# Building Footprint Observations — String Normalisation + IQR Outlier Flags

> Building Footprint Observations — String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Normalises strings and flags IQR-based outliers for building footprint (footprint_m2) and height (height_m) observation records, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-16-032?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/building-footprint-observations-string-normalisation-iqr-outlier-flags-10bb7243
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_IMH6RV85WKHCPqLemUdEA

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-observations-string-normalisation-iqr-outlier-flags-10bb7243 -d '<json body>'
```

Example prompt: Can you run string normalisation and IQR outlier detection on my building footprint observations? I have 4 rows with footprint_m2 and height_m values — row-1 group-a 2026-10-01 footprint 10 height 1, row-2 group-b 2026-10-02 footprint 20 height 2, row-3 group-a 2026-10-03 footprint 15 height 3, row-4 group-b 2026-10-04 footprint 30 height 4 — and I want to analyse the footprint_m2 field.

## When to prefer this

Choose this endpoint when you need both string normalisation and IQR-based outlier detection specifically on building footprint and height observation records in a single call. It is ideal for data quality pipelines processing up to 200 building survey rows before ingestion or analysis, and when no external lookup, live data, or prediction is needed — just local analytical processing of supplied data.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — rejected with maxItems violation
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) — request rejected
- Invalid field enum value (not footprint_m2 or height_m) — schema error
- Payment not included or insufficient — x402 payment required error

## How this service works

Building footprint observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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: (1) a string normalisation report covering the station_group and related string fields across all submitted rows, and (2) an IQR outlier flag report identifying which rows contain statistically anomalous values for the selected field (footprint_m2 or height_m). No external lookups or predictions are made; all analysis is performed locally on the supplied data.

## 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-observations-string-normalisation-iqr-outlier-flags-10bb7243/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)