Introduction
Systematic conservation planning requires making trade-offs (Margules & Pressey 2000; Vane-Wright et al. 1991). Since different criteria may conflict with one another – or not align perfectly – prioritizations need to make trade-offs between different criteria (Klein et al. 2013). Although some criteria can easily be accounted for by using locked constraints or representation targets (e.g., Dorji et al. 2020; Hermoso et al. 2018), this is not always the case (e.g., Beger et al. 2010). For example, prioritizations often need to balance overall cost with the overall level spatial fragmentation among reserves (Hermoso et al. 2011; Stewart & Possingham 2005). Additionally, prioritizations often need to balance the overall level of connectivity among reserves against other criteria (Hermoso et al. 2012). Since the best trade-off depends on a range of factors – such as available budgets, species’ connectivity requirements, and management capacity – finding the best balance can be challenging.
The prioritizr R package provides multi-objective optimization methods to help identify the best trade-offs between different criteria. To achieve this, a conservation planning problem can be formulated with a primary objective (e.g., add_min_set_objective()) and penalties (e.g., add_boundary_penalties()) that relate to such criteria. When building the problem, the nature of the trade-offs can be specified using certain parameters (e.g., the penalty parameter of the add_boundary_penalties() function). To identify a prioritization that finds the best balance between different criteria, the trade-off parameters can be tuned using a calibration analysis. These analyses – in the context of systematic conservation planning – typically involve generating a set of candidate prioritizations based on different parameters, measuring their performance according to each of the criteria, and then selecting a prioritization (or set of prioritizations) based on how well they achieve the criteria (Hermoso et al. 2011, 2012; Stewart & Possingham 2005). For example, the Marxan decision support tool has a range of parameters (e.g., species penalty factors, boundary length modifier) that are calibrated to balance cost, species’ representation, and spatial fragmentation (Ardron et al. 2010).
The aim of this tutorial is to provide guidance on calibrating trade-offs when using the prioritizr R package. Here we will explore a couple of different approaches for generating candidate prioritizations, and methods for finding the best balance between different criteria. Specifically, we will try to generate prioritizations that strike the best balance between total cost and spatial fragmentation (measured as total boundary length). As such, the code used in this vignette will be directly applicable when performing a boundary length calibration analysis.
Data
Let’s load the packages and dataset used in this tutorial. Since this tutorial uses the prioritizrdata R package along with several other R packages (see below), please ensure that they are all installed. This particular dataset comprises two objects: tas_pu and tas_features. Although we will briefly describe this dataset below, please refer ?prioritizrdata::tas_data for further details.
## Simple feature collection with 1130 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 298809.6 ymin: 5167775 xmax: 613818.8 ymax: 5502544
