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Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/73551

Type: Journal article
Title: Generalized index for spatial data sets as a measure of complete spatial randomness
Author: HackettJones, E.
Davies, K.
Binder, B.
Landman, K.
Citation: Physical Review E. (Statistical, Nonlinear, and Soft Matter Physics), 2012; 85(6):1-9
Publisher: American Physical Soc
Issue Date: 2012
ISSN: 1539-3755
1550-2376
Statement of
Responsibility: 
Emily J. Hackett-Jones, Kale J. Davies, Benjamin J. Binder, and Kerry A. Landman
Abstract: Spatial data sets, generated from a wide range of physical systems can be analyzed by counting the number of objects in a set of bins. Previous work has been limited to equal-sized bins, which are inappropriate for some domains (e.g., circular). We consider a nonequal size bin configuration whereby overlapping or nonoverlapping bins cover the domain. A generalized index, defined in terms of a variance between bin counts, is developed to indicate whether or not a spatial data set, generated from exclusion or nonexclusion processes, is at the complete spatial randomness (CSR) state. Limiting values of the index are determined. Using examples, we investigate trends in the generalized index as a function of density and compare the results with those using equal size bins. The smallest bin size must be much larger than the mean size of the objects. We can determine whether a spatial data set is at the CSR state or not by comparing the values of a generalized index for different bin configurations—the values will be approximately the same if the data is at the CSR state, while the values will differ if the data set is not at the CSR state. In general, the generalized index is lower than the limiting value of the index, since objects do not have access to the entire region due to blocking by other objects. These methods are applied to two applications: (i) spatial data sets generated from a cellular automata model of cell aggregation in the enteric nervous system and (ii) a known plant data distribution.
Keywords: Animals; Humans; Models, Statistical; Algorithms; Computer Simulation; Geography, Medical
Rights: ©2012 American Physical Society
RMID: 0020120018
DOI: 10.1103/PhysRevE.85.061908
Appears in Collections:Mathematical Sciences publications
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