This article is by Sydney Firmin and originally appeared on the Alteryx Data Science Blog here: https://community.alteryx.com/t5/Data-Science-Blog/Vector-and-Raster-A-Tale-of-Two-Spatial-Data-Types/ba-p/336141
Although it is true that vector and raster data are both ways of representing spatial data, and most data can be represented as either vector or raster, the data types are very different, and each one can shine brighter than the other for different use cases and data sets.
Vector data represents the world with points, lines, and polygons. Vector data is stored as a list of coordinates that define vertices (points), and a set of rules that determine if and how the vertices are joined into lines or polygons. Vector data is most useful to represent spatial phenomena that has discrete boundaries, like county borders or streets.
Raster data represents the world as a continuous surface divided into a regular grid of cells (pixels), where each cell contains a value corresponding to the measured value for the area the cell represents. The spatial resolution of raster data is determined by the size of the cells it is comprised of (e.g., one cell in a raster map can represent a 10x10m area on the surface of the Earth). Raster data can be continuous (e.g., elevation or rainfall) or discrete (e.g., land use or vegetation type).
There are many different pros and cons associated with each data type and selecting the appropriate data type for your use case or the phenomena you are trying to represent will depend on which best suits your needs.
Vector data tends to be more aesthetically pleasing, and as a result, most maps are created with vector data. Another major advantage is that vector data is not dependent on grid size. Vector data tends to be easier to register, re-project, and scale, which can make it more straightforward to use vector data from different data sources. Vector data can also allow for network analysis, where raster does not.
One of the most significant disadvantages of vector data is that it does not effectively represent continuous data.