## Projected CRS: WGS 84 / UTM zone 55S
## # A tibble: 1,130 × 5
## id cost locked_in locked_out geom
## <int> <dbl> <lgl> <lgl> <MULTIPOLYGON [m]>
## 1 1 60.2 FALSE TRUE (((328497 5497704, 326783.8 5500050, 326775…
## 2 2 19.9 FALSE FALSE (((307121.6 5490487, 305344.4 5492917, 3053…
## 3 3 59.7 FALSE TRUE (((321726.1 5492382, 320111 5494593, 320127…
## 4 4 32.4 FALSE FALSE (((304314.5 5494324, 304342.2 5494287, 3043…
## 5 5 26.2 FALSE FALSE (((314958.5 5487057, 312336 5490646, 312339…
## 6 6 51.3 FALSE FALSE (((327904.3 5491218, 326594.6 5493012, 3284…
## 7 7 32.3 FALSE FALSE (((308194.1 5481729, 306601.2 5483908, 3066…
## 8 8 38.4 FALSE FALSE (((322792.7 5483624, 319965.3 5487497, 3199…
## 9 9 3.55 FALSE FALSE (((334896.6 5490731, 335610.4 5492490, 3357…
## 10 10 1.83 FALSE FALSE (((356377.1 5487952, 353903.1 5487635, 3538…
## # ℹ 1,120 more rows
## class : SpatRaster
## dimensions : 398, 359, 33 (nrow, ncol, nlyr)
## resolution : 1000, 1000 (x, y)
## extent : 288801.7, 647801.7, 5142976, 5540976 (xmin, xmax, ymin, ymax)
## coord. ref. : WGS 84 / UTM zone 55S (EPSG:32755)
## source : tas_features.tif
## names : Banks~lands, Bould~marks, Calli~lands, Cool ~orest, Eucal~hyll), Eucal~torey, ...
## min values : 0, 0, 0, 0, 0, 0, ...
## max values : 1, 1, 1, 1, 1, 1, ...
The tas_pu object contains planning units represented as spatial polygons (i.e., converted to a sf::st_sf() object). This object has three columns that denote the following information for each planning unit: a unique identifier (id), unimproved land value (cost), and current conservation status (locked_in). Specifically, the conservation status column indicates if at least half the area planning unit is covered by existing protected areas (denoted by a value of 1) or not (denoted by a value of zero).
The tas_features object describes the spatial distribution of different vegetation communities (using presence/absence data). We will use the vegetation communities as the biodiversity features for the prioritization.
We can use this dataset to generate a prioritization. Specifically, we will use the minimum set objective so that the optimization process minimizes total cost. We will add representation targets to ensure that prioritizations cover 17% of each vegetation community. Additionally, we will add constraints to ensure that planning units covered by existing protected areas are selected (i.e., locked in). Finally, we will specify that the conservation planning exercise involves binary decisions (i.e., selecting or not selecting planning units for protected area establishment).
## A conservation problem (<ConservationProblem>)
## ├•data
## │├•features: "Banksia woodlands" , … (33 total)
## │└•planning units:
## │ ├•data: <sftbl_dftbldata.frame> (1130 total)
## │ ├•costs: continuous values (between 0.1925 and 61.9273)
## │ ├•extent: 298809.5764, 5167774.5993, 613818.7743, 5502543.7119 (xmin, ymin, xmax, ymax)
## │ └•CRS: WGS 84 / UTM zone 55S (projected)
## ├•formulation
## │├•objective: minimum set objective
## │├•penalties: none specified
## │├•targets: relative targets (between 0.17 and 0.17)
## │├•constraints:
## ││└•1: locked in constraints (257 planning units)
## │└•decisions: binary decision
## └•optimization
## ├•portfolio: default portfolio
## └•solver: gurobi solver (`gap` = 0.1, `time_limit` = 2147483647, `first_feasible` = FALSE, …)
## # ℹ Use `summary(...)` to see complete formulation.
## Set parameter Username
## Set parameter TimeLimit to value 2147483647
## Set parameter MIPGap to value 0.1
## Set parameter Presolve to value 2
## Set parameter Threads to value 1
## Academic license - for non-commercial use only - expires 2025-06-05
## Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (linux64 - "Ubuntu 20.04.6 LTS")
