively straightforward to understand and work with. They consist of a regular grid, where each cell contains a single data value.
• Raster data is typically more space-efficient for storing large-scale geographic information, especially when dealing with continuous or high-resolution data.
• Raster data is well-suited for various spatial analyses, including overlay operations, distance calculations, and terrain modeling. Operations like map algebra, which involves performing mathematical operations on raster layers, are efficient and widely used in GIS.
• Raster data is suitable for creating visually appealing maps and graphics, as it can represent complex terrain, satellite imagery, and other spatial information in a way that is familiar to users.

• Raster datasets can become quite large, especially at high resolutions or when covering large geographic areas.
• Raster data represents geographic features as cells or pixels, and this discretization can result in a loss of detail, especially when converting vector data to raster format.
• Raster data is not well-suited for representing irregularly shaped or complex boundaries.
• While raster data can represent continuous phenomena, it is less effective at storing attribute data associated with geographic features.
• Raster data lacks inherent topological relationships between features, which are essential for certain types of spatial analysis.

## What is vector Data?

Vector data is what most people think of when they consider spatial data. Data in this format consists of points, lines or polygons. At its simplest level, vector data comprises of individual points stored as coordinate pairs that indicate a physical location in the world. These points can be joined, in a particular order, to form lines or joined into closed areas to form polygons.

Vector data is extremely useful for storing and representing data that has discrete boundaries, such as borders or building footprints, streets and other transport links, and location points. Ubiquitous online mapping portals, such as Google Maps and Open Street Maps, present data in this format.

• Vector data excels at representing precise geographic features, such as roads, property boundaries, and administrative boundaries.
• Compared to raster data, vector data tends to be more space-efficient, especially when representing features with simple geometries.
• Vector data inherently maintains topological relationships between features, such as adjacency, connectivity, and containment.
• Vector data can store large attribute information for each geographic feature. This makes it well-suited for applications that require detailed attribute data, such as demographic information, land use classifications, or infrastructure attributes.
• Vector data is used for cartographic purposes, as it allows for the creation of visually pleasing and accurate maps. It provides the flexibility to adjust feature symbology, labeling, and representation to produce maps that effectively convey information to users.

• Vector data is not well-suited for representing continuous phenomena, such as elevation or temperature gradients.
• Complex vector datasets with many features, particularly those with intricate geometries (e.g., detailed road networks), can be challenging to manage in terms of storage and processing.
• Vector data can struggle to represent complex three-dimensional (3D) geometries and temporal changes effectively.
• Visualizing continuous data in vector format can be less intuitive than in raster format. Representing gradients or continuous surfaces in vector maps often requires the use of contour lines or point symbols, which may not convey the data’s full richness.
• Maintaining topological relationships among vector features can be challenging. Errors such as gaps, overlaps, and sliver polygons can occur during data creation and editing.

## Key Takeaways

• Vector data structures represent specific features on the Earth’s surface, and assign attributes to those features. Vectors are composed of discrete geometric locations (x, y values) known as vertices that define the shape of the spatial object.
• Vector data is a geographic data type where data is stored as a collection of points, lines, or polygons along with attribute data. Individual points recorded as coordinate pairs, which represent a physical position in the world, make up vector data at its most basic level.
• Vector data is the most common type of GIS data. Most data loaded into a GIS software program tends to be in vector data. Vector data represents geographic data symbolized as points, lines, or polygons.
• Vector data is considered to be a more traditional method for cartographic representation, it delivers a representation that is sharp, clean and scalable.
• Raster data is a geographic data type where data is stored as a grid of regularly sized pixels along with attribute data. Individual pixels linked to specific coordinates, which represent a physical position in the world, make up categorical or continuous raster data at its most basic level.
• Raster data represents geographic data as a matrix of cells that each contains an attribute value. While the area of different polygon shapes in a data set can differ, each cell in a raster data set is the same cell. The size of the area in the real world that each cell represents is known as the spatial resolution.
• Raster data is most commonly found in remotely sensed data, shaded relief and topographic data, satellite imagery, and aerial imagery.