| | --- |
| | 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., `-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. |
| | |
| | --- |
| | |
| | ## 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. |
