Forest_net

Study on the Spatial Structure of Forests from the Perspective of Network
authors: Peng Chen, Wu Haoran

Funtions

1. net_analyses can do the whole analyses of a ppp class object come from make_mod or case data automatically. All results can be find in a directory, including a map plot, networks plot, all indexs table, and, the res.pdf.
2. to.ppp can transfer your coordinate and crown radius data to a ppp class object.
3. make_mod can construct five spatial models('CSR','Mat','HC','Tho','Str').
4. plot_mod can plot the dot map of ppp class object come from make_mod or case data.
5. make_net can construct three type of networks ('CS', 'CL', 'WCL') from a ppp class object. its result is a 'source-target-(weight)' dataframe, graph_from_data_frame in igraph package can handle this.
6. plot_net can plot the three types of networks of ppp class object come from make_mod or case data.
7. chazhiplot can do a interpolation on network metrics.

Main

source('funcitons.R')
library(spatstat)
library(igraph)
library(dplyr)
library(reshape2)
library(ggpubr)
library(RColorBrewer)

Before you construct models, you should set the parameters:

max_crown <- 5
min_crown <- 2
lambda <- 0.015 #intensity
radius <- 5  #Diffusion radius
HC_R<-4 #HC model
ave_offspring_per_cluster <- 3

Use net_analyses:

dat3<-read.csv('case_data/example_case.csv')
to.ppp(dat3)->case_dat
plot_mod(case_dat)

net_analyse(case_dat,res_dir = './case_data/net_analyse_res/',n_simu = 30)

visualization

final result

we can tell which process major in the construction of spatial structure.

Cite

You can find more on our publication: Rethinking the complexity and uncertainty of spatial networks applied to forest ecology
Please cite:
Wu, H.-R., Peng, C. & Chen, M. Rethinking the complexity and uncertainty of spatial networks applied to forest ecology. Sci Rep 12, 15917 (2022).