# Carto Composite Scoring

> Guides the user through building composite score workflows when they ask about composite scores, indexes, multi-variable scores, ranking areas, site scoring, market potential, resilience indexes, risk indexes, weighted scores, PCA, or supervised/unsupervised scoring.

- Skill: `cartodb/carto-composite-scoring` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add cartodb/carto-composite-scoring`
- Raw SKILL.md: https://api.skillmd.com/api/skills/cartodb/carto-composite-scoring/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: cartodb (https://skillmd.com/u/cartodb)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/cartodb/carto-composite-scoring

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Use this skill whenever the user wants to create a composite score, index, or multi-variable ranking in a CARTO Workflow.

**Prerequisites**: Load `carto-create-workflow` for the development process. It covers both access paths — the MCP server's workflow tools (`create_workflow`, `validate_workflow`, `run_workflow`) when attached, the `carto workflows` CLI otherwise. Routing signals: `carto-basics/references/access-paths.md`.

## Instructions

### Step 1: Determine the scoring approach

Ask the user the following decision tree:

1. **"Do you have a target/outcome variable?"** (e.g. revenue, sales, crime rate)
   - Yes → **Supervised** method using `native.spatialcompositesupervised`
2. **"No target, but do you have expert knowledge of variable importance?"** (i.e. custom weights)
   - **2a. Transparency matters more than node count** → `native.normalize` per variable + `native.selectexpression` with the weighted-sum literally in SQL (`a_norm * 0.5 + b_norm * 0.2 + c_norm * 0.3`). Inputs and weights are inspectable inline in Workflows; each normalized column is materialized; no Analytics Toolbox dependency. Use when stakeholders need to see/audit the formula, or when you're not running on AT.
   - **2b. Want the AT pipeline (scaling, aggregation, bucketing) handled for you** → `native.spatialcompositeunsupervised` with `scoring_method: CUSTOM_WEIGHTS`. One node, but the formula is hidden inside the component.
3. **"No target, no weights?"**
   - → **Unsupervised** method with `ENTROPY` or `FIRST_PC` using `native.spatialcompositeunsupervised`

**Success**: You have identified which component and scoring method to use before designing the pipeline.

### Step 2: Build the pipeline

#### Supervised pipeline (`native.spatialcompositesupervised`)

1. Load the spatial features dataset (pre-indexed at H3 or Quadbin)
2. Load the target/outcome dataset
3. Join both datasets on the spatial index column
4. Select only the relevant feature columns (drop spatial index column and geometry from feature selection — pass only actual feature variables)
5. Run `native.spatialcompositesupervised` with parameters:
   - `model_type`: `LINEAR_REG`
   - `bucketize`: `EQUAL_INTERVALS_ZERO_CENTERED`
   - `n_buckets`: `5`
   - `outlier_removal`: `true`
   - `r_squared_threshold`: `0.4`
6. Output: composite score based on regression residuals (identifies areas that over/under-perform relative to the model)

**Success**: The workflow joins features with the target variable, selects only numeric feature columns, and produces a residual-based score per spatial cell.

#### Unsupervised pipeline (`native.spatialcompositeunsupervised`)

1. Load the spatial features dataset
2. Select only the relevant feature columns
3. Encode any categorical/ordinal variables to numeric using `native.casewhen` (e.g. "Low_density_urban" → 4, "High_density_urban" → 2)
4. Optionally reverse variables where higher = worse by multiplying by -1 in the SELECT query passed to the component
5. Run `native.spatialcompositeunsupervised` with parameters:
   - `scoring_method`: `CUSTOM_WEIGHTS` / `ENTROPY` / `FIRST_PC`
   - `weights`: required if `CUSTOM_WEIGHTS`. **Wire format is a JSON-encoded string of triples `[[<column>, <weight>, <reverse>], ...]`** — `column` is the feature column name, `weight` is a numeric weight (normalized internally to sum to 1), `reverse` is a boolean (`true` flips the variable so higher = worse becomes higher = better without needing a CASE WHEN). Example: `'[["population_density", 0.5, false], ["accident_rate", 0.5, true]]'`. **Not** an object map.
   - `scaling`: `RANKING`
   - `aggregation`: `LINEAR`
   - `output`: `RETURN_RANGE` with range `[0, 1]`
6. Output: composite index score per location

**Success**: All input variables are numeric, variable directions are aligned (higher = better for the score), and the output is a normalized score per spatial cell.

## Gotchas

- **Provider casing & SQL dialect.** This skill uses lowercase column names (BigQuery / Databricks / Postgres / Redshift convention). On Snowflake, unquoted identifiers surface UPPERCASE — reference `POPULATION_DENSITY`, `ACCIDENT_RATE`, etc. in weights, expressions, and downstream SQL. See `carto-create-workflow/references/providers/<provider>.md` for casing rules and SQL dialect equivalents.
- **All input variables must be numeric, with aligned direction.** Encode ordinal strings ("low"/"medium"/"high") via CASE WHEN first, and where "higher is worse" multiply by -1 (or set the `reverse` flag) before scoring — forgetting this inverts the score meaning.
- **Supervised R-squared threshold** (default 0.4) is permissive. If model fit is poor, the residual-based score is mostly noise. Inspect model diagnostics.
- **Custom weights are normalized internally** to sum to 1. The absolute values do not matter, only the ratios.
- **Supervised scores are residuals**, not raw values. The score identifies areas that DEVIATE from the model, not areas with the highest raw values.
- **Drop the spatial index column and geometry** from the feature selection — only pass actual feature variables to the scoring component.
- **`weights` type label is misreported.** The component schema (via `read_workflow_components` MCP or `carto workflows components get native.spatialcompositeunsupervised --json`) lists `weights` as `type: "ColumnNumber"`, but the actual wire shape is a `ColumnAndNumber` JSON-encoded string of triples `[[<column>, <weight>, <reverse>], ...]` (see Step 2 above). Trust this skill and the reference templates over the reported type label.
- **`indexcol.allowedColumns` is a UI hint, not a validator.** The schema lists `["geoid", "h3", "quadbin"]` for `native.spatialcompositeunsupervised.indexcol`, but it's a Workflows dropdown hint — any unique identifier column works at runtime (e.g. `store_id`, `cell_id`).

## Reference Templates

Both examples use Milan spatial features data at Quadbin resolution 18.

| Template | Component | File |
|---|---|---|
| Supervised — Identifying resilient neighbourhoods | `native.spatialcompositesupervised` | [composite-score-supervised.json](composite-score-supervised.json) |
| Unsupervised — Market potential scoring | `native.spatialcompositeunsupervised` | [composite-score-unsupervised.json](composite-score-unsupervised.json) |

## Common Variations

| Variation | Approach |
|---|---|
| Risk index (flood, crime, etc.) | Unsupervised with `CUSTOM_WEIGHTS`; reverse variables where higher = safer |
| Market potential / site scoring | Unsupervised with `CUSTOM_WEIGHTS` or `ENTROPY`; weight demand-side variables higher |
| Resilience index | Supervised with outcome variable (e.g. revenue change); residuals reveal over/under-performers |
| Data-driven index (no domain expertise) | Unsupervised with `FIRST_PC` or `ENTROPY` to let variance drive the weights |
| Human development / composite indicator | Unsupervised with `CUSTOM_WEIGHTS` and `RANKING` scaling for ordinal-safe aggregation |