我似乎发现单变量 Ripley 的 K 点模式分析的结果存在差异（图 1）。首先，我生成了一个 1x1 统一的点网格，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）。鉴于完全统一的数据，我不希望在任何搜索距离 (r) 上看到任何随机或聚集的点模式。附加的脚本用于生成这些结果。在这些受控条件下，为什么我应该只看到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
均匀点和窗口