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Building Footprint Observations — Numeric Interval Filter + Trailing Mean Series
x402
Building Footprint Observations — Numeric Interval Filter + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-30).
Filters building footprint observation records by a numeric interval and computes a trailing mean series for footprint_m2 or height_m fields, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Numeric interval filter + Trailing mean series: Numeric interval filter + Trailing mean series 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) records from the input dataset where the selected field (footprint_m2 or height_m) falls within the specified numeric interval [minimum, maximum], and (2) a trailing mean series computed over the selected field using the specified window size. Both reports cover up to 200 input rows and are computed locally without external data lookups.
Example use cases
Urban density analysis for city planners
“I have 200 rows of building footprint observations from downtown — filter them to only include buildings where footprint_m2 is between 100 and 800, and give me a trailing mean series with a window of 10 for footprint_m2 so I can see size trends over time.”
Height compliance check with rolling average
“Here are 80 building records from our site survey — filter to height_m values between 5 and 30 meters and compute a trailing mean series with a window of 3 for height_m so I can spot anomalies in the rolling average.”
Portfolio footprint trend reporting
“I need two analytical reports on these 120 building footprint observations: filter footprint_m2 values between 200 and 1000, and run a trailing mean series with window 7 on footprint_m2 grouped by station_group.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-126?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have building footprint or height observation records and need both a numeric range filter and a rolling/trailing mean series in a single call. It is well-suited for small-to-medium datasets (up to 200 rows) where you want two bundled analytical outputs without building custom code. Prefer it over general-purpose data tools when your data specifically involves footprint_m2 and height_m columns and you need a cost-effective ($0.01 USDC for both reports) batch computation.
Side effects
- Fewer than 1 row supplied — returns validation error
- More than 200 rows supplied — rejected with maxItems error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Minimum value greater than maximum value — may return empty filtered result with no error
- Window size larger than number of rows — trailing mean series may return nulls or partial results
- Null values in target field — nulls may propagate into mean calculations
- Missing required row fields (row_id, station_group, date) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer | |
| maximum | number | |
| minimum | number |
Tags
x402
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