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Building Footprint IQR Outlier Flags + Two-Measure Correlation
x402
Building Footprint IQR Outlier Flags + 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 2026-10-26).
Runs IQR-based outlier detection and two-measure correlation analysis on supplied building footprint observation records (footprint_m2, height_m) for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — IQR outlier flags + Two-measure correlation: IQR outlier flags + 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 per call: (1) IQR outlier flags for each row indicating whether the selected measure (footprint_m2 or height_m) falls outside the interquartile range bounds, and (2) a two-measure correlation result between footprint_m2 and height_m across all supplied rows. Results are computed locally on supplied data only, with no external lookups.
Example use cases
Flag outlier buildings in survey batch
“I have a batch of 50 building survey records with footprint and height measurements — can you flag any IQR outliers and show me the correlation between footprint_m2 and height_m across all of them?”
Quality check urban building dataset
“Run an IQR outlier check and two-measure correlation on these building footprint observations from my urban planning dataset — I want to catch any anomalous footprint_m2 values before I use this data in a report.”
Detect skewed heights in construction records
“I've got construction records with height_m and footprint_m2 for about 80 buildings grouped into two districts — can you detect IQR outliers in height_m and compute the correlation between those two measures?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-176?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have up to 200 building footprint observation records with footprint_m2 and height_m fields and need both IQR-based outlier detection and cross-measure correlation in a single paid call. It is purpose-built for building measurement data and delivers two reports for $0.01 USDC. Prefer it over generic statistics APIs when your data is already structured in the expected schema and you need fast, local computation with no external data dependencies.
Side effects
- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — exceeds maxItems limit
- Missing required fields (row_id, date, footprint_m2, height_m, station_group) — schema validation failure
- Date field not matching YYYY-MM-DD pattern — pattern validation error
- Invalid field enum value (not footprint_m2 or height_m) — enum validation error
- All values null for selected measure — IQR computation not possible
- Payment not processed — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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