Geospatial Analysis of Mobile Telecommunications Signals

Mobile Telecommunications

In large capital investment projects it is important to gain access to reliable data and analytical evidence to support investment decisions.


Data records collected to assess the quality of mobile telecoms signals along the national railway network


Kilometres of the railway network mapped with a quantitative and qualitative assessment of 4G, 3G and 2G mobile telecoms signals


Of the railway network in the UK is planned to benefit from over £1.3B in funding for new mobile telecoms technology


Over the next five years the UK government plans to invest over £1.3 billion in new telecoms equipment to maximise mobile phone coverage across the railway network. Our client is responsible for providing the railway industry with the telecoms connectivity that is essential to the safe, reliable and efficient operation of the national railway network.


In the process of planning for investments in new mobile telecommunications equipment our client needed to understand which geographical locations along the railway network could benefit most from new investments. In the past, the client would map the network using specialist equipment mounted on the roof of a train carriage, but this information was biased when assessing the quality of signals received by passengers in the train, or how mobile phone coverage differed across the major Mobile Network Operators (MNOs).


Shiny is an R package that makes it easy to build interactive web apps straight from R.

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Our team designed and built a interactive geospatial analytics tool that collected data from passengers travelling on the railway network. We combined passenger data with open-source data from the Office of Communications (Ofcom) and proprietary data from the client. This combination of data sources provided us with a unique opportunity to map the strength and quality of mobile telecoms signals across 35,200 kilometres of railway track and 152,771 square kilometres of land. We used a combination of techniques, including statistical signal processing, clustering algorithms, geospatial and spatiotemporal data analysis to extract intelligence from the data and communicate evidence to the client in a simple and easy to understand format.


By combining data from a variety of sources our team was able to build a reliable solution to assess the quality and strength of mobile telecoms signals received from specific MNOs in specific geographical locations in the United Kingdom. Our solution is now helping our client gain an understanding of where new telecoms equipment needs to be prioritised to improve the quality of mobile telecoms services for passengers travelling on the railway. This is also going into the governments wider strategy of building smart and connected cities.