Cartographic generalization

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Cartographic generalization, or map generalization, is the method whereby information is selected and represented on a map in a way that adapts to the scale of the display medium of the map, not necessarily preserving all intricate geographical or other cartographic details. The cartographer is given license to adjust the content within their maps to create a suitable and useful map that conveys geospatial information, while striking the right balance between the map's purpose and actuality of the subject being mapped. Scaling hierarchy or far smaller than larger ones is found to be a universal rule for cartographic generalization (1).

Well generalized maps are those that emphasize the most important map elements while still representing the world in the most faithful and recognizable way. The level of detail and importance in what is remaining on the map must outweigh the insignificance of items that were generalized, as to preserve the distinguishing characteristics of what makes the map useful and important.


Some cartographic generalization methods include the following:


Map generalization is designed to reduce the complexities of the real world by strategically reducing ancillary and unnecessary details. One way that geospatial data can be reduced is through the selection process. The cartographer can select and retain certain elements that he/she deems the most necessary or appropriate. In this method, the most important elements stand out while lesser elements are left out entirely. For example, a directional map between two points may have lesser and un-traveled roadways omitted as not to confuse the map-reader. The selection of the most direct and uncomplicated route between the two points is the most important data, and the cartographer may choose to emphasize this.


Generalization is not a process that only removes and selects data, but also a process that simplifies it as well. Simplification is a technique where shapes of retained features are altered to enhance visibility and reduce complexity. Smaller scale maps have more simplified features than larger scale maps because they simply exhibit more area. An example of simplification is to scale and remove points along an area. Doing this to a mountain would reduce the detail in and around the mountain but would ideally not detract from the map reader interpreting the feature as such a mountain.


Simplification also takes on other roles when considering the role of combination. Overall data reduction techniques can also mean that in addition to generalizing elements of particular features, features can also be combined when their separation is irrelevant to the map focus. A mountain chain may be isolated into several smaller ridges and peaks with intermittent forest in the natural environment, but shown as a continuous chain on the map, as determined by scale. The map reader has to, again remember, that because of scale limitations combined elements are not concise depictions of natural or manmade features.


Smoothing is also a process that the map maker can employ to reduce the angularity of line work. Smoothing is yet another way of simplifying the map features, but involves several other characteristics of generalization that lead into feature displacement and locational shifting. The purpose of smoothing is to exhibit linework in a much less complicated and a less visually jarring way. An example of smoothing would be for a jagged roadway, cut through a mountain, to be smoothed out so that the angular turns and transitions appear much more fluid and natural.


Enhancement is also a method that can be employed by the cartographer to illuminate specific elements that aid in map reading. As many of the aforementioned generalizing methods focus on the reduction and omission of detail, the enhancement method concentrates on the addition of detail. Enhancement can be used to show the true character of the feature being represented and is often used by the cartographer to highlight specific details about his or her specific knowledge, that would otherwise be left out. An example includes enhancing the detail about specific river rapids so that the map reader may know the facets of traversing the most difficult sections beforehand. Enhancement can be a valuable tool in aiding the map reader to elements that carry significant weight to the map’s intent.


Displacement can be employed when 2 objects are so close to each other that they would overlap at smaller scales. A common place where this would occur is the cities Brazzaville and Kinshasa on either side of the Congo river in Africa. They are both the capital city of their country and on overview maps they would be displayed with a slightly larger symbol than other cities. Depending on the scale of the map the symbols would overlap. By displacing both of them away from the river (and away from their true location) the symbol overlap can be avoided. Another common case is when a road and a railroad run parallel to each other.

GIS and automated generalization[edit]

As GIS gained prevalence in the late 20th century and the demand for producing maps automatically increased automated generalization became an important issue for National Mapping Agencies (NMAs) and other data providers. Thereby automated generalization is the automated extraction of data (becoming then information) regarding purpose and scale. Different researchers invented conceptual models for automated generalization:

  • Gruenreich model
  • Brassel & Weibel model
  • McMaster & Shea model

Besides these established models, different views on automated generalization have been established: the representation-oriented view and the process-oriented view. The first view focuses on the representation of data on different scales, which is related to the field of Multi-Representation Databases (MRDB). The latter view focuses on the process of generalization.

