How to account for CSR and clustering in uniform spatial data?
geospatial, r, spatial
Solution
Those points are regularly dispersed (sometimes also called hyperdispersed). Although in a colloquial sense they appear uniform, the point process underlying them is not itself uniform: if it were, there would be some chance of point pairs less than one unit apart.
In drawing your attention to that short range deviation from uniformity, Ripley's K is performing exactly as it was designed to!
Problem
I seem to have found a discrepancy in the results of a univariate Ripley's K point pattern analysis (figure 1). To begin, I generated a 1x1 uniform point grid to see if my R script was producing logical results (Figure 2). The study area is 20x40 (Figure2). Given the completely uniform data, I would not expect to see any random or clustered point patterns at any search distance (r). The attached script was used to generate these results. Under these controlled conditions why am I seeing clustering and CSR when there should only be a uniform point pattern? ``` require(spatstat) require(maptools) require(splancs) # Local Variables flower = 0 year = 2013 # Read the shapefile sdata = readShapePoints("C:/temp/sample_final.shp") #Read the shapefile data = sdata[sdata$flow_new == flower,] # subset only flowering plants data2 = data[data$year == year,] # subset flowering plants at year X data.frame(data2) # Check the data # Get the ripras estimate of area based on the study area measurements gapdata = readShapePoints("C:/temp/study_area_boundary.shp") #Read the shapefile whole = coordinates(gapdata) # get just the coords, excluding other data win = convexhull.xy(whole) # Ripras will determine a good bounding polygon for the points (usually a variant of Convex Hull) plot(win) # Converting to PPP points = coordinates(data2) # get just the coords, excluding other data ppp = as.ppp(points, win) # Convert the points into the spatstat format data.check = data.frame(ppp) # Check the format of the ppp data summary(ppp) # General info about the created ppp object plot(ppp) # Visually check the points and bounding area # Now run the ppa L.Env.ppp = envelope(ppp, Lest, nsim = 1000, correction = "best", rank =1) plot(L.Env.ppp, main = "Uniform Test") abline(v=(seq(1:12)), lty="dotted") ``` Figure 1 Results of the analysis Figure 2 The uniform points and the window