{"uid":"cap_rstcXRmsTvLfY63SZFnul","slug":"transport-ridership-string-normalisation-two-measure-correlation-19294384","name":"Transport Ridership String Normalisation + Two-Measure Correlation","description":"Transport ridership — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied transport ridership records (passenger_count, service_count). 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-economics-05-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","passenger_count","service_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"service_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passenger_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","service_count":1,"passenger_count":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","service_count":2,"passenger_count":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":30}],"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_uO-tA2fuRvDT9VyHSDnZd","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 region/date strings and computes a two-measure Pearson/rank correlation between passenger_count and service_count for up to 200 transport ridership rows, returning two analytical reports.","exampleAgentPrompt":"I have 150 rows of bus ridership data with region names that need cleaning — can you normalise the region and date strings and then compute a correlation between passenger_count and service_count across all of them?","exampleUseCases":[{"title":"Regional bus ridership correlation study","prompt":"I've got 80 rows of daily bus data across multiple regions with columns for date, region, passenger_count, and service_count — please normalise the region labels and tell me how strongly passenger volume correlates with the number of services run."},{"title":"Transit planning data quality check","prompt":"Before presenting this to the city council, can you clean up the region strings in my 120-row transit dataset and produce a two-measure correlation report showing whether more service runs actually bring more passengers?"},{"title":"Multi-city rail ridership analysis","prompt":"Here are 200 rows of rail ridership records across group-a and group-b cities — normalise the date and region fields and run a correlation between passenger_count and service_count so I can see which markets respond most to added service."}],"resultDescription":"Two structured analytical reports: (1) a string normalisation report showing standardised region and date values for each input row, and (2) a two-measure correlation report quantifying the statistical relationship between passenger_count and service_count across all supplied rows. No external data is fetched; all analysis is performed on the supplied records only.","failureModes":["More than 200 rows submitted — request rejected due to row limit","Missing required fields (row_id, region, date, passenger_count, or service_count) — validation error","Date field not matching YYYY-MM-DD pattern — schema validation failure","All passenger_count or service_count values are null — correlation cannot be computed","Malformed JSON body — request rejected","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have a local batch of up to 200 transport ridership records and need both string normalisation (cleaning region/date labels) and a correlation analysis between two numeric measures (passenger_count and service_count) in a single cheap call. It is ideal when you do not need live external data, predictions, or professional assessments — only local analytical computation on supplied data. Prefer it over generic statistics libraries when you want a bundled, pay-per-use API that handles both data cleaning and correlation in one step at a fixed low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T12:36:16.027Z","isFirstParty":false,"canonicalSlug":"transport-ridership-string-normalisation-two-measure-correlation-19294384"}