# 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