A raster map is a grid of constant-sized geographic squares, or "raster cells", that provides a generalized overview of the distribution of big data. It is often used to visually identify patterns in large datasets. It is most effective in scenarios where displaying individual data points is less meaningful than providing insights into data distributed over a geographical area.
Raster maps display discrete geographic cells, colored by calculated values without interpolation between data points. For example, you can map data points to geographical squares of 250x250 meters, and then average a value in the dataset for all points in each square. Then you create a raster map that displays a distribution of the averaged values.
Typical use cases for raster maps are large datasets containing geographical coordinates (latitude and longitude) and thousands or millions of records that are best represented as a distribution of data on a map.
Geo-visualization allows you to create raster maps with a fine degree of control over the data representation; see Creating Visualizations for details.
The following is an example of a raster map created with Geo-visualization. Click the image to view the full code for this example.