| Multi-scale generalization algorithms allow a user to zoom out of a dataset and still see a meaningful image. Because there is less pixel "real estate" to represent a dataset as the scale becomes smaller, we must use only the most critical and representative coordinates of the dataset. Geographers must learn the subtle differences between generalization algorithms. Generalization algorithms take different parameters into consideration when determining which coordinates should be in the post-processing dataset. Because of this, particular generalization algorithms are better suited to certain types of datasets than others. Geographers should understand which generalization algorithms work best for which types of datasets and the effects of parameter tweaking for those algorithms. We are developing an application to guide those seeking to visualize the effects of different generalization algorithms on datasets. We must carefully design the user interface, so that students without expert understanding of geography can easily understand the concepts they are to learn. The results should provide a useful tool for geography students and expert geographers alike, who wish to visualize the differences between multi-scale generalization algorithms. |
