If you work with spatial data, you’ll likely come across rasters at some point. Raster data can be thought of as a grid of cells, and each cell contains a value. This value can be continuous, such as in an elevation model where the value in each cell represents feet above sea level. The Crop Data Layer (published annually by the USDA) shows where specific crops are being grown across the US and is a good example of how rasters can store categorical data. Each cell in the CDL has an integer value, and that value corresponds to a crop.
Some rasters have multiple types of data for each cell, such as both elevation and aspect. Each characteristic of a cell is stored in a separate band so that it can be mapped separately or together as needed. Satellite images are usually raster data where each cell has a color, and sometimes the color is stored on 3 bands (red, green, and blue).
Raster data has a spatial resolution, determined by cell size, and a spatial extent, determined by the number of cells wide and tall the raster is. Usually I store raster data as TIFF files, but you’ll also see ASCII grids and other file types. JPEGs and other photo file types are also rasters, but these files can’t be mapped without additional spatial information.
At some point I’ll get into the pros and cons of raster data over vector grids or points at the centroid of each cell, but I hope this serves as a useful intro in the meantime!