We introduced the basics of the raster data model in Lesson 1, Part VI: Objects vs. Fields (and Vector vs. Raster). In this part, we will look at raster data in more detail and think about how different structures may have an impact upon your work as a cartographer.
Just to refresh your memory, the raster data model describes the world as a field over which the spatial distribution of some phenomenon varies. The geographical primitive that we use with this data model is a pixel. Pixels are commonly rectangular or square in shape and can be nested within each other to represent the phenomenon of interest at different levels of detail (i.e., resolutions). The raster data model is also different from the vector data model in that only one corner of the dataset needs to have an absolute georeference (i.e., coordinate in latitude and longitude or some other coordinate system); the location of all other pixels can be calculated on an as needed basis from their relative position from the georeferenced point and the resolution of the pixels.
Raster data are commonly used for three main types of mapping applications: mapping distributions of natural phenomena such as vegetation type (e.g., from a classified satellite image), as a backdrop for reference maps (e.g., digital orthophoto quadrangles created from aerial photography), or they may also be used to generate other types of surface representations that are displayed in maps (e.g., shaded relief).
The main factor that will have an impact upon your work as a cartographer with raster data is the resolution of the datasets with which you are working. As we briefly described in Lesson 1, Part VI: Objects vs. Fields (and Vector vs. Raster), raster data file sizes increase with increasing resolution. Typically, an increase of 2 times the resolution (e.g., going from 60m pixels to 30m pixels; see Figure 6.cg.32, below) will increase the file size by a factor of four.
Thanks to today's relatively fast desktop computers, file size is much less of an issue than it was previously; however, very large file sizes of high-resolution imagery (with pixels representing areas of less than 1m2 on the ground) can still have a substantial impact on your computer's performance. There are several different types of raster data structures that are designed to reduce file sizes (e.g., run-length encoding and quadtrees; see Figure 6.cg.32, below), these compression methods are most useful for raster data that do not contain a large amount of pixel to pixel variation (as most air-photos or satellite images do).
If you are using raster data as a backdrop image for your map, the resolution will also have an impact upon the visual appearance of objects within the image. At lower resolutions, many objects may take on a 'stair-step' appearance that can be distracting for the map reader, and at worst, can even prevent the map reader from being able to identify the feature (see Figure 6.cg.34, below).
The resolution of raster terrain data can also affect the appearance of shaded relief: if the source data are not at an appropriate scale for the final map product, the shaded relief can look blocky (see Figure 6.cg.35, below).
When you need to reproject raster data into a different projection, it is also important to be aware of the potential impacts of different resampling methods on the data. For example, if you have a thematic raster data set (e.g., land use or soil type), you should choose a nearest neighbor interpolation method rather than a bilinear or cubic convolution method (see Figure 6.cg.36, below). In cases where you need to use an equal area projection for the map you are creating, also be aware that reprojecting raster data can actually introduce errors into the data set, with this problem being most pronounced at lower resolutions (Usery et al. 2003).
If you are interested in investigating this subject further, I recommend the following:
- Usery, E. L., Finn, M. P., Cox, J.D., Beard, T., Ruhl, S. and M. Bearden. 2003. "Projecting global datasets to achieve equal areas." Cartography and Geographic Information Science. 30(1).