Exposure Calculation

The Exposure module in RiskChanges identifies which Elements-at-Risk (EaR) are affected by a hazard layer and quantifies the degree of exposure. The calculation approach depends on:

• The hazard data type (raster or vector)

• The Elements-at-Risk geometry type (point, line, polygon, or raster)

• The hazard classification settings

• The additional attributes assigned to the EaR layer (e.g., population, asset value, area)

This page explains how RiskChanges performs exposure calculations under different data combinations and how layer settings influence the final results.

Raster Hazard Layer with Point Elements-at-Risk

Using point datasets as Elements-at-Risk is the simplest exposure calculation scenario because each feature is represented by a single coordinate location.

RiskChanges calculates exposure by extracting the hazard pixel value at the location of each point feature. The extracted hazard value determines the hazard class assigned to the point.

In some cases, a point may intersect multiple pixels (for example, when located exactly on a pixel boundary). In this situation, the selected Intensity option from the Add Exposure menu determines which hazard value is used for visualization and classification.

Although one intensity option is selected for visualization, RiskChanges still computes all available intensity statistics internally. These results are stored in the final exposure layer attributes, allowing users to switch visualization fields later from the Detail menu without recalculating the exposure analysis.

For point datasets:

• The Exposed Fraction is always 1 (100%) because the entire feature is represented by a single point location.

• Exposure is assigned directly to the hazard class intersecting the point.

Raster Hazard Layer with Line, Polygon, and Raster Elements-at-Risk

For line, polygon, and raster/grid EaR datasets, a single feature may overlap multiple hazard pixels with different hazard intensities. To address this, RiskChanges applies a fraction-based exposure approach.

The system calculates how much of an EaR feature overlaps each hazard class while preserving the original geometry and attributes of the EaR dataset.

The exposed fraction unit depends on the geometry type:

EaR Geometry Type	Exposure Unit
Line	Length
Polygon	Area
Raster/Grid	Area

Line Elements-at-Risk

For line datasets (e.g., roads, pipelines, transmission lines), the Exposed Fraction represents:

The proportion of the total feature length exposed to a certain hazard class.

Example:

• Total road length = 10 km

• Portion exposed to High Flood Hazard = 4 km

Then:

• Exposed Fraction = 0.4 (40%)

Polygon Elements-at-Risk

For polygon datasets (e.g., land parcels, administrative areas, building footprints), the Exposed Fraction represents:

The proportion of the total feature area exposed to a certain hazard class.

Example:

• Total building area = 1,000 m²

• Area exposed to landslide hazard = 250 m²

Then:

• Exposed Fraction = 0.25 (25%)

Raster/Grid Elements-at-Risk

Raster EaR datasets are treated similarly to polygon data where the exposed portion is calculated based on overlapping area.

This approach is commonly used for:

• Population grids

• Land cover rasters

• Agricultural production rasters

• Economic raster datasets

Exposure Result Representation

Although the exposure calculation internally uses fractions, RiskChanges preserves the original EaR features and summarizes the results in exposure tables.

The results are presented in two forms:

Detail Table

Contains exposure information for each individual EaR feature.

Typical fields include:

• Hazard class

• Exposed fraction

• Exposed area/length

• Additional exposure metrics

Summary Table

Aggregates exposure statistics by EaR category or class.

Typical summary outputs include:

• Number of exposed features

• Total exposed area

• Total exposed population

• Total exposed asset value

Vector Hazard Layer with Elements-at-Risk

Although raster hazard layers are the most common format in risk assessment, some hazard datasets are provided as vector polygons.

Examples include:

• Landslide susceptibility zones

• Flood zoning maps

• Coastal setback zones

• Hazard planning maps

These vector hazard layers commonly contain categorical hazard classes such as:

• Very High

• High

• Medium

• Low

• Very Low

In this case, RiskChanges performs exposure calculations using spatial intersection between the hazard polygons and EaR layers.

The exposure workflow is similar for all EaR geometry types (point, line, polygon, and raster), but with several important differences:

• Hazard classes are typically categorical (non-numerical) rather than continuous values.

• The hazard classification shown in the results follows the original vector hazard categories.

• Exposure results use the Automatic Classes mode by default.

• Numerical hazard reclassification methods (e.g., Equal Interval, Quantile) are generally not applicable.

