Tutorial: Conflict Index#

Important

Before proceeding, make sure your project structure is fully configured. See Tutorial: Setting Up a PEM Project.

Overview#

The Conflict Index quantifies the spatial probability of interaction conflicts between ocean users within a given scenario.

Unlike the Habitat Risk Index, which integrates ecological exposure, the Conflict Index focuses exclusively on user–user spatial overlap.

Ocean Users may be represented as:

  • Boolean footprint rasters (0/1 presence); or

  • Continuous fuzzy rasters (values between 0 and 1).

The Conflict Index is computed as:

  1. Pairwise raster multiplication between all user combinations;

  2. Weighting of each overlap using a conflict matrix;

  3. Aggregation of weighted overlaps;

  4. Final normalization to a 0–1 scale.

The result is a normalized spatial indicator representing the relative likelihood of conflict within the scenario.

See also

Learn more on the Conflict Index in About: Conflict Index

See also

Ensure that Ocean Users have been properly configured before computing the Conflict Index. See Tutorial: Setting Up a PEM Project and Populate Ocean Users.

Complete Workflow#

  1. setup_conflict_matrix()

  2. (Optional but recommended) Adjust conflict weights

  3. get_conflict_index()

Each stage is detailed below.

1. Script: Initialize the Conflict Matrix#

Before computing spatial conflicts, a conflict weight matrix must be defined.

Run:

setup_conflict_matrix(folder_project, scenario)

Example:

Script example
# !WARNING: run this in QGIS Python Environment
import importlib.util as iu

# define the paths to the module file
# ------------------------------------------------------
file = "path/to/conflict.py" # change here

# define the project folder
# ------------------------------------------------------
folder = "path/to/folder" # change here

# define scenario
# ------------------------------------------------------
scenario = "baseline"

# call the function
# ------------------------------------------------------
# do not change here
spec = iu.spec_from_file_location("module", file)
module = iu.module_from_spec(spec)
spec.loader.exec_module(module)

output = module.setup_conflict_matrix(
    folder_project=folder,
    scenario=scenario
)

print(" ----- DONE -----")

What the function does:

  • Scans all user rasters under:

    {project}/inputs/users/{scenario}

  • Identifies all user layer names;

  • Creates a square CSV matrix;

  • Initializes the lower triangle with value 1;

  • Sets diagonal and upper triangle to 0.

The generated file:

{project}/inputs/users/{scenario}/conflict.csv

This CSV defines pairwise conflict weights between users.

2. Manual Step: Adjust Conflict Weights#

By default, all user pairs are assigned weight = 1.

However, in realistic marine spatial planning contexts, conflict intensity is not uniform across activities.

Examples:

  • Offshore wind vs tourism → potentially high conflict

  • Fisheries vs conservation zones → context-dependent

  • Submarine cables vs shipping lanes → possibly low conflict

  • Compatible activities → zero conflict

The user should manually edit conflict.csv to reflect domain knowledge, policy priorities, or stakeholder input.

Important notes:

  • Only the lower triangle is used.

  • Diagonal values are ignored.

  • Weights typically range from 0 (no conflict) to 1 (maximum conflict), but any non-negative numeric value is allowed.

  • Symmetry is assumed.

Although optional, adjusting weights is strongly recommended to ensure realistic conflict representation.

Conflict Matrix example
users        , cargo , fisheries , tourism , oilngas , offshwind
cargo        , 0     , 0         , 0       , 0       , 0
fisheries    , 5     , 0         , 0       , 0       , 0
tourism      , 10    , 7         , 0       , 0       , 0
oilngas      , 13    , 9         , 6       , 0       , 0
offshwind    , 8     , 4         , 3       , 11      , 0

3. Script: Generate the Conflict Index#

Once the matrix is finalized, compute the Conflict Index:

get_conflict_index(folder_project, scenario)

Example:

Script example
# !WARNING: run this in QGIS Python Environment
import importlib.util as iu

# define the paths to the module file
# ------------------------------------------------------
file = "path/to/conflict.py" # change here

# define the project folder
# ------------------------------------------------------
folder = "path/to/folder" # change here

# define scenario
# ------------------------------------------------------
scenario = "baseline"

# call the function
# ------------------------------------------------------
# do not change here
spec = iu.spec_from_file_location("module", file)
module = iu.module_from_spec(spec)
spec.loader.exec_module(module)

output = module.get_conflict_index(
    folder_project=folder,
    scenario=scenario
)

print(" ----- DONE -----")

Computation Steps#

The function performs:

  1. Identification of all unique user raster pairs.

  2. Pairwise raster multiplication:

  3. Normalization of each pairwise overlap.

  4. Weighting using the conflict matrix:

  5. Summation of all weighted overlaps.

  6. Final normalization of the aggregated map.

Output:

{project}/outputs/{scenario}/{scenario}_conflict.tif

The output raster:

  • Is continuous;

  • Is normalized between 0 and 1;

  • Represents relative spatial conflict probability;

  • Is comparable between scenarios.

Conceptual Interpretation#

The Conflict Index measures spatial coincidence of activities, weighted by their incompatibility.

Conceptually:

User A × User B
User A × User C
User B × User C
        ↓
  Apply conflict weights
        ↓
  Sum weighted overlaps
        ↓
    Normalize (0–1)

Important clarification:

The index magnitude is normalized per scenario. Therefore:

  • Absolute values may not be directly comparable across different spatial extents or user compositions.

  • Spatial patterns and relative intensities are the primary interpretation targets.

The Conflict Index can now be integrated into the multi-criteria Performance Index framework.