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Building Footprint Observations — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill
x402
Building Footprint Observations — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill 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).
Computes a secondary-measure weighted mean and fills missing values for building footprint observation records (footprint_m2, height_m) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Building footprint observations — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill 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) a secondary-measure weighted mean computed over the supplied building footprint or height field, and (2) explicit missing-value fill results replacing nulls with zero, mean, or median — all computed locally on the provided rows without any external lookup or prediction.
Example use cases
Imputing missing building heights
“I have a building survey dataset with about 80 rows where some height_m values are null — can you fill in the missing height_m values using the mean method and also give me the weighted mean across all the records?”
Weighted footprint aggregation by station group
“Run a weighted mean analysis on these 120 building footprint observation records grouped by station, using footprint_m2 as the target field, and fill any missing values with zero.”
Pre-analysis data cleaning for urban study
“Before I run my urban density model, I need to clean up this building dataset of 200 rows — fill any missing footprint_m2 entries using median and compute the weighted mean so I have a baseline figure.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-16-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have a batch of up to 200 building footprint or height observation records and need both a weighted mean aggregation and missing-value imputation in a single call. It is ideal for pre-processing building survey data before feeding into downstream models or dashboards. Prefer this over general-purpose analytics services when your data specifically involves footprint_m2 and height_m fields and you need two coordinated reports (weighted mean + fill) for a fixed low cost.
Side effects
- Fewer than 1 row or more than 200 rows submitted — validation error
- Invalid date format (not YYYY-MM-DD) — schema rejection
- Field value outside allowed numeric range — rejected
- Unrecognized fill method enum value — schema rejection
- All values for target field are null — weighted mean may be undefined or return NaN
- Missing required fields (row_id, station_group, date) — request rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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