{"uid":"cap_DYWcD6rUXDLfBy7FgJFzD","slug":"geospatial-tile-processing-string-normalisation-two-measure-correlation-0fedbe04","name":"Geospatial Tile Processing — String Normalisation + Two-Measure Correlation","description":"Geospatial tile processing — String normalisation + Two-measure correlation: String normalisation + 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-21-033?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}}},"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_wjpLd-i43XtVRJrqW504_","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":"Normalises station group strings and computes two-measure Pearson/rank correlation between tile_count and processing_seconds for up to 200 geospatial tile processing records.","exampleAgentPrompt":"I have up to 200 rows of geospatial tile processing records with fields row_id, station_group, date, tile_count, and processing_seconds — can you run string normalisation on the station group names and compute the two-measure correlation between tile_count and processing_seconds for me?","exampleUseCases":[{"title":"Tile throughput correlation analysis","prompt":"I've got 150 rows of tile processing logs with station groups like 'Group-A', 'group_a', and 'GROUP A' — can you normalise those group name strings and tell me the correlation between how many tiles were processed and how many seconds it took?"},{"title":"Processing efficiency statistical check","prompt":"Here are 80 records from our geospatial pipeline: each row has a row_id, station_group, date, tile_count, and processing_seconds. Run the two-measure correlation to see if tile count is a reliable predictor of processing time, and clean up the group labels while you're at it."},{"title":"Multi-group tile performance review","prompt":"I have tile processing data from three station groups over the past month — about 200 rows total — with tile_count and processing_seconds for each. Can you normalise the station group strings and give me a two-measure correlation report?"}],"resultDescription":"Returns two analytical reports per call: (1) a string normalisation report for the station_group field across all supplied rows, and (2) a two-measure correlation report quantifying the statistical relationship between tile_count and processing_seconds. Results are computed locally from the supplied data — no external lookups occur.","failureModes":["Exceeding 200 rows returns a validation error","Missing required fields (row_id, tile_count, processing_seconds, station_group, date) cause row-level rejection","Malformed date strings (not YYYY-MM-DD) fail pattern validation","Null values in both numeric columns may reduce correlation reliability","Empty rows array rejected by minItems:1 constraint"],"whenToPreferThis":"Choose this endpoint when you have raw geospatial tile processing tabular data (up to 200 rows) and need both string normalisation of categorical group labels and a two-measure statistical correlation between tile count and processing time in a single low-cost call. It is ideal for lightweight offline statistical analysis without needing a full data platform or external data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:34:04.075Z","isFirstParty":false,"canonicalSlug":"geospatial-tile-processing-string-normalisation-two-measure-correlation-0fedbe04"}