, Nikolaos Nagkoulis
[aut] | Title: | Graph Community Detection Methods into Systematic Conservation Planning |
|---|---|
| Description: | An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, 'priorCON' focuses on clusters of features that exhibit strong ecological linkages. The 'priorCON' package is built upon the 'prioritizr' package <doi:10.32614/CRAN.package.prioritizr>, using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems. |
| Authors: | Christos Adam [aut, cre], Aggeliki Doxa [aut]
, Nikolaos Nagkoulis
[aut] |
| Maintainer: | Christos Adam <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.2 |
| Built: | 2024-11-07 13:47:19 UTC |
| Source: | CRAN |
Solve an ordinary prioritizr prioritization problem.
basic_scenario(cost_raster, features_rasters, budget_perc)basic_scenario(cost_raster, features_rasters, budget_perc)
cost_raster |
|
features_rasters |
features |
budget_perc |
|
A basic prioritization problem is created and solved using prioritizr package. The solver used for solving the problems is the best available on the computer, following the solver hierarchy of prioritizr. By default, the highs package using the HiGHS solver is downloaded during package installation.
A list containing input for get_outputs.
Hanson, Jeffrey O, Richard Schuster, Nina Morrell, Matthew Strimas-Mackey, Brandon P M Edwards, Matthew E Watts, Peter Arcese, Joseph Bennett, and Hugh P Possingham. 2024. prioritizr: Systematic Conservation Prioritization in R. https://prioritizr.net.
Huangfu, Qi, and JA Julian Hall. 2018. Parallelizing the Dual Revised Simplex Method. Mathematical Programming Computation 10 (1): 119–42. doi:10.1007/s12532-017-0130-5
preprocess_graphs,
get_metrics
# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve an ordinary prioritizr prioritization problem basic_solution <- basic_scenario(cost_raster=cost_raster, features_rasters=features_rasters, budget_perc=0.1)# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve an ordinary prioritizr prioritization problem basic_solution <- basic_scenario(cost_raster=cost_raster, features_rasters=features_rasters, budget_perc=0.1)
Solve a prioritizr prioritization problem, by incorporating graph connectivity of the features.
connectivity_scenario(cost_raster, features_rasters = NULL, budget_perc, pre_graphs)connectivity_scenario(cost_raster, features_rasters = NULL, budget_perc, pre_graphs)
cost_raster |
|
features_rasters |
features |
budget_perc |
|
pre_graphs |
output of get_metrics function. |
A connectivity prioritization problem is created and solved using prioritizr package. The solver used for solving the problems is the best available on the computer, following the solver hierarchy of prioritizr. By default, the highs package using the HiGHS solver is downloaded during package installation.
A list containing input for get_outputs.
Hanson, Jeffrey O, Richard Schuster, Nina Morrell, Matthew Strimas-Mackey, Brandon P M Edwards, Matthew E Watts, Peter Arcese, Joseph Bennett, and Hugh P Possingham. 2024. prioritizr: Systematic Conservation Prioritization in R. https://prioritizr.net.
Huangfu, Qi, and JA Julian Hall. 2018. Parallelizing the Dual Revised Simplex Method. Mathematical Programming Computation 10 (1): 119–42. doi:10.1007/s12532-017-0130-5
preprocess_graphs,
get_metrics
# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core") cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve a prioritizr prioritization problem, # by incorporating graph connectivity of the features connectivity_solution <- connectivity_scenario(cost_raster=cost_raster, features_rasters=features_rasters, budget_perc=0.1, pre_graphs=pre_graphs)# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core") cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve a prioritizr prioritization problem, # by incorporating graph connectivity of the features connectivity_solution <- connectivity_scenario(cost_raster=cost_raster, features_rasters=features_rasters, budget_perc=0.1, pre_graphs=pre_graphs)
Cost raster example.
get_cost_raster()get_cost_raster()
A cost SpatRaster object to use for examples.
library(tmap) ## Import features_raster cost_raster <- get_cost_raster() ## Plot with tmap tm_shape(cost_raster) + tm_raster(title = "cost")library(tmap) ## Import features_raster cost_raster <- get_cost_raster() ## Plot with tmap tm_shape(cost_raster) + tm_raster(title = "cost")
Features raster example.
