如何实现二元Ripley的K函数？

随附的图像显示了一个森林间隙，赤松表示为圆圈，白松表示为十字架。我有兴趣确定两种松树之间是否存在正关联或负关联（即它们是否在同一地区生长）。我知道R spatstat套件中的Kcross和Kmulti。但是，由于我需要分析50个空白并且比R更熟悉python编程，因此我想找到一种使用ArcGIS和python的迭代方法。我也愿意接受R解决方案。

如何实现二元Ripley的K函数？

在ESRI文档的各个角落进行了大量搜索之后，我得出结论，没有合理的方法在Arcpy / ArcGIS中运行双变量Ripley的K函数。但是，我发现了使用R的解决方案：

# Calculates an estimate of the cross-type L-function for a multitype point pattern.
library(maptools)
library(spatstat)
library(sp)

# Subset certain areas within a points shapefile.  In this case, features are grouped by gap number
gap = 1

# Read the shapefile
sdata = readShapePoints("C:/temp/GapPoints.shp")  #Read the shapefile
data = sdata[sdata$SITE_ID == gap,]  # segregate only those points in the given cluster

# Get the convex hull of the study area measurements
gapdata = readShapePoints("C:/temp/GapAreaPoints_merged.shp")  #Read the shapefile that is used to estimate the study area boundary
data2 = gapdata[gapdata$FinalGap == gap,]  # segregate only those points in the given cluster
whole = coordinates(data2) # get just the coords, excluding other data
win = convexhull.xy(whole) # Convex hull is used to get the study area boundary
plot(win)

# Converting to PPP
points = coordinates(data) # get just the coords, excluding other data
ppp = as.ppp(points, win) # Convert the points into the spatstat format
ppp = setmarks(ppp, data$SPECIES) # Set the marks to species type YB or EH
summary(ppp) # General info about the created ppp object
plot(ppp) # Visually check the points and bounding area

# Plot the cross type L function
# Note that the red and green lines show the effects of different edge corrections
plot(Lcross(ppp,"EH","YB"))

# Use the Lcross function to test the spatial relationship between YB and EH
L <- envelope(ppp, Lcross, nsim = 999, i = "EH", j = "YB")
plot(L)

在ArcToolbox中的空间统计 - 分析模式工具集下，有一个内置的脚本工具，称为多距离空间聚类分析（Ripleys K函数）。如果进入工具的属性并找到“源”选项卡中使用的脚本，则可以阅读该工具的源代码。