{"uid":"cap_PEWFCDzmB2Taxl5i46Twb","slug":"building-footprint-observations-secondary-measure-weighted-mean-475abde6","name":"Building Footprint Observations — Secondary-Measure Weighted Mean + Explicit Missing-Value Fill","description":"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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-16-187?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","footprint_m2","height_m"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"height_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"footprint_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","height_m":1,"footprint_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","height_m":2,"footprint_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","height_m":3,"footprint_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","height_m":4,"footprint_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["footprint_m2","height_m"],"type":"string","default":"footprint_m2"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_dA5mQPPpeJaxXv_L6erXE","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes a secondary-measure weighted mean and fills missing values for building footprint observation records (footprint_m2, height_m) across up to 200 rows.","exampleAgentPrompt":"I have a dataset of 150 building footprint observations with some missing footprint_m2 values — can you compute the secondary-measure weighted mean for footprint_m2 and fill the missing values using the median method?","exampleUseCases":[{"title":"Imputing missing building heights","prompt":"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?"},{"title":"Weighted footprint aggregation by station group","prompt":"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."},{"title":"Pre-analysis data cleaning for urban study","prompt":"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."}],"resultDescription":"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.","failureModes":["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"],"whenToPreferThis":"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.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-03T12:41:42.165Z","isFirstParty":false,"canonicalSlug":"building-footprint-observations-secondary-measure-weighted-mean-475abde6"}