I am pleased to share one of my recent developments: RF KPI Heatmap, an application built with Python and Dash to simultaneously visualize and analyze key LTE mobile network performance indicators on an interactive map.
The solution displays three essential KPIs for each network cell:
- Downlink PRB Utilization, represented by a short-range sector.
- Downlink User Throughput, represented by a medium-range sector.
- Average RRC Connected User Load, represented by a long-range sector.
Each KPI is identified by a different sector length and border style, while colors provide a clear indication of its performance level. By hovering over each sector, users can review the KPI value, performance range, selected period, and number of aggregated records.
Key features include:
Data upload from CSV and Excel files.
Simultaneous overlay of multiple KPIs on the same network cell.
Geolocation using coordinates and azimuth data stored in a SQLite database.
Filters by month, week, day, custom date range, and specific cells.
Aggregation by average, median, maximum, minimum, or latest record.
Automatic correction of numeric values fragmented by regional formatting.
Visual performance classification using thresholds and color coding.
Optimized polygon grouping and interactive hover areas for improved performance.
A modern, responsive dark-mode interface designed for technical network analysis.
This development aims to transform large volumes of technical data into an intuitive visual tool that helps identify sectors with high resource utilization, low throughput, or elevated user load.
It combines data analytics, software development, geographic visualization, and mobile network engineering to support optimization processes, performance monitoring, and data-driven decision-making.
Tool:
LinkedIn: 