Cartographic generalization

From Wikipedia, the free encyclopedia
Jump to: navigation, search

Cartographic generalization, or map generalization, is a method for deriving a smaller-scale map from a larger scale map or map data[1] 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. 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. 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.

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.


There are many cartographic techniques that may fall into the broad category of generalization.[1] Brief descriptions of some of the more common methods follow.


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.[2] 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 or abstracts it as well. Simplification is a technique where the general shapes of features are retained, while eliminating unnecessary detail. Generally, smaller scale maps have more simplified features than larger scale maps. One common line or polygon generalization technique is the Ramer–Douglas–Peucker algorithm.


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 developed from about the late 1960s onward, the need for automatic, algorithmic generalization techniques became clear. Ideally, agencies responsible for collecting and maintaining spatial data should try to keep only one canonical representation of a given feature, at the highest possible level of detail. That way there is only one record to update when that feature changes in the real world.[1] From this large-scale data, it should ideally be possible, through automated generalization, to produce maps and other data products at any scale required. The alternative is to maintain separate databases each at the scale required for a given set of mapping projects, each of which requires attention when something changes in the real world.

. Several broad approaches to generalization were developed around this time:

  • The representation-oriented view focuses on the representation of data on different scales, which is related to the field of Multi-Representation Databases (MRDB).[citation needed]
  • The process-oriented view focuses on the process of generalization.[citation needed]
  • The ladder-approach is a stepwise generalization, in which each derived dataset is based on the other database of the next larger scale.[citation needed]
  • The star-approach is the derived data on all scales is based on a single (large-scale) data base.[citation needed]

The 'Baltimore Phenomenon'[edit]

The Baltimore Phenomenon[citation needed] is the tendency for a city (or other object) to be omitted from maps due to space constraints while 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.


  1. ^ a b c Li, Zhilin (February 2007). "Digital Map Generalization at the Age of the Enlightenment: A review of the First Forty Years". The Cartographic Journal. 44 (1): 80–93. 
  2. ^ Topfer, F; Pillwizer, W (1966). "The Principles of Selection". The Cartographic Journal: 10–16. 

Further reading[edit]

  • 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.

External links[edit]