get_features_raster()get_features_raster()
A features SpatRaster object to use for examples.
library(tmap) ## Import features_raster features_raster <- get_features_raster() ## Plot with tmap tm_shape(features_raster) + tm_raster(title = "f1")library(tmap) ## Import features_raster features_raster <- get_features_raster() ## Plot with tmap tm_shape(features_raster) + tm_raster(title = "f1")
Detect graph communities for each biodiversity feature.
get_metrics(connect_mat, which_community = "s_core")get_metrics(connect_mat, which_community = "s_core")
connect_mat |
a |
which_community |
|
Function get_metrics is used to calculate graph metrics values. The edge lists created from the previous step, or inserted directly from the user are used in this step to create graphs. The directed graphs are transformed to undirected. The function is based on the igraph package which is used to create clusters using Louvain and Walktrap and calculate the following metrics: Eigenvector Centrality, Betweenness Centrality and Degree and PageRank. S-core is calculated using the package brainGraph.
connect_mat is either the output of preprocess_graphs or
a custom edge list data.frame object, with the following
columns:
feature: feature name.
from.X: longitude of the origin (source).
from.Y: latitude of the origin (source).
to.X: longitude of the destination (target).
to.Y: latitude of the destination (target).
weight: connection weight.
A list containing input for basic_scenario or connectivity_scenario.
Csárdi, Gábor, and Tamás Nepusz. 2006. The Igraph Software Package for Complex Network Research. InterJournal Complex Systems: 1695. https://igraph.org.
Csárdi, Gábor, Tamás Nepusz, Vincent Traag, Szabolcs Horvát, Fabio Zanini, Daniel Noom, and Kirill Müller. 2024. igraph: Network Analysis and Visualization in R. doi:10.5281/zenodo.7682609.
Watson, Christopher G. 2024. brainGraph: Graph Theory Analysis of Brain MRI Data. doi:10.32614/CRAN.package.brainGraph.
preprocess_graphs,
get_metrics
# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core")# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core")
Evaluate outputs from basic_scenario or connectivity_scenario functions for a selected feature.
get_outputs(solution, feature, pre_graphs, loose = FALSE, patch = FALSE)get_outputs(solution, feature, pre_graphs, loose = FALSE, patch = FALSE)
solution |
output from basic_scenario or connectivity_scenario functions. |
feature |
|
pre_graphs |
output of get_metrics function. |
loose |
use loose or strict graph community connectivity definition. See more in details. |
patch |
|
Loose graph connectivity indicates the case where two protected nodes (cells)
can be considered connected, even if the between them cells are not protected
(thus not included in the solution), whereas strict connectivity indicates the
case where two protected cells can be considered connected, only if they are
cells between them that are also protected. The default is loose = FALSE,
indicating the use of the strict connectivity definition.
A list containing the following items:
tmap: tmap plot of the solution including connections.
solution: terra SpatRaster object representing the
prioritization solution.
connections: sf LINESTRING object representing the preserved
connections of the solution.
connectivity_table: data.frame containing all feature names at the
first column, the relative held percentages at the second column and the
percentage of connections held at the third column.
Hijmans, Robert J. 2024. terra: Spatial Data Analysis. doi:10.32614/CRAN.package.terra.
Pebesma, Edzer. 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal 10 (1): 439–46. doi:10.32614/RJ-2018-009.
Pebesma, Edzer, and Roger Bivand. 2023. Spatial Data Science: With applications in R. Chapman and Hall/CRC. doi:10.1201/9780429459016
basic_scenario,
connectivity_scenario
# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core") cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve a prioritizr prioritization problem, by incorporating graph connectivity of the features connectivity_solution <- connectivity_scenario( cost_raster = cost_raster, features_rasters = features_rasters, budget_perc = 0.1, pre_graphs = pre_graphs ) # Get outputs from connectivity_scenario function for feature "f1" connectivity_outputs <- get_outputs(solution = connectivity_solution, feature = "f1", pre_graphs = pre_graphs) # Plot tmap connectivity_outputs$tmap # Print summary of features and connections held percentages for connectivity scenario print(connectivity_outputs$connectivity_table) ## feature relative_held connections(%) ## 1 f1 0.1637209 0.3339886# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") # Set seed for reproducibility set.seed(42) # Detect graph communities using the s-core algorithm pre_graphs <- get_metrics(combined_edge_list, which_community = "s_core") cost_raster <- get_cost_raster() features_rasters <- get_features_raster() # Solve a prioritizr prioritization problem, by incorporating graph connectivity of the features connectivity_solution <- connectivity_scenario( cost_raster = cost_raster, features_rasters = features_rasters, budget_perc = 0.1, pre_graphs = pre_graphs ) # Get outputs from connectivity_scenario function for feature "f1" connectivity_outputs <- get_outputs(solution = connectivity_solution, feature = "f1", pre_graphs = pre_graphs) # Plot tmap connectivity_outputs$tmap # Print summary of features and connections held percentages for connectivity scenario print(connectivity_outputs$connectivity_table) ## feature relative_held connections(%) ## 1 f1 0.1637209 0.3339886
Read connectivity data from multiple sub-folders.
