Meeting Room Occupancy

This demo visualizes Taika Meeting room occupancy for the full month of January 2026. Data shows occupancy status during Finnish office hours (Monday-Friday, 8:00-17:00 EET/EEST). All data is loaded directly from Parquet files using hyparquet.

About This Demo

This visualization shows the occupancy pattern of Taika Meeting room during January 2026. The calendar displays:

• Office Hours Only: Monday through Friday, 8:00 AM to 5:00 PM (Finnish time - EET)
• OccupaGradient Heatmap:
  • 🟢 Green (0-25%) = Mostly vacant
  • 🟡 Yellow-Green (25-50%) = Low occupancy
  • 🟠 Orange (50-75%) = Moderate occupancy
  • 🔴 Deep Orange (75-100%) = High occupancy
  • 🟩 Green = Less than 50% occupied (mostly vacant)
  • ⬜ Gray = No data or weekend

Data Source

• Device: Taika Meeting room occypancy (TSPR04 presence sensor)
• Measurement Type: measurementOccupancyStatus (binary: 0 = vacant, 1 = occupied)
• Time Period: January 2026 (full month)
• Update Frequency: Real-time presence detection

How It Works

The demo loads hourly Parquet files for each business day in January 2026 using batch loading (all hours per day in parallel for faster performance). It filters measurements for the Taika Meeting room device and calculates occupancy rates based on time duration - each status persists until the next measurement, providing accurate time-weighted occupancy percentages.

All data processing happens in your browser using the hyparquet library - no server-side processing required!

See the Data API documentation to learn how to access this data programmatically.

Build This with AI

Want to recreate this demo or build your own variation? Copy this prompt to your AI coding assistant (Claude, ChatGPT, GitHub Copilot, etc.):

You are a meeting room analytics expert. I need you to build a meeting room occupancy calendar visualization.

Step 1: Read the Haltian IoT data documentation at https://developer.haltian.io/haltian-iot/apis/data-api/ to understand the data model.

Step 2: Carefully study the complete implementation at https://developer.haltian.io/demos/meeting-room-occupancy/ including:
- All technical details in the "About This Demo" section
- The time-weighted occupancy calculation algorithm in the source code
- The data loading and visualization approach

Step 3: Build me the same occupancy calendar using your preferred technology stack (Python, JavaScript, R, or other).

Key requirement: The occupancy calculation MUST be time-weighted (status persists until next measurement), not count-based. This is critical to get accurate results.

Feel free to modify the prompt for different time periods, meeting rooms, or analytics features!