Date of Award
Spatial point pattern analysis investigates the localizations of random events in a defined spatial space usually conveyed in the form of images. Spatial distribution of two types of events observed in these images reflects their underlying interactions, which is the focus of co-localization analysis in spatial statistics. Malkusch et al. (Malkusch, et al., 2012) recently proposed the Coordinate-based Co-localization (CBC) method for co-localization analysis. However, the method did not incorporate edge corrections for point proportions and ignored their correlations over nested incremental observational regions. Hence, it yields false positive results for even complete spatial random distributions. In this research, we propose the new K(r) function Coordinate-based Colocalization (KCBC) method to quantify co-localization of two species by utilizing local bivariate Ripley's K and Pearson’s Correlation Coefficient. Simulation studies are conducted to demonstrate the unbiasedness of the new method. An application to real life data was provided to illustrate its applicability.
Komladzei, Stephan C., "Co-localization Analysis of Bivariate Spatial Point Pattern" (2021). University of New Orleans Theses and Dissertations. 2859.