The Effect of Layer Settings on Exposure Calculation

The Exposure module is highly dependent on the settings defined in both the Hazard and Elements-at-Risk modules. Proper layer preparation is therefore essential to produce meaningful exposure results.

Hazard Layer Settings

Hazard Classification

Hazard classification controls how hazard intensity values are grouped into classes and directly affects the exposure results.

For raster hazard layers, users can select different Class Modes:

Class Mode	Description
Single Class	Entire hazard layer treated as one class
User Defined Classes	Users manually define class ranges
Automatic Classes	Hazard already contains categorical classes

Important Notes

• Single Class is suitable for binary hazard maps (hazard/no hazard).

• Automatic Classes is mainly used for vector hazard datasets with predefined categories.

• User Defined Classes provides the most flexibility and is recommended for continuous raster hazard datasets.

Field Column

The Field column specifies which hazard attribute or raster band is used for the exposure calculation.

RiskChanges automatically detects:

• Minimum value

• Maximum value

Users may modify these thresholds to:

• Exclude outliers

• Ignore background/noise values

• Focus only on relevant hazard intensities

Values outside the selected range are excluded from the exposure analysis.

Hazard Classification Methods

The selected classification method strongly influences how hazard severity is interpreted. Different methods are appropriate for different dataset characteristics.

Classification Method	Definition	Best Use Case
User Defined	Users manually specify class thresholds	Recommended when using official hazard standards, engineering thresholds, regulatory criteria, or datasets with large gaps/outliers
Equal Interval	Divides the value range into equal-sized intervals	Suitable for evenly distributed datasets with relatively uniform ranges
Quantile	Places an equal number of pixels/features into each class	Useful when balanced class sizes or relative ranking are desired
Natural Breaks (Jenks)	Minimizes variance within classes and maximizes variance between classes	Best for skewed or naturally clustered datasets
Standard Deviation	Classes are based on deviation from the mean	Useful for identifying anomalies or areas above/below average hazard
Geometric Interval	Uses geometric progression to create classes	Appropriate for moderately skewed continuous datasets
Logarithmic Scale	Applies logarithmic transformation before classification	Recommended for highly skewed datasets with extreme values
Percentile	Classification based on percentile thresholds	Useful for prioritization or identifying top-risk percentages

Elements-at-Risk Layer Settings

For Elements-at-Risk datasets, the most commonly used Class Mode is Automatic Classes.

This allows RiskChanges to categorize assets based on an attribute field such as:

• Building type

• Road type

• Land use type

• Infrastructure category

The selected Field column becomes the primary EaR category used in the Summary Table.

Additional EaR Fields

RiskChanges also allows users to include additional numerical fields for derivative exposure calculations.

Field Type	Description
Area Field	Area-related attribute
Value Field	Economic or replacement value
Population Field	Population count
Other Field	Any additional numerical variable

Warning

The Other Field must contain numerical values. Non-numerical fields cannot be used for exposure calculations.

Exposure Result Fields

The main outputs from the Exposure module include:

Output Field	Description
Exposed Fraction	Portion of the feature exposed to a hazard class
Exposed Count	Number of exposed features
Exposed Area	Area exposed based on fraction calculation
Exposed Value	Economic value exposed
Exposed Population	Population exposed
Exposed Other	Exposure value derived from the Other Field

Exposure Fraction

The Exposed Fraction represents the proportion of an EaR feature exposed to a hazard class.

This is applicable for:

• Line datasets

• Polygon datasets

• Raster/grid datasets

For point datasets, the Exposed Fraction is always 1 (100%) because the entire feature is represented by a single point.

Exposed Count

The Exposed Count represents:

The number of EaR features exposed to the hazard in general.

This value is not separated by hazard class unless users explicitly summarize the results by hazard category.

Additional Exposure Metrics

If additional EaR fields are included, RiskChanges calculates derivative exposure metrics using:

Exposed Metric = Exposed Fraction × EaR Field Value

Examples include:

• Exposed Population

• Exposed Asset Value

• Exposed Agricultural Area

• Exposed Infrastructure Capacity

Example:

• Building value = USD 100,000

• Exposed Fraction = 0.3

Then:

• Exposed Value = USD 30,000

This approach allows RiskChanges to estimate proportional exposure without modifying the original EaR dataset.