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Building Footprint Observations — String Normalisation + Secondary-Measure Weighted Mean
x402
Building Footprint Observations — String Normalisation + 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 2026-11-03).
Performs string normalisation and secondary-measure weighted mean analysis on up to 200 building footprint observation records (footprint_m2, height_m), returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — String normalisation + Secondary-measure weighted mean: String normalisation + 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 2 analytical reports: a string normalisation report cleaning station_group and other string fields, and a secondary-measure weighted mean report computing weighted averages of the selected field (footprint_m2 or height_m) using the other numeric measure as the weight — all derived locally from the supplied rows without external lookups.
Example use cases
Normalise urban building survey data
“I have a batch of 50 building survey records with footprint_m2 and height_m values across different station groups. Can you run string normalisation and compute the secondary-measure weighted mean on the footprint_m2 field and give me both reports?”
Quality-check building footprint dataset
“I've collected building observations from two station groups over the past month — up to 120 rows with dates, footprint sizes, and heights. Please normalise the strings and calculate the height-weighted mean for footprint_m2 so I can see a clean summary.”
Compare footprint data across construction sites
“I need two analytical reports on my building footprint dataset — 30 rows tagged by site group with dates, footprint_m2, and height_m. Run string normalisation and the secondary-measure weighted mean on height_m for me.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular building footprint observation data (up to 200 rows) and need both string normalisation of categorical fields and a secondary-measure weighted mean — all computed locally without any external data lookup, prediction, or live API calls. It is best suited for offline analytical pipelines processing building measurement datasets where both data cleaning and weighted aggregation are needed together in a single call at low cost.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Null values in both numeric fields may produce undefined weighted mean results
- Invalid field enum value (not footprint_m2 or height_m) causes schema rejection
- Empty rows array rejected due to minItems constraint
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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