{"uid":"cap_pLNVvnNKmpohh1VCuDBqE","slug":"museum-footfall-adjacent-row-changes-a5cc6736","name":"Museum Footfall Adjacent Row Changes","description":"Museum footfall - Adjacent row changes: Adjacent row changes for Museum footfall. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded adjacent row changes report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-museum-footfall-period-change?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","exhibit","date","visitors","dwell_minutes"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"exhibit":{"type":"string","default":"","maxLength":80},"visitors":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dwell_minutes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","exhibit":"group-a","visitors":10,"dwell_minutes":1},{"date":"2026-10-02","row_id":"row-2","exhibit":"group-b","visitors":20,"dwell_minutes":2},{"date":"2026-10-03","row_id":"row-3","exhibit":"group-a","visitors":15,"dwell_minutes":3},{"date":"2026-10-04","row_id":"row-4","exhibit":"group-b","visitors":30,"dwell_minutes":4}],"maxItems":200,"minItems":1},"field":{"enum":["visitors","dwell_minutes"],"type":"string","default":"visitors"}}},"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_nVmx0p2YCouQcv4tVXAKE","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 period-over-period adjacent row changes for museum visitor footfall data (visitors or dwell time) across up to 200 exhibit records.","exampleAgentPrompt":"Calculate the adjacent row changes in visitor counts for my museum footfall data — I have 4 exhibit records: exhibit group-a had 10 visitors on Oct 1, group-b had 20 on Oct 2, group-a had 15 on Oct 3, and group-b had 30 on Oct 4. Use the visitors field.","exampleUseCases":[{"title":"Daily visitor trend comparison","prompt":"I have a week of daily footfall data for our Natural History Museum exhibits — can you compute how visitor counts changed between each consecutive day for all four galleries? Here are the rows: gallery-a saw 120 visitors on Nov 1, 145 on Nov 2, 130 on Nov 3, and 160 on Nov 4. Analyze the visitors field."},{"title":"Dwell time shift analysis","prompt":"Run an adjacent row change report on dwell time for our museum exhibits — I want to see how average dwell minutes shifted between consecutive records. I have 6 rows: Impressionism had 22 minutes on 2025-03-01, 18 on 2025-03-02, Sculpture had 35 on 2025-03-03, 40 on 2025-03-04, Antiquities had 28 on 2025-03-05, and 31 on 2025-03-06. Use the dwell_minutes field."},{"title":"Post-event footfall change audit","prompt":"After our special exhibition weekend, I need to see how visitor numbers changed row by row across our five exhibit zones — can you compute adjacent period changes? Zone-A: 500 on 2025-06-14, Zone-B: 620 on 2025-06-15, Zone-A: 430 on 2025-06-16, Zone-B: 710 on 2025-06-17, Zone-C: 380 on 2025-06-18. Analyze the visitors field."}],"resultDescription":"A bounded adjacent row changes report containing period-over-period delta values for each consecutive pair of input records, computed locally on the submitted museum footfall data (visitors or dwell_minutes field). No enrichment, forecasting, or external data is applied — purely descriptive analytics on the supplied rows.","failureModes":["Empty or missing rows array returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, exhibit, date, visitors, dwell_minutes) on any row causes rejection","Invalid field enum value (anything other than 'visitors' or 'dwell_minutes') returns an error","All-null numeric values may produce empty or degenerate delta output"],"whenToPreferThis":"Choose this endpoint when you need fast, local period-over-period adjacent row change analytics specifically on museum visitor footfall data (visitor counts or dwell minutes) without any enrichment, forecasting, or external data dependencies. It is ideal for small to medium datasets (up to 200 records) where the goal is purely descriptive row-level delta computation. Prefer alternatives if you need forecasting, outlier detection, histogram binning, or data from non-museum domains.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T00:53:05.665Z","isFirstParty":false,"canonicalSlug":"museum-footfall-adjacent-row-changes-a5cc6736"}