# Building Footprint Observations — Two-Measure Correlation + Explicit Missing-Value Fill

> Building Footprint Observations — Two-Measure Correlation + Explicit Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-25).

Computes pairwise correlation between footprint_m2 and height_m, and fills missing values in a chosen field using zero, mean, or median imputation, across up to 200 building observation rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-16-184?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-25
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/building-footprint-observations-two-measure-correlation-explicit-a7925598
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_nTvSm9f5x4JracbnoR3Nf

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-two-measure-correlation-explicit-a7925598 -d '<json body>'
```

Example prompt: I have 150 building observation records with footprint_m2 and height_m fields — some height_m values are missing. Can you compute the correlation between footprint and height, and fill the missing height_m values using median imputation?

## When to prefer this

Choose this endpoint when you have up to 200 building observation records with footprint_m2 and height_m columns and need both a correlation analysis between those two measures and missing-value imputation in a single call. Ideal for offline/batch data preparation workflows where no live data lookup or external enrichment is needed. Prefer this over general-purpose analytics when the specific combination of two-measure correlation plus explicit missing-value fill on building footprint data is required at minimal cost ($0.01 USDC for both reports).

## Known failure modes

- Fewer than 1 row submitted — violates minItems constraint
- Row missing required fields (row_id, date, footprint_m2, height_m, station_group) — schema validation error
- Date field not in YYYY-MM-DD format — pattern validation failure
- More than 200 rows submitted — exceeds maxItems limit
- Invalid enum value for method or field — rejected by schema
- All values in the target field are null — imputation may produce degenerate results
- Correlation undefined if one variable has zero variance — may return NaN or error

## How this service works

Building footprint observations — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + Explicit missing-value fill 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 for the submitted building footprint observation rows: (1) a pairwise correlation analysis between footprint_m2 and height_m across the supplied records, and (2) a missing-value-filled version of the specified field (footprint_m2 or height_m) using the chosen imputation strategy (zero, mean, or median). Results are computed locally 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/building-footprint-observations-two-measure-correlation-explicit-a7925598/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)