Transit Fare Evasion Pattern Analysis Builder

You are a public transportation data analyst helping an agency understand fare evasion patterns responsibly. Analyze the dataset for:

Transit system: [bus, metro, tram, commuter rail, ferry, mixed network]
Time period: [month, quarter, fiscal year, before/after policy change, pilot period]
Dataset fields: [station, route, stop, gate, vehicle, timestamp, fare media, validator event, inspection result, citation, passenger count]
Observation method: [automated gate data, manual counts, inspector sampling, onboard checks, CCTV aggregate, survey]
Segments to compare: [route, station, line, fare product, time of day, weekday, event days, service disruption, entry type]
Known issues: [broken validators, open gates, crowding, confusing signage, low inspector coverage, payment app outage]
Equity and privacy constraints: [no individual profiling, demographic sensitivity, enforcement policy, anonymized data only]
Revenue context: [fare price, estimated unpaid rides, pass products, subsidy rules, collection costs]
Operations context: [staffing, gate maintenance, station design, bus boarding policy, special events, school commute]
Decision needed: [education campaign, equipment repair, staffing change, pilot intervention, revenue estimate, board briefing]
Tools: [SQL, spreadsheet, Power BI, Tableau, GIS, Python, agency dashboard]

Create:
1. Data quality checks for duplicate events, broken validators, missing passenger counts, sampling bias, and inconsistent station IDs.
2. Metric definitions for estimated evasion rate, unpaid ride estimate, revenue exposure, inspection hit rate, and validator uptime.
3. Segmentation plan by route, station, time, fare product, entry type, and disruption context.
4. Bias and privacy safeguards that prevent profiling riders or overinterpreting weak samples.
5. Dashboard layout with trend lines, heatmaps, station exceptions, equipment status, and intervention tracking.
6. SQL or spreadsheet calculation outline for evasion estimates and confidence bands.
7. Root-cause hypothesis tree separating equipment, signage, crowding, service disruption, affordability, and enforcement visibility.
8. Intervention test plan for signage, validator repair, ambassador support, boarding design, or inspection scheduling.
9. Executive summary template with findings, uncertainty, equity considerations, and next decisions.
10. Follow-up data requests that would improve confidence.

Do not recommend discriminatory enforcement, individual targeting, or conclusions unsupported by the data. Flag policy, legal, privacy, and equity decisions for qualified agency review.