# Building Footprint IQR Outlier Flags + Two-Measure Correlation

> Building Footprint IQR Outlier Flags + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).

Runs IQR-based outlier detection and two-measure correlation analysis on supplied building footprint observation records (footprint_m2, height_m) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-16-176?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/building-footprint-iqr-outlier-flags-two-measure-correlation-388931a2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_TzT4ksA2JKHCC3JWiV882

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability building-footprint-iqr-outlier-flags-two-measure-correlation-388931a2 -d '<json body>'
```

Example prompt: Can you run IQR outlier detection and two-measure correlation on my building footprint observations? I have 4 rows: row-1 on 2026-10-01 in group-a with footprint_m2=10 and height_m=1, row-2 on 2026-10-02 in group-b with footprint_m2=20 and height_m=2, row-3 on 2026-10-03 in group-a with footprint_m2=15 and height_m=3, row-4 on 2026-10-04 in group-b with footprint_m2=30 and height_m=4 — analyze the footprint_m2 field.

## When to prefer this

Choose this endpoint when you have up to 200 building footprint observation records with footprint_m2 and height_m fields and need both IQR-based outlier detection and cross-measure correlation in a single paid call. It is purpose-built for building measurement data and delivers two reports for $0.01 USDC. Prefer it over generic statistics APIs when your data is already structured in the expected schema and you need fast, local computation with no external data dependencies.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, footprint_m2, height_m, station_group) — schema validation failure
- Date field not matching YYYY-MM-DD pattern — pattern validation error
- Invalid field enum value (not footprint_m2 or height_m) — enum validation error
- All values null for selected measure — IQR computation not possible
- Payment not processed — x402 payment required response

## How this service works

Building footprint observations — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation for supplied building footprint observations records (footprint_m2, height_m). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.

## Output

Returns two analytical reports per call: (1) IQR outlier flags for each row indicating whether the selected measure (footprint_m2 or height_m) falls outside the interquartile range bounds, and (2) a two-measure correlation result between footprint_m2 and height_m across all supplied rows. Results are computed locally on supplied data only, with no external lookups.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "footprint_m2",
     "height_m"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "height_m": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "footprint_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "height_m": 1,
     "footprint_m2": 10,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "height_m": 2,
     "footprint_m2": 20,
     "station_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "height_m": 3,
     "footprint_m2": 15,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "height_m": 4,
     "footprint_m2": 30,
     "station_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "footprint_m2",
    "height_m"
   ],
   "type": "string",
   "default": "footprint_m2"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/building-footprint-iqr-outlier-flags-two-measure-correlation-388931a2/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)