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Building Footprint Observations — Trailing Mean Series + Secondary-Measure Weighted Mean
x402
Building Footprint Observations — Trailing Mean Series + Secondary-Measure Weighted Mean 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 secondary-measure weighted mean for building footprint observation records (footprint area and height).
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Trailing mean series + Secondary-measure weighted mean: Trailing mean series + Secondary-measure weighted mean 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 supplied building footprint records: (1) a trailing mean time series computed over the specified window for the chosen primary field (footprint_m2 or height_m), and (2) a secondary-measure weighted mean where one field weights the average of the other — all computed locally without any external lookups or predictions.
Example use cases
Smoothing urban building footprint survey data
“I have a dataset of 50 building footprint observations from a city survey, each with a date, footprint in square meters, and height in meters grouped by neighborhood — can you compute a trailing mean series with a window of 5 on footprint_m2 and also a height-weighted mean for each group?”
Tracking construction site footprint trends
“We've been logging daily building footprint measurements across 3 construction groups over the past month — around 90 records total with footprint_m2 and height_m columns — please calculate a rolling trailing mean using a window of 7 on footprint_m2 and give me the secondary-measure weighted mean using height_m.”
Comparing building stock across station groups
“I've got building observation records for two station groups with dates, footprints in m², and heights in m — up to 150 rows — can you run a trailing mean window of 3 on height_m and compute the secondary-measure weighted mean so I can compare the groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-151?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have structured building footprint observation records with footprint area and height measurements and need both a trailing/rolling mean time series and a weighted mean statistic in a single call. It is ideal for urban analytics, construction monitoring, or real estate analysis workflows where you supply your own data and need fast, local statistical aggregation without any external data lookups, predictions, or AI-generated content. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for batch statistical summarization of building datasets.
Side effects
- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size smaller than 2 or larger than 200 — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) — error
- Invalid field enum value (not footprint_m2 or height_m) — rejected
- Null values in footprint_m2 or height_m may affect weighted mean calculation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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