license: cc-by-nc-sa-4.0
Dataset Overview
This dataset contains time-stamped spatial tracking records collected from tagged entities (e.g., wearable tags, assets, or devices) operating within a monitored environment.
Each row represents a single localization event captured at a precise moment in time, including 3D position coordinates and device status information.
The dataset is inherently temporal and spatial, making it suitable for trajectory reconstruction, movement analysis, and time-based behavioral studies.
Core Characteristics
- Event-based structure: each record is an independent positioning event.
- High temporal resolution: timestamps include milliseconds.
- Spatial awareness: positions are provided in Cartesian coordinates (x, y, z).
- Multi-entity tracking: multiple tags can be tracked simultaneously.
- Device health monitoring: battery level is recorded per event.
Temporal Analysis Potential
The time field enables rich temporal investigations, including:
Trajectory reconstruction
Ordering events by time allows reconstruction of movement paths for each tag.Speed and motion dynamics
Temporal differences combined with spatial displacement enable:- Velocity estimation
- Acceleration and stop–go detection
Activity and dwell-time analysis
Identification of stationary periods, frequent locations, and movement patterns.Event frequency and sampling analysis
Analysis of tag reporting rates, missing intervals, and signal reliability.
Spatial Analysis Potential
Using (x, y, z) coordinates, the dataset supports:
- 2D / 3D movement analysis
- Zone-based analytics (e.g., region entry/exit detection)
- Clustering of positions to identify hotspots or frequently visited areas
- Path similarity and trajectory comparison across tags or time windows
The constant z value in the sample suggests planar tracking, but the structure supports full 3D positioning.
Device and System Monitoring
battery_level enables:
- Device health monitoring over time
- Correlation between battery decay and data quality
- Detection of invalid or unavailable readings (e.g.,
-1values)
tag_id allows differentiation between multiple tracked entities.
master_id can be used to group tags under a common subject, asset, or system.
Typical Analytical Use Cases
- Indoor localization and tracking
- Human or asset mobility analysis
- Time-based behavior modeling
- Trajectory segmentation and clustering
- Anomaly detection in movement or device status
- Spatio-temporal visualization and dashboards
Scope
This dataset is designed for spatio-temporal analytics, not static positioning.
Its strength lies in enabling dynamic movement analysis over time, supporting applications in IoT tracking, smart environments, human–computer interaction studies, and behavioral analytics.