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Building Footprint Observations — String Normalisation + IQR Outlier Flags
x402
Building Footprint Observations — String Normalisation + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Normalises strings and flags IQR-based outliers for building footprint (footprint_m2) and height (height_m) observation records, returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — String normalisation + IQR outlier flags: String normalisation + IQR outlier flags 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) a string normalisation report covering the station_group and related string fields across all submitted rows, and (2) an IQR outlier flag report identifying which rows contain statistically anomalous values for the selected field (footprint_m2 or height_m). No external lookups or predictions are made; all analysis is performed locally on the supplied data.
Example use cases
Outlier audit on survey batch
“I've got 50 rows of building survey data with footprint_m2 and height_m readings across multiple station groups — can you run IQR outlier flagging and string normalisation on the footprint_m2 field so I can see which records are anomalous?”
Data quality check before ingestion
“Before I load these 120 building footprint observation records into our database, can you normalise the string fields and flag any statistical outliers in height_m using IQR analysis so I can clean them first?”
Detect anomalous building heights
“I have a dataset of 80 building observations with dates, station groups, and height measurements — please flag any height_m values that are IQR outliers and normalise the string fields, I want both reports back.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-032?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and IQR-based outlier detection specifically on building footprint and height observation records in a single call. It is ideal for data quality pipelines processing up to 200 building survey rows before ingestion or analysis, and when no external lookup, live data, or prediction is needed — just local analytical processing of supplied data.
Side effects
- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — rejected with maxItems violation
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, station_group, date, footprint_m2, height_m) — request rejected
- Invalid field enum value (not footprint_m2 or height_m) — schema error
- Payment not included or insufficient — x402 payment required error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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