Geographical cluster

A geographical cluster is a localized anomaly, usually an excess of something given the distribution or variation of something else.[1] Often it is considered as an incidence rate that is unusual in that there is more of some variable than might be expected. Examples would include: a local excess disease rate, a crime hot spot, areas of high unemployment, accident blackspots, unusually high positive residuals from a model, high concentrations of flora or fauna, areas with high levels of creative activity,[2] physical features or events like earthquake epicenters etc...[citation needed]

Identifying these extreme regions may be useful in that there could be implicit geographical associations with other variables that can be identified and would be of interest. Pattern detection via the identification of such geographical clusters is a very simple and generic form of geographical analysis that has many applications in many different contexts. The emphasis is on localized clustering or patterning because this may well contain the most useful information.[citation needed]

A geographical cluster is different from a high concentration as it is generally second order, involving the factoring in of the distribution of something else.[citation needed]

Geographical cluster detectionEdit

Identifying geographical clusters can be an important stage in a geographical analysis. Mapping the locations of unusual concentrations may help identify causes of these. Some techniques include the Geographical Analysis Machine and Besag and Newell's cluster detection method.[3]

ReferencesEdit

  1. ^ Ian Turton, Stan Openshaw (1998-02-25). "WayBackMachine capture of http://www.ccg.leeds.ac.uk/smart/gam/gam3.html". CCG GAM Web Pages. Archived from the original on October 7, 1999. Retrieved 2011-04-19. {{cite web}}: External link in |title= (help)CS1 maint: unfit URL (link)
  2. ^ Borowiecki, Karol Jan; Dahl, Christian Møller (January 2021). "What makes an artist? The evolution and clustering of creative activity in the US since 1850". Regional Science and Urban Economics. 86.
  3. ^ Fotheringham, A. Stewart; Zhan, F. Benjamin (September 3, 2010). "A Comparison Of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns". Geographical Analysis. 28 (3): 200–218. doi:10.1111/j.1538-4632.1996.tb00931.x.