##
## CPU model: AMD Ryzen Threadripper 3960X 24-Core Processor, instruction set [SSE2|AVX|AVX2]
## Thread count: 24 physical cores, 48 logical processors, using up to 1 threads
##
## Optimize a model with 33 rows, 1130 columns and 8157 nonzeros
## Model fingerprint: 0xd205b612
## Variable types: 0 continuous, 1130 integer (1130 binary)
## Coefficient statistics:
## Matrix range [2e-06, 6e+01]
## Objective range [2e-01, 6e+01]
## Bounds range [1e+00, 1e+00]
## RHS range [2e-01, 2e+03]
## Found heuristic solution: objective 10337.581214
## Presolve removed 19 rows and 354 columns
## Presolve time: 0.02s
## Presolved: 14 rows, 776 columns, 2296 nonzeros
## Variable types: 0 continuous, 776 integer (776 binary)
## Found heuristic solution: objective 9018.7830067
##
## Explored 0 nodes (0 simplex iterations) in 0.05 seconds (0.01 work units)
## Thread count was 1 (of 48 available processors)
##
## Solution count 2: 9018.78 10337.6
##
## Optimal solution found (tolerance 1.00e-01)
## Best objective 9.018783006750e+03, best bound 8.496084047117e+03, gap 5.7957%
## Simple feature collection with 1130 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 298809.6 ymin: 5167775 xmax: 613818.8 ymax: 5502544
## Projected CRS: WGS 84 / UTM zone 55S
## # A tibble: 1,130 × 6
## id cost locked_in locked_out solution_1 geom
## <int> <dbl> <lgl> <lgl> <dbl> <MULTIPOLYGON [m]>
## 1 1 60.2 FALSE TRUE 0 (((328497 5497704, 326783.8 5500…
## 2 2 19.9 FALSE FALSE 0 (((307121.6 5490487, 305344.4 54…
## 3 3 59.7 FALSE TRUE 0 (((321726.1 5492382, 320111 5494…
## 4 4 32.4 FALSE FALSE 0 (((304314.5 5494324, 304342.2 54…
## 5 5 26.2 FALSE FALSE 0 (((314958.5 5487057, 312336 5490…
## 6 6 51.3 FALSE FALSE 0 (((327904.3 5491218, 326594.6 54…
## 7 7 32.3 FALSE FALSE 0 (((308194.1 5481729, 306601.2 54…
## 8 8 38.4 FALSE FALSE 0 (((322792.7 5483624, 319965.3 54…
## 9 9 3.55 FALSE FALSE 0 (((334896.6 5490731, 335610.4 54…
## 10 10 1.83 FALSE FALSE 0 (((356377.1 5487952, 353903.1 54…
## # ℹ 1,120 more rows
We can see that the priority areas identified by the prioritization are scattered across the study area (shown in green). Indeed, none of the priority areas are connected to existing protected areas (shown in purple), and very of them are connected with other priority areas. As such, the prioritization has a high level of spatial fragmentation. If it is important to avoid such levels of spatial fragmentation, then we will need to explicitly account for spatial fragmentation in the optimization process.
Generating candidate prioritizations
Here we will start the calibration analysis by generating a set of candidate prioritizations. Specifically, these prioritizations will be generated using different parameters to specify different trade-offs between the different criteria. Since this tutorial involves navigating trade-offs between the overall cost of a prioritization and the level of spatial fragmentation associated with a prioritization (as measured by total boundary length), we will generate prioritizations using different parameters related to these criteria. We will examine two approaches for generating candidate prioritizations based on multi-objective optimization procedures. Although we’ll be examining both approaches in this tutorial, you would normally only use one of these approaches when conducting your own analysis
Blended approach
The blended approach for multi-objective optimization involves combining separate criteria (e.g., total cost and total boundary length) into a single joint criterion. To achieve this, a trade-off (or scaling) parameter is used to specify the relative importance of each criterion. This approach is the default approach provided by the prioritizr R package. Specifically, each of the functions for adding a penalty to a problem formulation (e.g., add_boundary_penalties()) contains a parameter to control the relative importance of the penalties (i.e., the penalty parameter). For example, when using the add_boundary_penalties() function, setting a high penalty value will indicate that it is important to reduce the overall exposed boundary (perimeter) of the prioritization.
The main challenge with the blended approach is identifying a range of suitable penalty values to generate candidate prioritizations. If we set a penalty value that is too low, then the penalties will have no effect (e.g., boundary length penalties would have no effect on the prioritization). If we set a penalty value too high, then the prioritization will effectively ignore the primary objective. In such cases, the prioritization will be overly spatially clustered – because the planning unit cost values have no effect — and contain a single reserve. Thus we need to find a suitable range of penalty values before we can generate a set of candidate prioritizations.
