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