Cartographic 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. It is a core part of cartographic design. 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.

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. Well generalized maps are those that emphasize the most important map elements while still representing the world in the most faithful and recognizable way.

During the first half of the 20th century, cartographers began to think seriously about how the features they drew depended on scale. 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. As analytical approaches to geography arose in the 1950s and 1960s, generalization, especially line simplification and raster smoothing, was a target of study.

Generalization was probably the most thoroughly studied aspect of cartography from the 1970s to the 1990s. 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). Early research focused primarily on algorithms for automating individual generalization operations. 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. 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.

In recent years, the generalization community has seen a resurgence, fueled in part by the renewed opportunities of AI. Another recent trend has been a focus on multi-scale mapping, integrating GIS databases developed for several target scales, narrowing the scope of need for generalization to the scale "gaps" between them, a more manageable level for automation.

Generalization is often defined simply as removing detail, but it is based on the notion, originally adopted from Information theory, of the volume of information or detail found on the map, and how that volume is controlled by map scale, map purpose, and intended audience. If there is an optimal amount of information for a given map project, then generalization is the process of taking existing available data, often called (especially in Europe) the digital landscape model (DLM), which usually but not always has a larger amount of information than needed, and processing it to create a new data set, often called the digital cartographic model (DCM), with the desired amount.

Many general conceptual models have been proposed for understanding this process, often attempting to capture the decision process of the human master cartographer. One of the most popular models, developed by McMaster and Shea in 1988, divides these decisions into three phases: Philosophical objectives, the general reasons why generalization is desirable or necessary, and criteria for evaluating its success; Cartometric evaluation, the characteristics of a given map (or feature within that map) that demands generalization; and Spatial and attribute transformations, the set of generalization operators available to use on a given feature, layer, or map. In the first, most conceptual phase, McMaster and Shea show how generalization plays a central role in resolving the often conflicting goals of Cartographic design as a whole: functionality vs. aesthetics, information richness vs. clarity, and the desire to do more vs. the limitations of technology and medium. These conflicts can be reduced to a basic conflict between the need for more data on the map, and the need for less, with generalization as the tool for balancing them.

One challenge with the information theory approach to generalization is its basis on measuring the amount of information on the map, before and after generalization procedures. One could conceive of a map being quantified by its map information density, the average number of "bits" of information per unit area on the map (or its corollary, information resolution, the average distance between bits), and by its ground information density or resolution, the same measures per unit area on the Earth. Scale would thus be proportional to the ratio between them, and a change in scale would require the adjustment of one or both of them by means of generalization.