We can find a suitable range of penalty values by generating a set of preliminary prioritizations. These preliminary prioritizations will be based on different penalty values – similar to the process for generating the candidate prioritizations – but solved using customized settings that sacrifice optimality for fast run times (see below for details). This is especially important because specifying a penalty value that is too high will cause the optimization process to take a very long time to generate a solutions (due to the numerical scaling issues mentioned previously). To find a suitable range of penalty values, we need to identify an upper limit for the penalty value (i.e., the highest penalty value that result in a prioritization containing a single reserve). Let’s create some preliminary penalty to identify this upper limit. Please note that you might need to adjust the prelim_upper value to find the upper limit when analyzing different datasets.
## [1] 0.00001 0.00007 0.00049 0.00340 0.02371 0.16548 1.15478 8.05842
## [9] 56.23413
Next, let’s use the preliminary penalty values to generate preliminary prioritizations. As mentioned earlier, we will generate these preliminary prioritizations using customized settings to reduce runtime. Specifically, we will set a time limit of 10 minutes per run, and relax the optimality gap to 20%. Although we would not normally use such settings – because the resulting prioritizations are not guaranteed to be near-optimal (the default gap is 10%) – this is fine because our goal here is to tune the preliminary penalty values. Indeed, none of these preliminary prioritizations will be considered as candidate prioritizations. Please note that you might need to set a higher time limit, or relax the optimality gap even further (e.g., 40%) when analyzing larger datasets.
After generating the preliminary prioritizations, let’s create some maps to visualize them. In particular, we want to understand how different penalty values influence the spatial fragmentation of the prioritizations.
We can see that as the penalty value used to generate the prioritizations increases, the spatial fragmentation of the prioritizations decreases. In particular, we can see that a penalty value of 56.23413 results in a single reserve – meaning this is our best guess of the upper limit. Using this penalty value as an upper limit, we will now generate a second series of prioritizations that will be the candidate prioritizations. Critically, these candidate prioritizations will not be generated using with time limit and be generated using a more suitable gap (i.e., default gap of 10%).
# define a new set of penalty values
penalty <- round(10^seq(-5, log10(prelim_penalty[9]), length.out = 9), 5)
# generate prioritizations based on each penalty
blended_results <- lapply(penalty, function(x) {
## generate solution
s <-
p0 %>%
add_boundary_penalties(penalty = x, data = tas_bd) %>%
solve()
## return data frame with solution
s <- data.frame(s = s$solution_1)
names(s) <- with_options(list(scipen = 30), paste0("penalty_", x))
s
})
# format results as a single spatial object
blended_results <- cbind(tas_pu, do.call(bind_cols, blended_results))
# plot maps of prioritizations
plot(
x =
blended_results %>%
dplyr::select(starts_with("penalty_")) %>%
mutate_if(is.numeric, function(x) {
case_when(
blended_results$locked_in > 0.5 ~ "locked in",
x > 0.5 ~ "priority",
TRUE ~ "other"
)
}),
pal = c("purple", "grey90", "darkgreen")
)
We now have a set of candidate prioritizations generated using the blended approach. The main advantages of this approach is that it is similar calibration analyses used by other decision support tools for conservation (i.e., Marxan) and it is relatively straightforward to implement. However, this approach also has a key disadvantage. Because the penalty parameter is a unitless trade-off parameter – meaning that we can’t leverage existing knowledge to specify a suitable range of penalty values – we first have to conduct a preliminary analysis to identify an upper limit. Although finding an upper limit was fairly simple for the example dataset, it can be difficult to find for more realistic data. In the next section, we will show how to generate a set of candidate prioritizations using the hierarchical approach – which does not have this disadvantage.
Hierarchical approach
The hierarchical approach for multi-objective optimization involves generating a series of incremental prioritizations – using a different objective at each increment to refine the previous solution – until the final solution achieves all of the objectives. The advantage with this approach is that we can specify trade-off parameters for each objective based on a percentage from optimality. This means that we can leverage our own knowledge – or that of decision maker – to generate a range of suitable trade-off parameters. As such, this approach does not require us to generate a series of preliminary prioritizations.
