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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Cartographic generalization | 5/5 | https://en.wikipedia.org/wiki/Cartographic_generalization | reference | science, encyclopedia | 2026-05-05T15:17:39.374723+00:00 | kb-cron |
== 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. 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. This mapping process can be efficiently and effectively achieved by head/tail breaks, 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.
== See also == Cartographic censorship
== References ==
== Further reading == Buttenfield, B. P., & McMaster, R. B. (Eds.). (1991). Map Generalization: making rules for knowledge representation. New York: John Wiley and Sons. Harrie, L. (2003). Weight-setting and quality assessment in simultaneous graphic generalization. Cartographic Journal, 40(3), 221–233. 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. Qi, H., & Zhaloi, L. (2004). Progress in studies on automated generalization of spatial point cluster. IEEE Letters on Remote Sensing, 2994, 2841–2844. Burdziej J., Gawrysiak P. (2012) Using Web Mining for Discovering Spatial Patterns and Hot Spots for Spatial Generalization. In: Chen L., Felfernig A., Liu J., Raś Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science, vol 7661. Springer, Berlin, Heidelberg 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.
== External links == The ICA commission on generalization