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Building Footprint Observations — Trailing Mean Series + Two-Measure Correlation
x402
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).
Price / call$0.01
Success—
StatusUnknown
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.
Example use cases
Urban building size trend smoothing
“I've got monthly building footprint area readings in m2 for about 80 buildings across two station groups from 2024. Can you compute a trailing mean series with a window of 4 on the footprint_m2 field and also give me the correlation between footprint_m2 and height_m?”
Architecture dataset statistical audit
“We have 150 rows of building observation records with footprint_m2 and height_m columns. Run a trailing mean with window size 5 on height_m and also calculate the two-measure correlation between footprint_m2 and height_m so I can see how they relate.”
Real estate portfolio footprint analysis
“I'm analyzing a portfolio of 120 commercial properties — each has a date, station group, footprint in square meters, and height in meters. Can you smooth the footprint_m2 values with a trailing mean of window 6 and show me the correlation between footprint and height across the dataset?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-150?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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