In recent years, the generalization community has seen a resurgence, fueled in part by the renewed opportunities of AI. These tracks foundered somewhat in the late 1990s, coinciding with a general loss of faith in the promise of AI, and the rise of post-modern criticisms of the impacts of the automation of design. By the late 1980s, academic cartographers were thinking bigger, developing a general theory of generalization, and exploring the use of expert systems and other nascent Artificial intelligence technologies to automate the entire process, including decisions on which tools to use when. Early research focused primarily on algorithms for automating individual generalization operations. This is probably because it fit within both of the major two research trends of the era: cartographic communication (especially signal processing algorithms based on Information theory), and the opportunities afforded by technological advance (because of its potential for automation). Generalization was probably the most thoroughly studied aspect of cartography from the 1970s to the 1990s. As analytical approaches to geography arose in the 1950s and 1960s, generalization, especially line simplification and raster smoothing, was a target of study.
Eduard Imhof, one of the most accomplished academic and professional cartographers at the time, published a study of city plans on maps at a variety of scales in 1937, itemizing several forms of generalization that occurred, including those later termed symbolization, merging, simplification, enhancement, and displacement. Well generalized maps are those that emphasize the most important map elements while still representing the world in the most faithful and recognizable way.ĭuring the first half of the 20th century, cartographers began to think seriously about how the features they drew depended on scale.
The cartographer has license to adjust the content within their maps to create a suitable and useful map that conveys spatial information, while striking the right balance between the map's purpose and the precise detail of the subject being mapped. Instead of throwing out the building information, or trying to render it all at once, we could generalize the data into some sort of outline of the urbanized area of the region. For example, we might have the outlines of all of the thousands of buildings in a region, but we wish to make a map of the whole city no more than a few inches wide.
Whether done manually by a cartographer or by a computer or set of algorithms, generalization seeks to abstract spatial information at a high level of detail to information that can be rendered on a map at a lower level of detail. It is a core part of cartographic design. Reduction of information complexity on mapsĬartographic generalization, or map generalization, includes all changes in a map that are made when one derives a smaller-scale map from a larger-scale map or map data, or vice versa.