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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Cartographic generalization | 3/5 | https://en.wikipedia.org/wiki/Cartographic_generalization | reference | science, encyclopedia | 2026-05-05T15:17:39.374723+00:00 | kb-cron |
Layer Selection: (also called class selection or add) the choice of which data layers or themes to include or not (for example, a street map including streets but not geology). Feature Selection: (sometimes called refinement or eliminate) the choice of which specific features to include or remove within an included layers (for example, which 50 of the millions of cities to show on a world map). In feature selection, the choice of which features to keep or exclude is more challenging than it might seem. Using a simple attribute of real-world size (city population, road width or traffic volume, river flow volume), while often easily available in existing GIS data, often produces a selection that is excessively concentrated in some areas and sparse in others. Thus, cartographers often filter them using their degree of regional importance, their prominence in their local area rather than the map as a whole, which produces a more balanced map, but is more difficult to automate. Many formulas have been developed for automatically ranking the regional importance of features, for example by balancing the raw size with the distance to the nearest feature of significantly greater size, similar to measures of Topographic prominence, but this is much more difficult for line features than points, and sometimes produces undesirable results (such as the "Baltimore Problem," in which cities that seem important get left out). Another approach is to manually encode a subjective judgment of regional importance into the GIS data, which can subsequently be used to filter features; this was the approach taken for the Natural Earth dataset created by cartographers.
=== Simplify ===
Another early focus of generalization research, simplification is the removal of vertices in lines and area boundaries. A variety of algorithms have been developed, but most involve searching through the vertices of the line, removing those that contribute the least to the overall shape of the line. The Ramer–Douglas–Peucker algorithm (1972/1973) is one of the earliest and still most common techniques for line simplification. Most of these algorithms, especially the early ones, placed a higher priority on reducing the size of datasets in the days of limited digital storage, than on quality appearance on maps, and often produce lines that look excessively angular, especially on curves such as rivers. Some other algorithms include the Wang-Müller algorithm (1998) which looks for critical bends and is typically more accurate at the cost of processing time, and the Zhou-Jones algorithm (2005) and Visvalingam-Whyatt algorithm (1992) which use properties of the triangles within the polygon to determine which vertices to remove.
=== Smooth ===
For line features (and area boundaries), Smoothing seems similar to simplification, and in the past, was sometimes combined with simplification. The difference is that smoothing is designed to make the overall shape of the line look simpler by removing small details; which may actually require more vertices than the original. Simplification tends to make a curved line look angular, while Smoothing tends to do the opposite. The smoothing principle is also often used to generalize raster representations of fields, often using a Kernel smoother approach. This was actually one of the first published generalization algorithms, by Waldo Tobler in 1966.
=== Merge === Also called dissolve, amalgamation, agglomeration, or combine This operation, identified by Imhof in 1937, involves combining neighboring features into a single feature of the same type, at scales where the distinction between them is not important. For example, a mountain chain may consist of several isolated ridges in the natural environment, but shown as a continuous chain on a small scale the map. Or, adjacent buildings in a complex could be combined into a single "building." For proper interpretation, the map reader must be aware that because of scale limitations combined elements are not perfect depictions of natural or manmade features. Dissolve is a common GIS tool that is used for this generalization operation, but additional tools have been developed for specific situations, such as finding very small polygons and merging them into neighboring larger polygons. This operator is different from aggregation because there is no change in dimensionality (i.e. lines are dissolved into lines and polygons into polygons), and the original and final objects are of the same conceptual type (e.g., building becomes building).
=== Aggregate === Also called combine or regionalization Aggregation is the merger of multiple features into a new composite feature, often of increased Dimension (usually points to areas). The new feature is of an ontological type different than the original individuals, because it conceptualizes the group. For example, a multitude of "buildings" can be turned into a single region representing an "urban area" (not a "building"), or a cluster of "trees" into a "forest". Some GIS software has aggregation tools that identify clusters of features and combine them. Aggregation differs from Merging in that it can operate across dimensions, such as aggregating points to lines, points to polygons, lines to polygons, and polygons to polygons, and that there is a conceptual difference between the source and product.
=== Typify === Also called distribution refinement Typify is a symbology operator that replaces a large set of similar features with a smaller number of representative symbols, resulting in a sparser, cleaner map. For example, an area with dozens of mines might be symbolized with only 3 or 4 mine symbols that do not represent actual mine locations, just the general presence of mines in the area. Unlike the aggregation operator which replaces many related features with a single "group" feature, the symbols used in the typify operator still represent individuals, just "typical" individuals. It reduces the density of features while still maintaining its relative location and design. When using the typify operator, a new set of symbols is created, it does not change the spatial data. This operator can be used on point, line, and polygon features.