Relative wealth index

Example: A raster map showing socio-economic vulnerability through relative wealth or poverty indices, used as a proxy for coping capacity and resilience to disasters.

Step-by-step guidance

1. Dataset-level metadata

Select the following values when describing your dataset:

  • Risk data type: vulnerability

  • Title: “Relative wealth index for [region]”

  • Description: Brief description of the index methodology, data sources (e.g., census, DHS surveys, satellite imagery), and interpretation

  • Publisher: Organization that produced the wealth index (e.g., World Bank, research institution)

  • License: Appropriate license

2. Resources

Add resources for your wealth index files:

  • Format: geotiff, netcdf, csv, or shapefile

  • Spatial resolution: Resolution in meters (e.g., 1000m) or administrative level

  • Coordinate reference system: EPSG:4326 or appropriate projected CRS

3. Vulnerability metadata

Under the Vulnerability section:

Category

  • Category: socioeconomic

Vulnerability indicator

Since this is not a hazard-specific damage function, use the indicator approach:

Indicator specification:

  • Type: socioeconomic

  • Variable: wealth_index or poverty_rate

  • Unit: Dimensionless index (e.g., 0-100) or ratio (0-1)

  • Interpretation: Document scale direction (higher values = more wealth/less vulnerable)

Scale and normalization

  • Scale minimum: Minimum value (e.g., 0)

  • Scale maximum: Maximum value (e.g., 100 or 1)

  • Reference population: National, regional, or global normalization

4. Spatial coverage

Define the geographic extent:

  • Scale: national, sub-national, or regional

  • Countries: Select applicable ISO 3166-1 alpha-3 country codes

  • Administrative regions: If aggregated by admin units

  • Bounding box: Specify coordinates of the mapped area

Example data structure

Your wealth index dataset should include:

For raster data:

  • Gridded wealth index values

  • NoData value specification

  • Value interpretation (scale definition)

For vector/tabular data:

  • Geographic unit ID (e.g., grid cell ID, admin code)

  • Geometry or coordinates

  • Wealth index value

  • Population count (optional)

  • Confidence interval or uncertainty (optional)

Example CSV structure:

Admin_Code,Admin_Name,Wealth_Index,Population,Data_Source
THA001001,Bangkok,82.5,1568737,Census_2020
THA002001,Chiang_Mai,65.3,174235,Census_2020
THA003001,Phuket,78.9,416582,Census_2020

Key considerations

  • Clearly document the methodology used to compute the index

    • Household asset ownership

    • Satellite imagery analysis (nighttime lights, building density)

    • Survey data (DHS, LSMS)

    • Composite indicators (education, health, income)

  • Specify the reference year for the data

  • Explain the scale and interpretation (is higher better or worse?)

  • Document normalization approach (national percentiles, z-scores, etc.)

  • Include data sources used in index construction

  • Note limitations and uncertainty in the estimates

  • Specify how missing data or gaps are handled

  • Link to detailed methodology documentation

Relationship to risk assessment

The wealth index serves as a proxy for:

  • Coping capacity: Ability to absorb and recover from disaster impacts

  • Adaptive capacity: Resources available for adaptation and preparedness

  • Social vulnerability: Susceptibility to harm due to socio-economic factors

This differs from hazard-specific vulnerability functions in that it represents:

  • Pre-existing conditions that modify disaster impacts

  • Differential capacity to prepare, respond, and recover

  • Non-structural factors affecting risk outcomes

When combined with hazard and exposure data, the wealth index can help identify:

  • Communities with high exposure but low capacity

  • Priority areas for risk reduction investments

  • Equity considerations in disaster planning