In the context of creating databases on different scales, additionally it can be distinguished between the ladder and the star-approach. The ladder-approach is a stepwise generalization, in which each derived dataset is based on the other database of the next larger scale. The star-approach is the derived data on all scales is based on a single (large-scale) data base.

Operators in automated generalization[edit]

Automated generalization had always to compete with manual cartographers, therefore the manual generalization process was studied intensively. These studies resulted early in different generalization operators. By now there is no clear classification of operators available and it is doubtful if a comprehensive classification will evolve in future.

The Baltimore Phenomenon[edit]

The Baltimore Phenomenon is the tendency for a city to be omitted from maps due to space constraints while much smaller cities are included on the same map simply because space is available to display them. This phenomenon gets its name from Baltimore, Maryland, which, despite its large population, is commonly omitted on maps of the United States because there is not enough space in the surrounding area of the map. Larger cities surrounding Baltimore take precedence. In contrast, much smaller cities in other geographic locations are included at the same scale because the level of competition for map space may not exist in that particular area. The Baltimore phenomenon can be effectively avoided if the cartographer took a holistic view based on the scaling of geographic space, which has been recently suggested as a universal rule for map generalization.

Competition for Limited Map Space[edit]

During the design process of any map, either created manually or electronically, there will always be a finite amount of map space and an almost infinite amount of information that can be included in that space. Voids in the map will be present in rural areas where population is not very dense. This creates an easier decision-making process for the cartographer since most cities can be shown. There is plenty of space and therefore very little competition for that space by objects or points to be displayed. In contrast, densely populated areas create the constraint of working with a limited spatial area for both points representing cities and the labels of those cities being displayed. Baltimore is the largest city in Maryland, but due to its close proximity to Washington, D.C., and the necessity of labeling the oddly-shaped state of Maryland, it is omitted on maps in favor of other places that fit more easily in the map space available. Baltimore, despite its population, is omitted because of the necessary “competition for limited map space” in that geographic area.

Baltimore in online mapping sites[edit]

The Baltimore Phenomenon does not hold consistently true for every automated mapping site at every scale. Google Maps will display Baltimore once zoomed into the 7th zoom level. At the 6th zoom level, Baltimore is not displayed but cities such as Annapolis, Maryland, and Newton, Iowa, are displayed. Yahoo Maps displays the major roads surrounding Baltimore at the 6th zoom level, but no city label appears until the 7th zoom level. Bing Maps displays Baltimore beginning at the 5th zoom level, but other cities and surrounding details at this level are fairly sparse. OpenStreetMap is similar to Bing in that it displays Baltimore more readily than Google or Yahoo.


Further reading[edit]

  • (1) Jiang, B. Liu, X. & Jia, T. (2013). Scaling of geographic space as a universal rule for map generalization. Annals of the Association of American Geographers, 4, 844-855.
  • Buttenfield, B. P., & McMaster, R. B. (Eds.). (1991). Map Generalization: making rules for knowledge representation. New York: John Wiley and Sons.
  • Campbell, J. (2001). Map Use and Analysis (4th ed.). New York: McGraw Hill.
  • Harrie, L. (2003). Weight-setting and quality assessment in simultaneous graphic generalization. Cartographic Journal, 40(3), 221-233.
  • Krygier, J., & Wood, D. (2005). Making Maps: A Visual Guide To Map Design for GIS (). New York: The Guilford Press.
  • Lonergan, M., & Jones, C. B. (2001). An iterative displacement method for conflict resolution in map generalization. Algorithmica, 30, 287-301.
  • Li, Z. (2006). Algorithmic Foundations of Multi-Scale Spatial Representation. Boca Raton: CRC Press.
  • Mackanaess, W.A., Ruas, A., & Sarjakoski, L.T. (eds)(2007). Generalisation of Geographic Information: Cartographic Modelling and Applications. Oxford: Elsevier.
  • McMaster, R.B. & Shea, K.S. (1992) Generalization in Digital Cartography. Washington, DC: Association of American Geographers.
  • Qi, H., & Zhaloi, L. (2004). Progress in studies on automated generalization of spatial point cluster. IEEE Letters on Remote Sensing, 2994, 2841-2844.
  • Töpfer, F. T., & Pillewizer, K. (1966). The principles of selection. The Cartographic Journal, 3, 10-16.

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