What type of data model is characterized by grid cells and continuous values?

The correct answer is indeed raster. Raster data models use a grid-based structure, where geographic areas are represented as a matrix of cells (or pixels). Each cell in the grid contains a value that represents some attribute of the area it covers, such as temperature, elevation, or land cover type. This structure allows raster data to capture continuous phenomena effectively, making it suitable for representing data that varies continuously across a landscape.

Raster models are particularly advantageous for analyses involving spatial statistics, image processing, and environmental modeling, where variables change gradually rather than in discrete steps. The continuous values assigned to each grid cell enable the representation of gradients and transitions in data, such as changes in elevation or temperature across large regions.

Understanding this type of data model is essential for GIS practitioners, as it influences how data is collected, analyzed, and visualized in various applications, such as remote sensing and terrain modeling.