# Building Footprint Observations — Trailing Mean Series + Two-Measure Correlation

> Building Footprint Observations — Trailing Mean Series + 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 2026-11-02).

Computes a trailing mean time series and a two-measure correlation report for user-supplied building footprint observation records (footprint_m2 and height_m).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-16-150?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/building-footprint-observations-trailing-mean-series-two-measure-bc9c5b3f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_406PCDXio7t_M2EZ1lDE3

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-trailing-mean-series-two-measure-bc9c5b3f -d '<json body>'
```

Example prompt: I have a dataset of building footprint observations with footprint_m2 and height_m readings across multiple station groups and dates — can you run a trailing mean series using a window of 3 and also compute the two-measure correlation between footprint_m2 and height_m for all 50 rows?

## When to prefer this

Choose this endpoint when you have tabular building observation data with footprint and height measurements and need both a smoothed trailing mean time series and a statistical correlation between the two measures in a single API call. It is ideal for offline/batch analytical workflows where no live data lookup, prediction, or external action is needed, and where the dataset fits within 200 rows. Prefer this over general-purpose analytics platforms when you want a low-cost ($0.01 USDC), self-contained computation on already-collected building data.

## Known failure modes

- Fewer than 1 row submitted — request rejected
- More than 200 rows submitted — exceeds row limit, request rejected
- Window size larger than number of rows — trailing mean cannot be computed
- Invalid date format (not YYYY-MM-DD) — schema validation error
- Missing required fields (row_id, date, footprint_m2, height_m) — validation failure
- All null values in a measure field — correlation or mean computation may be undefined

## How this service works

Building footprint observations — Trailing mean series + Two-measure correlation: Trailing mean series + 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 computed locally on the supplied data: (1) a trailing mean time series for the selected field (footprint_m2 or height_m) using the specified window size, and (2) a correlation analysis between footprint_m2 and height_m across the provided rows. No external data is fetched; all analysis is performed on the submitted records.

## 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/building-footprint-observations-trailing-mean-series-two-measure-bc9c5b3f/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)