Indicators of spatial association
From HandWiki
Indicators of spatial association are statistics that evaluate the existence of clusters in the spatial arrangement of a given variable. For instance, if we are studying cancer rates among census tracts in a given city local clusters in the rates mean that there are areas that have higher or lower rates than is to be expected by chance alone; that is, the values occurring are above or below those of a random distribution in space.
Global indicators
Notable global indicators of spatial association include:[1]
- Global Moran's I: The most commonly used measure of global spatial autocorrelation or the overall clustering of the spatial data developed by Patrick Alfred Pierce Moran.[2][3]
- Geary's C (Geary's Contiguity Ratio): A measure of global spatial autocorrelation developed by Roy C. Geary in 1954.[4][5] It is inversely related to Moran's I, but more sensitive to local autocorrelation than Moran's I.
- Getis–Ord G (Getis–Ord global G, Geleral G-Statistic): Introduced by Arthur Getis and J. Keith Ord in 1992 to supplement Moran's I.[6]
Local indicators
Notable local indicators of spatial association (LISA) include:[1]
- Local Moran's I: Derived from Global Moran's I, it was introduced by Luc Anselin in 1995[7] and can be computed using GeoDa.[8]
- Getis–Ord Gi (local Gi): Developed by Getis and Ord based on their global G.
- INDICATE's IN: Originally developed to assess the spatial behaviour of stars,[9] can be computed for any discrete 2+D dataset using python-based INDICATE tool available from GitHub.[10]
See also
References
- ↑ 1.0 1.1 George Grekousis (2020). Spatial Analysis Methods and Practice. Cambridge University Press. p. 210. ISBN 9781108712934.
- ↑ Moran, P. A. P. (1950). "Notes on Continuous Stochastic Phenomena". Biometrika 37 (1): 17–23. doi:10.2307/2332142. PMID 15420245.
- ↑ Li, Hongfei; Calder, Catherine A.; Cressie, Noel (2007). "Beyond Moran's I: Testing for Spatial Dependence Based on the Spatial Autoregressive Model". Geographical Analysis 39 (4): 357–375. doi:10.1111/j.1538-4632.2007.00708.x.
- ↑ Geary, R. C. (1954). "The Contiguity Ratio and Statistical Mapping". The Incorporated Statistician 5 (3): 115–145. doi:10.2307/2986645.
- ↑ J. N. R. Jeffers (1973). "A Basic Subroutine for Geary's Contiguity Ratio". Journal of the Royal Statistical Society, Series D (Wiley) 22 (4): 299–302. doi:10.2307/2986827.
- ↑ Getis, Arthur; Ord, J. Keith (1992). "The analysis of spatial association by use of distance statistics". Geographical Analysis 24 (3): 189–206. doi:10.1111/j.1538-4632.1992.tb00261.x.
- ↑ Anselin, Luc (1995). "Local Indicators of Spatial Association—LISA". Geographical Analysis 27 (2): 93–115. doi:10.1111/j.1538-4632.1995.tb00338.x.
- ↑ Anselin, Luc (2005). "Exploring Spatial Data with GeoDaTM: A Workbook". Spatial Analysis Laboratory. p. 138. https://www.geos.ed.ac.uk/~gisteac/fspat/geodaworkbook.pdf.
- ↑ Buckner, Anne S. M.; Khorrami, Zeinab; Khalaj, Pouria; Lumsden, Stuart L.; Joncour, Isabelle; Moraux, Estelle; Clark, Paul; Oudmaijer, René D. et al. (2019-02-01). "The spatial evolution of young massive clusters. I. A new tool to quantitatively trace stellar clustering". Astronomy and Astrophysics 622: A184. doi:10.1051/0004-6361/201832936. ISSN 0004-6361. Bibcode: 2019A&A...622A.184B. https://ui.adsabs.harvard.edu/abs/2019A&A...622A.184B.
- ↑ abuckner89 (2021-07-22), abuckner89/INDICATE, https://github.com/abuckner89/INDICATE, retrieved 2022-09-14
Further reading
- Bivand, Roger S.; Wong, David W. S. (2018). "Comparing implementations of global and local indicators of spatial association". Test 27 (3): 716–748. doi:10.1007/s11749-018-0599-x. https://link.springer.com/article/10.1007/s11749-018-0599-x.
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