ecorisk (version 0.2.1)

aggregate_risk: Compute High-Complexity Multi-Risk Scores and (Eco)system Risk

Description

The function aggregate_risk uses the output of the risk function or the vulnerability function. The risk or vulnerability scores are aggregated in three ways:

1. as multi-pressure risk per state indicator, representing the overall effect on one indicator

2. as multi- state indicator risk per pressure, representing the overall effect each pressure has on all state indicators

3. as ecosystem risk, the combined multi-pressure risks of all indicators.

Usage

aggregate_risk(risk_results, method = "mean")

Value

The function returns a list containing three sublists, first the multi-state indicator risk list, containing the risks and uncertainties aggregated per pressure, type and pathway. Second the multi-pressure risk list, where risks and uncertainties are aggregated per state indicator and type and pathway. The third list contains the ecosystem risks, which aggregates the multi-pressure risk and uncertainty scores per type and pathway.

Arguments

risk_results

Risk score for each state indicator ~ pressure ~ type combination, derived from the function risk or vulnerability.

method

Character indicating the method for aggregating the pressures and state indicators to multiple risk scores and the ecosystem risk score. The use can choose between (arithmetic) mean, median, sum, maximum and minimum. Default is the arithmetic mean.

Details

The returned lists are required in the plotting functions plot_radar and plot_heatmap. The aggregated scores are calculated for each type and pathway combination individually and across all types and pathways. If only one type and/or one pathway has been evaluated beforehand, the results will be the same for the different combinations.

See Also

vulnerability, risk, plot_radar, plot_heatmap

Examples

### Demo with example output from the risk() function based on expert scores
# (where direct and direct/indirect effects were evaluated)

# Calculate mean risks scores per indicator/pressure/ecosystem:
mean_risk <- aggregate_risk(
  risk_results = ex_output_risk_expert,
  method = "mean" # default
)
mean_risk
# Calculate median risks scores:
aggregate_risk(
  risk_results = ex_output_risk_expert,
  method = "median"
)
# Calculate maximum risks scores:
aggregate_risk(
  risk_results = ex_output_risk_expert,
  method = "maximum"
)


### Demo with example output from the risk() function based on modelled
#   scores (where only direct/indirect effects were evaluated)

# Calculate mean risks scores:
aggregate_risk(risk_results = ex_output_risk_model)


### Demo with combined expert-based and model-based pathways

combined_risk <- rbind(ex_output_risk_expert, ex_output_risk_model)
aggr_risk <- aggregate_risk(risk_results = combined_risk)
aggr_risk

aggr_risk$multi_indicator_risk |>
  dplyr::filter(type == "combined", pathway == "combined")
aggr_risk$multi_pressure_risk |>
  dplyr::filter(type == "combined", pathway == "combined")
aggr_risk$ecosystem_risk |>
  dplyr::filter(type == "combined", pathway == "combined")


### Demo with vulnerability scores using example output data from
#   vulnerability() based on modelled scores

aggregate_risk(risk_results = ex_output_vulnerability_model)