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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Cartographic generalization | 2/5 | https://en.wikipedia.org/wiki/Cartographic_generalization | reference | science, encyclopedia | 2026-05-05T15:17:39.374723+00:00 | kb-cron |
== Theories of Map detail == 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. But what counts as a "bit" of map information? In specific cases, that is not difficult, such as counting the total number of features on the map, or the number of vertices in a single line (possibly reduced to the number of salient vertices); such straightforwardness explains why these were early targets for generalization research. However, it is a challenge for the map in general, in which questions arise such as "how much graphical information is there in a map label: one bit (the entire word), a bit for each character, or bits for each vertex or curve in every character, as if they were each area features?" Each option can be relevant at different times. This measurement is further complicated by the role of map symbology, which can affect the apparent information density. A map with a strong visual hierarchy (i.e., with less important layers being subdued but still present) carries an aesthetic of being "clear" because it appears at first glance to contain less data than it really does; conversely, a map with no visual hierarchy, in which all layers seem equally important, might be summarized as "cluttered" because one's first impression is that it contains more data than it really does. Designing a map to achieve the desired gestalt aesthetic is therefore about managing the apparent information density more than the actual information density. In the words of Edward Tufte,
Confusion and clutter are failures of design, not attributes of information. And so the point is to find design strategies that reveal detail and complexity--rather than to fault the data for an excess of complication. There is recent work that recognizes the role of map symbols, including the Roth-Brewer typology of generalization operators, although they clarify that symbology is not a form of generalization, just a partner with generalization in achieving a desired apparent information density.
== Operators == There are many cartographic techniques that are used to adjust the amount of geographic data on the map. Over the decades of generalization research, over a dozen unique lists of such generalization operators have been published, with significant differences. In fact, there are multiple reviews comparing the lists, and even they miss a few salient ones, such as that found in John Keates' first textbook (1973) that was apparently ahead of its time. Some of these operations have been automated by multiple algorithms, with tools available in Geographic information systems and other software; others have proven much more difficult, with most cartographers still performing them manually.
=== Select === Also called filter, omission One of the first operators to be recognized and analyzed, first appearing in the 1973 Keates list, selection is the process of simply removing entire geographic features from the map. There are two types of selection, which are combined in some models, and separated in others: