{"uid":"cap_TfkPqhHaxaNKpdCN-tGGc","slug":"transport-ridership-analytics-numeric-row-ordering-two-measure-8084712e","name":"Transport Ridership Analytics: Numeric Row Ordering + Two-Measure Correlation","description":"Transport ridership — Numeric row ordering + Two-measure correlation: Numeric row ordering + 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-05-120?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},"field":{"enum":["passenger_count","service_count"],"type":"string","default":"passenger_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_0kZssLK73tL6bElKx6YGy","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 numeric row ordering and Pearson/Spearman correlation between passenger_count and service_count for up to 200 supplied transport ridership records, returning two independent analytical reports.","exampleAgentPrompt":"I have a dataset of transit records — can you rank the rows by passenger_count and also compute the correlation between passenger_count and service_count? Here are 4 rows: row-1 group-a 2026-10-01 passengers=10 services=1, row-2 group-b 2026-10-02 passengers=20 services=2, row-3 group-a 2026-10-03 passengers=15 services=3, row-4 group-b 2026-10-04 passengers=30 services=4.","exampleUseCases":[{"title":"Transit efficiency correlation analysis","prompt":"I have monthly ridership data for 50 bus routes — can you calculate the correlation between passenger_count and service_count across all routes, and also rank each route by passenger_count?"},{"title":"Regional ridership ranking for planning","prompt":"Here are 120 rows of regional transit records with dates, passenger counts, and service counts — please rank them numerically by service_count and also run a correlation analysis between the two measures so I can see if busier routes get more services."},{"title":"Daily transit data quality review","prompt":"I've collected 30 days of daily ridership data for two groups — can you order all the rows by passenger_count and tell me how strongly passenger volume correlates with service frequency across those days?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric row ordering of the supplied records ranked by the specified field (passenger_count or service_count), and (2) a correlation analysis between passenger_count and service_count across all submitted rows. All computation is performed locally on the supplied data — no external data sources, predictions, or live lookups are involved.","failureModes":["Fewer than 1 row supplied — validation error returned","More than 200 rows submitted — request rejected with item limit error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, region, date) — schema rejection","Null values in both passenger_count and service_count — correlation report may be undefined or NaN","Invalid field enum value — request rejected"],"whenToPreferThis":"Choose this endpoint when you have pre-collected transport ridership tabular data (up to 200 rows) and need both a ranked ordering of rows by a numeric measure AND a statistical correlation between passenger_count and service_count in a single low-cost call. It is ideal for offline/batch analytical workflows where no live data lookup or prediction is needed, and where two reports for $0.01 USDC provides cost efficiency over separate API calls.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:06.615Z","isFirstParty":false,"canonicalSlug":"transport-ridership-analytics-numeric-row-ordering-two-measure-8084712e"}