| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - telecommunications |
| - mobile |
| - internet |
| - 4G |
| pretty_name: Synthetic Mobile Performance Dataset |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
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| # Dataset Card: Synthetic Mobile Network Performance |
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| **Dataset Description** |
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| This dataset contains synthetically generated mobile signal measurements designed to mirror real-world data in the UK. The data represents geolocated signal quality metrics from mobile devices, capturing a range of environmental and temporal conditions over several months in 2025. |
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| All data has been anonymized, aggregated, and processed to protect user privacy. The synthetic dataset has undergone pre-processing, including coordinate validation and outlier removal. |
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| **Fields** |
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| The dataset contains the following columns: |
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| * **timestamp**: (date) The date and time of the measurement, recorded in UTC. |
| * **unique_cell**: (string) A unique identifier for the mobile network cell to which the device was connected. |
| * **measurement_type_name**: (string) The type of measurement recorded. This column appears to be null in the provided sample. |
| * **in_outdoor_state**: (string) A categorical label indicating the predicted environment of the device, with values such as "Surely Indoor," "Probably Indoor," and "Surely Outdoor." |
| * **value**: (float) A numerical value associated with the measurement. The specific KPI this represents is not defined. |
| * **latitude**: (float) The latitude of the device's location at the time of measurement. |
| * **longitude**: (float) The longitude of the device's location at the time of measurement. |
| * **signal_level**: (float) The received signal strength, likely measured in dBm. |
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| **Potential Uses** |
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| This dataset is suitable for a variety of analyses, including: |
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| * **Network Coverage Mapping**: Visualizing signal strength across different geographic areas to identify zones with strong or weak coverage. |
| * **Performance Analysis**: Correlating signal quality with factors like location (indoor/outdoor), time of day, and cell tower. |
| * **Mobility Pattern Simulation**: Understanding how user movement impacts network performance. |
| * **Machine Learning Model Training**: Developing models to predict signal quality based on location and environmental factors. |
