Public Park Attendance Weather Impact Analysis Builder

You are a parks and recreation data analyst studying how weather affects park attendance and operations. Build an analysis plan for:

Park system context: [single park, city park network, regional park, waterfront park, sports complex, trail system]
Attendance data: [gate counts, parking counts, Wi-Fi counts, program registrations, manual estimates, concession sales, facility bookings]
Weather data: [temperature, rain, wind, humidity, heat index, air quality, storm alerts, daylight, snow]
Time period: [season, year, multi-year, event series, school holiday period]
Operational questions: [staffing, restroom cleaning, trash collection, programming, concessions, maintenance, safety messaging]
Segments needed: [park, entrance, day of week, hour, program type, event, school calendar, holiday, neighborhood]
Known confounders: [special events, school breaks, construction, closures, sports schedules, marketing campaigns, transit disruption]
Data quality issues: [missing counts, sensor downtime, estimated attendance, cancelled events, inconsistent weather station]
Tools available: [spreadsheet, SQL, Python, R, Power BI, Tableau, GIS]
Stakeholders: [parks director, operations, programming, maintenance, finance, city council, concessions vendor]
Privacy and policy needs: [no individual tracking, public data rules, accessibility, heat safety, emergency messaging]
Output format: [dashboard, memo, staffing model, seasonal forecast, council presentation]

Create:
1. Data inventory and joining plan by date, hour, location, event, and weather station.
2. Cleaning checklist for missing counts, closures, outliers, duplicate events, and weather gaps.
3. Metric definitions for visits, program attendance, no-show rate, capacity use, weather-normalized turnout, and service demand.
4. Exploratory analysis by temperature band, precipitation, humidity, wind, air quality, weekday, holiday, and event type.
5. Confounder controls and caveats so weather is not blamed for every attendance change.
6. Forecast or scenario model for staffing, cleaning, concessions, and program go/no-go decisions.
7. Dashboard layout with trend lines, heatmaps, weather bands, map views, and operational alerts.
8. Recommendations for programming, maintenance, communications, and heat or storm readiness.
9. Executive summary template for non-technical stakeholders.
10. Data governance and privacy notes.

Do not infer individual behavior from aggregate counts. Clearly separate correlation, operational judgment, and verified causal evidence.