This approach is slightly more complicated to implement within the prioritizr R package then the blended approach. To start off, we generate an initial prioritization based on a problem formulation that does not consider any penalties. Critically, we will generate this prioritization by solving the problem to optimality (using the gap parameter of the add_default_solver() function).
# define a problem without boundary penalties
p1 <-
problem(tas_pu, tas_features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.17) %>%
add_locked_in_constraints("locked_in") %>%
add_binary_decisions() %>%
add_default_solver(gap = 0)
# solve problem
s1 <- solve(p1)
# add column for making a map of the prioritization
s1$map_1 <- case_when(
s1$locked_in > 0.5 ~ "locked in",
s1$solution_1 > 0.5 ~ "priority",
TRUE ~ "other"
)
# plot map of prioritization
plot(
s1[, "map_1"], pal = c("purple", "grey90", "darkgreen"),
main = NULL, key.pos = 1
)
Next, we will calculate the total cost of the initial prioritization.
## [1] 354.3258
Now we will calculate a series of cost thresholds. These cost thresholds will be calculated by inflating the cost of the initial prioritization by a range of percentage values. Since these values are percentages – and not unitless values unlike those used in the blended approach – we can use domain knowledge to specify a suitable range of cost thresholds. For this tutorial, let’s assume that it would be impractical – per our domain knowledge – to expend more than four times the total cost of the initial prioritization to reduce spatial fragmentation.
## [1] 355 532 709 886 1063 1241 1418 1595 1772
After generating the cost thresholds, we can use them to generate prioritizations. Specifically, we will generate prioritizations that aim to minimize total boundary length as much as possible – ignoring the total cost of the prioritizations – whilst ensuring that the total cost of the prioritization does not exceed a given cost threshold and the other considerations (e.g., locked in constraints). To achieve this, we create a new column in the tas_pu object that contains only zero values (called zeros) and use this new column to specify the cost data for the prioritizations. Although we normally recommend against cost data that contain zero values – because planning units with zero costs are often selected in prioritizations even if they are not needed – here we use zero cost values so that the prioritization will focus exclusively on spatial fragmentation. Additionally, when it comes to generating the prioritization, we will add linear constraints to ensure that the total cost of the prioritization does not exceed a given cost threshold (using the add_linear_constraints() function).
## Warning: `x[, cost_column]` has only zero values.
# generate prioritizations based on each cost threshold
## note that the prioritizations are solved to within 10% of optimality
## (the default gap) because the gap is not specified
hierarchical_results <- lapply(threshold, function(x) {
## generate solution by adding a constraint based on the threshold and
## using the "real" cost values (i.e., not zeros)
s <-
p2 %>%
add_linear_constraints(threshold = x, sense = "<=", data = "cost") %>%
solve()
## return data frame with solution
s <- data.frame(s = s$solution_1)
names(s) <- paste0("threshold_", x)
s
})
# format results as a single spatial object
hierarchical_results <- cbind(tas_pu, do.call(bind_cols, hierarchical_results))
# plot maps of prioritizations
plot(
x =
hierarchical_results %>%
dplyr::select(starts_with("threshold_")) %>%
mutate_if(is.numeric, function(x) {
case_when(
hierarchical_results$locked_in > 0.5 ~ "locked in",
x > 0.5 ~ "priority",
TRUE ~ "other"
)
}),
pal = c("purple", "grey90", "darkgreen")
)
We now have a set of candidate prioritizations generated using the hierarchical approach. This approach can be much faster than the blended approach because it does not require generating a set of prioritizations to identify an upper limit for the penalty trade-off parameter. After generating a set of candidate prioritizations, we can then calculate performance metrics to compare the prioritizations.
Selecting a prioritization
Now we need to decide on which candidate prioritization achieves the best trade-off. There are a range of qualitative and quantitative methods that are available to select a candidate prioritization (Ardron et al. 2010). Here we will consider three different methods. Since some of these methods use a set of candidate prioritizations, we will use the candidate prioritizations using the hierarchical approach for these methods. To keep track of the prioritizations selected by different methods, let’s create a results_data table.
