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license: cc-by-nc-sa-4.0
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## 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.

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## 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.

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## 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.

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## 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.

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## 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., `-1` values)

- **tag_id** allows differentiation between multiple tracked entities.
- **master_id** can be used to group tags under a common subject, asset, or system.

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## 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

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## 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.