Cartographic generalization

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.

Methods

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

Selection

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.

Simplification

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. The Ramer–Douglas–Peucker algorithm is one of the earliest and still most common techniques for line simplification.

Simplification can be achieved in different ways: by eliminating parts of and giving an area a more familiar shape, by removing unimportant points off a shape - usually performed by a software, by eliminating small features such as islands, or by smoothing "rough" features such as straightening curvy lines to highlight their trend.[3]

Combination

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

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

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

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. this is mostly adopted using the azimuthal projection method.

Exaggeration

Exaggeration is the selection of map symbols that make features appear larger than they really are to make them more visible, recognizable, or higher in the visual hierarchy. For example, in a small-scale map, highways, rivers, and railroads may be drawn as thick lines that would be miles wide if measured according to the scale. Exaggeration often necessitates a subsequent displacement operation because wide lines representing features located near each other will overlap.[4]

Aggregation

Aggregation is the merger of multiple features into a new composite feature. The new feature is of a 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."[5]

GIS and automated generalization

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).
  • The process-oriented view focuses on the process of generalization.
  • 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.

Scaling law

There are far more small geographic features than large ones in the Earth's surface, or far more small things than large ones in maps. This notion of far more small things than large ones is also called spatial heterogeneity, which has been formulated as scaling law.[6] Cartographic generalization or any mapping practices in general is essentially to retain the underlying scaling of numerous smallest, a very few largest, and some in between the smallest and largest.[7] This mapping process can be efficiently and effectively achieved by head/tail breaks,[8][9] a new classification scheme or visualization tool for data with a heavy tailed distribution. Scaling law is likely to replace Töpfer's radical law to be a universal law for various mapping practices. What underlies scaling law is something of paradigm shift from Euclidean geometry to fractal, from non-recursive thinking to recursive thinking.[10]

The 'Baltimore Phenomenon'

The Baltimore Phenomenon 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 owes its name to the city of Baltimore, Maryland, which tends to be omitted on maps due to the presence of larger cities in close proximity within the Mid-Atlantic United States. As larger cities near Baltimore appear on maps, smaller and lesser known cities may also appear at the same scale simply because there is enough space for them on the map.

Although the Baltimore Phenomenon occurs more frequently on automated mapping sites, it does not occur at every scale. Popular mapping sites like Google Maps, Bing Maps, OpenStreetMap, and Yahoo Maps will only begin displaying Baltimore at certain zoom levels: 5th, 6th, 7th, etc.

See also

References

  1. 1 2 3 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. doi:10.1179/000870407x173913.
  2. ↑ Topfer, F; Pillwizer, W (1966). "The Principles of Selection". The Cartographic Journal: 10–16.
  3. ↑ Stern, Boris (2014). "Generalisation of Map Data". Geographic Information Technology Training Alliance: 08–11.
  4. ↑ Stern, Boris (2014). "Generalisation of Map Data". Geographic Information Technology Training Alliance: 12.
  5. ↑ Stern, Boris (2014). "Generalisation of Map Data". Geographic Information Technology Training Alliance: 16.
  6. ↑ Jiang, Bin (2015a). "Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity". GeoJournal. 80 (1): 1–13. arXiv:1401.5889. doi:10.1007/s10708-014-9537-y.
  7. ↑ Jiang, Bin (2015b). "The fractal nature of maps and mapping". International Journal of Geographical Information Science. 29 (1): 159–174. arXiv:1406.5410. doi:10.1080/13658816.2014.953165.
  8. ↑ Jiang, Bin (2015c). "Head/tail breaks for visualization of city structure and dynamics". Cities. 43 (3): 69–77. doi:10.1016/j.cities.2014.11.013.
  9. ↑ Jiang, Bin (2013). "Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution". The Professional Geographer. 65: 482–494. doi:10.1080/00330124.2012.700499.
  10. ↑ Jiang, Bin (2017). "Scaling as a design principle for cartography". Annals of GIS. 23 (1): 67–69. doi:10.1080/19475683.2016.1251491.

Further reading

  • 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.
  • Jiang B. and Yin J. (2014), Ht-index for quantifying the fractal or scaling structure of geographic features, Annals of the Association of American Geographers, 104(3), 530–541.
  • Jiang B., Liu X. and Jia T. (2013), Scaling of geographic space as a universal rule for map generalization, Annals of the Association of American Geographers, 103(4), 844–855.
  • Chrobak T., Szombara S., Kozioł K., Lupa M. (2017), A method for assessing generalized data accuracy with linear object resolution verification, Geocarto International, 32(3), 238–256.
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