## # A tibble: 9 × 6
## total_cost total_boundary_length value name label method
## <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 355. 2843602. 355 threshold_355 Threshold = 355 none
## 2 526. 2288083. 532 threshold_532 Threshold = 532 none
## 3 708. 2118755. 709 threshold_709 Threshold = 709 none
## 4 885. 2031741. 886 threshold_886 Threshold = 886 none
## 5 1059. 1930579. 1063 threshold_1063 Threshold = 1063 none
## 6 1235. 1893317. 1241 threshold_1241 Threshold = 1241 none
## 7 1415. 1816536. 1418 threshold_1418 Threshold = 1418 none
## 8 1585. 1773003. 1595 threshold_1595 Threshold = 1595 none
## 9 1769. 1733304. 1772 threshold_1772 Threshold = 1772 none
Next, let’s examine some different methods for selecting prioritizations.
Visual method
One qualitative method involves plotting the relationship between the different criteria, and using the plot to visually select a candidate prioritization. This visual method is often used to help calibrate trade-offs among prioritizations generated using the Marxan decision support tool (e.g., Hermoso et al. 2011; Stewart & Possingham 2005). So, let’s create a plot to select a prioritization.
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
We can see that there is a clear relationship between total cost and total boundary length. It would seem that in order to achieve a lower total boundary length – and thus lower spatial fragmentation – the prioritization must have a greater cost. Although we might expect the results to show a smoother curve – in other words, only Pareto dominant solutions – this result is expected because we generated candidate prioritizations using the default optimality gap of 10%. Typically, the visual method involves selecting a prioritization near the elbow of the plot. So, let’s select the prioritization generated using a threshold value of 709. To keep track of the prioritizations selected based on different methods, let’s create a method column in the result_data table.
Next, let’s consider a quantitative approach.
TOPSIS method
Multiple-criteria decision analysis is a discipline that uses analytical methods to evaluate trade-offs between multiple criteria (MCDA; reviewed in Greene et al. 2011). Although this discipline contains many different methods, here we will use the the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method (Hwang & Yoon 1981). This method requires (i) data describing the performance of each prioritization according to the different criteria, (ii) weights to encode the relative importance of each criteria, and (iii) details on whether each criteria should ideally be minimized or maximized. Let’s run the analysis, assuming that we want equal weighting for total cost and total boundary length.
## alt.row score rank
## 1 1 0.6960070 3
## 2 2 0.7839360 1
## 3 3 0.7330384 2
## 4 4 0.6415080 4
## 5 5 0.5507468 5
## 6 6 0.4592623 6
## 7 7 0.3879434 7
## 8 8 0.3364144 8
## 9 9 0.3039930 9
The candidate prioritization with the greatest TOPSIS score is considered to represent the best trade-off between total cost and total boundary length. So, based on this method, we would select the prioritization generated using a threshold value of 532. Let’s update the result_data with this information.
Next, let’s consider another quantitative method.
Cohon et al. (1979) method
This method is based on an algorithm that was originally developed by Cohon et al. (Cohon et al. 1979), and was later adapted for use in systematic conservation planning (Fischer & Church 2005). Specifically, it involves generating two ideal prioritizations – with each prioritization representing the ideal prioritization for each criteria – and then using the performance metrics calculated for these prioritizations to automatically derive a trade-off penalty value (Ardron et al. 2010; Cohon et al. 1979). Thus, unlike the two other methods, this method does not require a set of candidate prioritizations. As such, this method can be used to find a prioritization that meets multiple criteria in a much shorter period of time than the other methods. To implement this method, we first need to generate the ideal prioritizations (note that we specify an gap of 0% to ensure optimality).
## Warning: `x[, cost_column]` has only zero values.
Next, let’s calculate the performance metrics for these prioritizations.
