Maps to support the business case

The first mapping exercise has to be to understand what the broadband landscape looks like today.  It is critical to base this on data from primary resources – the incumbent operator, the cable companies, and so forth – and to seek or generate further data sets to validate the carrier’s data.  This is equally true for an organisation based within the community as it is for an external organisation considering an investment in an area they know little about.

In order to test the level of competition for a new broadband network it is necessary to plot existing broadband services and the number and type of operators.  In the UK that typically means mapping the ADSL performance for BT services; the extent of Virgin Media’s cable network; and the number of operators unbundling the local loop.

The provenance of the data is important. There have been many attempts to model broadband speeds based on ADSL performance curves from manufacturers and GIS (graphical information system) tools that calculate the radial, as-the-crow-flies distance from the telephone exchange.  Some of these have tried to build in factors for guessing the true cable length, quality and so on but at the end of the day they are just increasingly smart guesses – and this becomes very clear during a mapping exercise.  Estimating broadband speeds based on radial distance will create nice, uniform shapes on a map from which simply doesn’t match with reality.

Mapping quality empirical data provides a more organic image of broadband performance which starts to mould itself to the geography and topography of the area.  From this it’s possible to build a narrative to link the cold data with the tales of broadband woe.  (Samknows is the main source of such information in the UK).

A variety of mapping techniques can be useful in order to gain the fullest understanding.  As well as maps that blanket fill a postcode polygon with traffic light colours to represent poor, mean and good broadband speeds, it’s worth considering other techniques such as contoured heat maps – while it’s harder to say precisely what the speed is at a given location, it does provide a much richer picture from which the broadband landscape can be described.

It immediately becomes clear that the underlying data is not based on a simple model because it does not produce the neat conical contours of a radial guesstimate.  A closer inspection begins to show how the broadband landscape is affected by the contours of hills and valleys, and by man-made features like railways.  These can provide indicators of the problems past infrastructure builders have had to grapple with, and the potential opportunities an alternative approach might bring.

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Map A (above) was generated from broadband data in Oxfordshire. There had been long established rumours that broadband in parts of central Oxford were slow, and the reasons given seemed perfectly plausible but unproven. The story was that some phone lines had had to take a long, circuitous route skirting around the old Morris car plant, which made them too long to support a good broadband service, even though some of the homes and businesses affected were just a few hundred metres from the present-day telephone exchange.

A glance at the new map clearly shows a “ghost valley” of poorer broadband to the north east of Oxford.  While the now BMW car plant is much more compact, the data appears to support tales of the city’s industrial past, still haunting one of the world’s most important knowledge centres.

Slightly to the north of the city is Oxford Airport. Following the northern perimeter fence, the map predicts the existence of an “ox-bow lake” of poor broadband coverage which is perfectly reasonable – it’s unlikely that cables will take the short path across the runway.

Other supporting data sets

Technical broadband data is but one aspect, but other data sets can provide important contributions to the business case.  Another approach to consider is the impact that property density has on the cost of deployment.  Data from the Office of National Statistics can prove very useful here.

Map B (below) was generated from a combination of land use and population dataset from the ONS for the North West of England, and attempts to assess the “mean distance between neighbours” as a proxy for the cost of the civil works required for a fibre-optic network build.

In this case, blue indicates areas where premises are typically farther apart and will therefore cost more to deploy using fibre alone, while green areas indicate areas where homes are closer together and the cost of deploying fibre will typically be lower.

Maps could also provide clues about the kinds of services that might appeal to the community, and therefore drive take-up.  Only when combining such data with the previous technical mapping is it possible to properly understand the business case for investing in a new broadband infrastructure.

It is quite possible, for example, to find a community which is currently under-served by first-generation broadband, and which is sufficiently densely populated to suggest a lower cost of deploying fibre, but which has little interest in adopting new services.

There are a number of possible datasets available that can provide clues, such as the ONS output area classification system and perhaps more usefully the eSociety classification system from the Centre for Spatial Literacy.

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By Adrian Wooster

This article originally appeared in Beyond Broadband: Giving our Communities the Digital Networks They Need.