point-pattern-analysis
Point pattern analysis (PPA) studies the spatial distribution of points (Boots & Getis, 1988). As outlined above, PPA uses the density, dispersion and homogeneity in our point datasets to assess, quantify and characterise its distribution.
https://jo-wilkin.github.io/GEOG0030/coursebook/analysing-spatial-patterns-iii-point-pattern-analysis.html
有三种描述点聚集程度的方式：
Descriptive statistics
Density-based methods
Distanced-based methods
这里使用第三种方式：基于距离
Estimates Ripley's reduced second moment function K(r) from a point pattern in a window of arbitrary shape.
使用来源于spatstat包的Kest 函数，描述点之间距离不断变化的过程中，点的聚集程度。
X <- runifpoint(50) #### 首先使用点的经纬度构造ppp对象
K <- Kest(X) ### 运行Kest
K <- Kest(cells, correction="isotropic")
plot(K)
# jo-wilkin.github.io/GEOG0030/coursebook/analysing-spatial-patterns-iii-point-pattern-analysis.html
# The Kpois(r) line shows the theoretical value of K for each distance radius (r) under a Poisson assumption of Complete Spatial Randomness.
# When our observed/calculated K values are greater than the expected K, this indicates clustering of points at a given distance band.
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