{"uid":"cap_oGkmW9vXPfsiDFFQ5U85i","slug":"geospatial-tile-processing-numeric-row-ordering-two-measure-correlation-ba8e1aea","name":"Geospatial Tile Processing — Numeric Row Ordering + Two-Measure Correlation","description":"Geospatial tile processing — Numeric row ordering + Two-measure correlation: Numeric row ordering + Two-measure correlation for supplied geospatial tile processing records (tile_count, processing_seconds). 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-21-120?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","tile_count","processing_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"tile_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"processing_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","tile_count":10,"station_group":"group-a","processing_seconds":1},{"date":"2026-10-02","row_id":"row-2","tile_count":20,"station_group":"group-b","processing_seconds":2},{"date":"2026-10-03","row_id":"row-3","tile_count":15,"station_group":"group-a","processing_seconds":3},{"date":"2026-10-04","row_id":"row-4","tile_count":30,"station_group":"group-b","processing_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["tile_count","processing_seconds"],"type":"string","default":"tile_count"}}},"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_YdNVa8ELCtVh6fuHo8bf0","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":"Ranks rows by a chosen numeric field and computes correlation between tile_count and processing_seconds for up to 200 geospatial tile processing records, returning two analytical reports.","exampleAgentPrompt":"I have up to 200 geospatial tile processing records with tile_count and processing_seconds fields — can you rank them by tile_count and also compute the correlation between tile_count and processing_seconds across all rows?","exampleUseCases":[{"title":"Tile throughput performance ranking","prompt":"Here are my tile processing logs for October — each row has a station group, date, tile_count, and processing_seconds. Can you rank all 150 rows by tile_count and also tell me how strongly tile count correlates with processing time?"},{"title":"Station group processing efficiency analysis","prompt":"I've got records from two station groups across 60 days, each with tile_count and processing_seconds. Please rank the rows by processing_seconds and compute the correlation between tile count and processing time so I can see if busier tiles take proportionally longer."},{"title":"Geospatial pipeline bottleneck investigation","prompt":"Can you take these 80 geospatial tile processing records and give me a numeric row ordering by tile_count plus a two-measure correlation report showing whether tile_count and processing_seconds move together?"}],"resultDescription":"Returns two analytical reports: (1) rows ranked numerically by the selected field (tile_count or processing_seconds), and (2) a correlation analysis between tile_count and processing_seconds across all supplied rows. No external data is fetched — all computation is performed locally on the submitted dataset of up to 200 rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, tile_count, processing_seconds) — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Null values in both numeric fields may reduce correlation reliability","Invalid field enum value (not tile_count or processing_seconds) — error returned"],"whenToPreferThis":"Choose this endpoint when you have geospatial tile processing records with tile_count and processing_seconds fields and need both a numeric ranking and a correlation analysis in a single low-cost call. Ideal for lightweight local statistical analysis without needing a full data warehouse or external ML service. Best suited for datasets up to 200 rows where the two specific measures (tile count and processing time) are the focus.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:39:52.671Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-numeric-row-ordering-two-measure-correlation-ba8e1aea"}