# generate problem formulation with costs and boundary penalties for
# calculating performance metrics
p5 <-
problem(tas_pu, tas_features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_boundary_penalties(penalty = 1, data = tas_bd) %>%
add_relative_targets(0.17) %>%
add_locked_in_constraints("locked_in") %>%
add_binary_decisions()
# calculate performance metrics for ideal cost prioritization
s3_metrics <- tibble(
total_cost = eval_cost_summary(p5, s3[, "solution_1"])$cost,
total_boundary_length =
eval_boundary_summary(p5, s3[, "solution_1"])$boundary
)
# calculate performance metrics for ideal boundary length prioritization
s4_metrics <- tibble(
total_cost = eval_cost_summary(p5, s4[, "solution_1"])$cost,
total_boundary_length =
eval_boundary_summary(p5, s4[, "solution_1"])$boundary
)
After calculating these performance metrics, we can use them to automatically calculate a penalty value.
## [1] 0.00902
Now that we have calculated a penalty value using this method, we can use it to generate a prioritization.
Let’s update the results_data table with results about the prioritization.
# add new row with data for prioritization generated following Cohon et al. 1979
result_data <- bind_rows(
result_data,
tibble(
total_cost = eval_cost_summary(p6, s6[, "solution_1"])$cost,
total_boundary_length =
eval_boundary_summary(p6, s6[, "solution_1"])$boundary,
value = cohon_penalty,
name = paste0("penalty_", cohon_penalty),
label = paste0("Penalty = ", cohon_penalty),
method = "Cohon"
)
)
Next, let’s compare the prioritizations selected by different methods.
Method comparison
Let’s create a plot to visualize the results from the different methods.
# create plot to visualize trade-offs and show selected candidate prioritization
result_plot <-
ggplot(
data =
result_data %>%
mutate(vjust = if_else(method == "Cohon", -1, 0.5)),
aes(x = total_boundary_length, y = total_cost, label = label)
) +
geom_line() +
geom_point(aes(color = method), size = 3) +
geom_text(aes(vjust = vjust, color = method), hjust = -0.1) +
scale_color_manual(
name = "Method",
values = c(
"visual" = "#984ea3",
"none" = "#000000",
"TOPSIS" = "#e41a1c",
"Cohon" = "#377eb8"
)
) +
xlab("Total boundary length of prioritization") +
ylab("Total cost of prioritization") +
scale_x_continuous(expand = expansion(mult = c(0.05, 0.4)))
# render plot
print(result_plot)
We can see that the different methods selected different prioritizations. To further compare the results from the different methods, let’s create some maps showing the selected prioritizations.
# extract column names for creating the prioritizations
visual_name <- result_data$name[[which(result_data$method == "visual")]]
topsis_name <- result_data$name[[which(result_data$method == "TOPSIS")]]
# create object with selected prioritizations
solutions <- bind_cols(
tas_pu,
hierarchical_results %>%
st_drop_geometry() %>%
dplyr::select(all_of(c(visual_name, topsis_name))) %>%
setNames(c("Visual", "TOPSIS")),
s6 %>%
st_drop_geometry() %>%
dplyr::select(solution_1) %>%
rename(Cohon = "solution_1")
)
# plot maps of selected prioritizations
plot(
x =
solutions %>%
dplyr::select(Visual, TOPSIS, Cohon) %>%
mutate_if(is.numeric, function(x) {
case_when(
hierarchical_results$locked_in > 0.5 ~ "locked in",
x > 0.5 ~ "priority",
TRUE ~ "other"
)
}),
pal = c("purple", "grey90", "darkgreen")
)
How do we determine which one is best? This is difficult to say. Ideally, additional information could be used to help select a prioritization, such as knowledge on available resources, species’ connectivity requirements, and impacts of neighboring land use. However, from a practical perspective, prioritizations generated for academic contexts might find the quantitative approaches more useful because they have greater transparency and reproducibility. Ultimately, all of these methods are designed to support decision making. This means that they are intended to assist the decision making process, not serve as a replacement.
