如何在统一空间数据中考虑CSR和聚类？

Question

我似乎发现了单变量Ripley的K点模式分析结果的差异（图1）。首先，我生成了1x1统一点网格，以查看我的R脚本是否产生了逻辑结果（图2）。研究区域为20x40（图2）。鉴于数据完全一致，我不希望在任何搜索距离（r）处看到任何随机或聚簇点模式。附加的脚本用于生成这些结果。在这些受控条件下，为什么我只能看到一个统一的点模式时会看到聚类和CSR？

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")

图1

分析结果

图2

统一点和窗口

Answer 1

这些点经常分散（有时也称为超分散）。虽然在口语意义上它们看起来是统一的，但它们之间的点过程本身并不是统一的：如果是，那么点对的可能性会小于一个单位。

为了引起你对那种短距离偏离均匀性的注意，Ripley的K表现完全符合它的设计！