preprocess_graphs(path, ...)preprocess_graphs(path, ...)
path |
a path of the folder where sub-folders containing txt or csv files are contained. Each sub-folder has the name of the corresponding connectivity data. In case that a connectivity folder corresponds to a specific biodiversity feature, it should be named as the corresponding feature. |
... |
additional arguments passed to |
This is an auxiliary function for creating an edge list data.frame
object from multiple files, like the ones provided from softwares estimating
Lagrangian models.
Function preprocess_graphs takes as input a list of .txt/.csv objects. Each object represents the connections between a node and all the other nodes. For the model to read the data, it is necessary to have all the .txt/.csv objects in one folder. There are two ways to incorporate connectivity data, based on their linkage to features:
Case 1: the connectivity data correspond to specific biodiversity features. If a biodiversity feature has its own connectivity dataset then the file including the edge lists needs to have the same name as the corresponding feature. For example, consider having 5 species (f1, f2, f3, f4, f5) and 5 connectivity datasets. Then the connectivity datasets need to be in separate folders named: f1,f2,f3,f4,f5 and the algorithm will understand that they correspond to the species.
Case 2: the connectivity dataset represents a spatial pattern that is not directly connected with a specific biodiversity feature. Then the connectivity data need to be included in a separate folder named in a different way than the species. For example consider having 5 species (f1,f2,f3,f4,f5) and 1 connectivity dataset. This dataset can be included in a separate folder (e.g. "Langragian_con").
A typical Lagrangian output is a set of files representing the likelihood of a point moving from an origin (source) to a destination (target). This can be represented using a list of .txt/.csv files (as many as the origin points) including information for the destination probability. The .txt/.csv files need to be named in an increasing order. The name of the files need to correspond to the numbering of the points, in order for the algorithm to match the coordinates with the points.
an edge list data.frame object, with the following columns:
feature: feature name.
from.X: longitude of the origin (source).
from.Y: latitude of the origin (source).
to.X: longitude of the destination (target).
to.Y: latitude of the destination (target).
weight: connection weight.
preprocess_graphs,
get_metrics
# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") head(combined_edge_list) ## feature from.X from.Y to.X to.Y weight ## 1 f1 22.62309 40.30342 22.62309 40.30342 0.000 ## 2 f1 22.62309 40.30342 22.62309 40.39144 0.000 ## 3 f1 22.62309 40.30342 22.62309 40.41341 0.000 ## 4 f1 22.62309 40.30342 22.62309 40.43537 0.005 ## 5 f1 22.62309 40.30342 22.62309 40.45731 0.000 ## 6 f1 22.62309 40.30342 22.65266 40.30342 0.000# Read connectivity files from folder and combine them combined_edge_list <- preprocess_graphs(system.file("external", package="priorCON"), header = FALSE, sep =";") head(combined_edge_list) ## feature from.X from.Y to.X to.Y weight ## 1 f1 22.62309 40.30342 22.62309 40.30342 0.000 ## 2 f1 22.62309 40.30342 22.62309 40.39144 0.000 ## 3 f1 22.62309 40.30342 22.62309 40.41341 0.000 ## 4 f1 22.62309 40.30342 22.62309 40.43537 0.005 ## 5 f1 22.62309 40.30342 22.62309 40.45731 0.000 ## 6 f1 22.62309 40.30342 22.65266 40.30342 0.000