r - 如何解释统一空间数据中的 CSR 和聚类？

Question

我似乎发现单变量 Ripley's 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) 处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，为什么我会看到集群和 CSR 而应该只有统一的点模式？

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

Answer 1

这些点有规律地分散(有时也称为超分散)。尽管在口语中它们看起来很统一，但 它们背后的点过程本身不是统一的:如果是，那么点对之间的距离可能小于一个单位。

在提请您注意与均匀性的短距离偏差时，Ripley's K 的性能与设计完全一样!