An exciting new project is underway for Geofutures: working with the Centre for Sustainable Energy, we’re modelling and mapping residential heat demand for Regen SW, the renewable energy agency for the south-west of England.

The aim is region-wide insight into the potential for renewable and low-carbon heat generation and distribution, with outputs at sufficiently fine scales to allow users to identify individual buildings and groups of buildings which could benefit from heat distribution installations.

It’s set to be the most advanced heat mapping exercise undertaken in the UK to date, building on CSE’s proven expertise in modelling heat demand in London, Bristol and West Sussex, with the addition of Geofutures’ experience in using GIS to analyse fine-resolution data, as well as simply visualising results.

An important benefit of starting with data at building level is the ability to aggregate results upwards without losing accuracy, still maintaining the ability to drill down to fine scales at chosen locations. Other studies have started with data generalised for hundreds of addresses, which can only output heat demand results for broad areas. For local heat distribution to become a reality, we need data for highly localised decision making.

See the website of the Centre for Sustainable Energy.

Yet here I am thinking about companies using geographic information science (GIS) and I can’t avoid those clichés. Our industry is certainly becoming more mature – maybe even mainstream – but talking to clients across every sector, it’s clear that many organisations could do much more with their data using GIS. Many could still take it to the max, as it were. Their data is just not feeling the burn.

So I’m going to take on the role of personal trainer (not an everyday experience) and explore why this is so, what most public, private and third sector enterprises are doing with GIS now, and how much more they can achieve.

It’s not generally a want of investment. Considerable sums are spent on people, data, hardware and software that make up an in-house GIS function. Companies who make this investment often do so because they need to perform fairly rigidly defined tasks, based around routine data-management tasks. This makes perfect sense, but in these circumstances it’s easy to ignore the full potential in both the data and technology.

So, like a personal trainer, the point of a specialist adviser like Geofutures is that we are able to keep our eyes on the prize. It’s no disrespect to an in-house GIS officer who is head down keeping the wheels turning if we come along and offer new ways to push the software, hardware and data of GIS to deliver much more than is conventionally possible.

And often it’s the data, rather than the technology itself, which holds the key to unlock hidden value, identify new revenue streams and streamline processes.

Is your business in this position? Here’s a little test. The paragraphs which follow describe the most common GIS functions within an organisation. Is yours doing any or all of them?

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It’s nice to have your career choice reaffirmed from time to time. I did feel a bit of that special warm glow this month at a great event organised by our clients Ipsos MORI to launch their National Indicators Mapping Application (NIMA), developed by Geofutures.

What set me glowing? Being reminded that a picture is worth a thousand words (the bumper-sticker of GIS professionals everywhere). In fact it was two pictures, so maybe that’s two thousand words. Here they are:

Twin images of perception data in North London from Ipsos MORI's NIMA app show strong correlation

The audience, a who’s who of local authority research heads and their suppliers, got a whistle-stop tour of all Ipsos MORI’s work in this important market, and NIMA was centre stage. All authorities now have to poll their electors on 198 National Indicators of satisfaction and the factors affecting it, and NIMA provides instant online insight into the results. Side-by-side ‘double view’ comparisons of maps like these are a key part of the application.

What these two visualisations show are three key reasons why mapping these kinds of data is such a compellingly good idea: the correlation of the two hotspots, the fact that both are visible despite the ward boundaries, and the geographical context that the map offers.

So firstly, the two maps describe responses to two different survey questions: overall satisfaction/dissatisfaction with the area as a place to live on the left, and perception of social cohesion on the right. Only by locating these respondents on the map in a statistically smoothed data landscape can we so immediately see the close spatial correlation of the low-perception hotspots. For a local authority looking for ways to focus resources in hotspots of this kind, to deal with specific issues where they are being experienced and to maximise policy effectiveness, the benefits are obvious.

And if your local authority is only offering National Indicator results by ward, IMHO you want to be asking how efficiently they are spending your council tax. If the same results had been aggregated by ward, the hotspots would disappear altogether. It certainly wouldn’t be evident that dissatisfaction and issues of social cohesion were concentrated in one area which impacts sections of four separate wards. Tying data to actual location, rather than some arbitrary zonal boundary, is a key benefit of GIS analysis. Cue warm glow.

And a map does another simple but fundamental thing: it shows what’s on the ground in the hotspot locations. These two hotspots have a major roads running through them. Does this mean we’re looking at a pocket of high-density roadside dwellings choked with exhaust fumes, whose residents are struggling with low incomes, transient neighbours and the social issues that go with them? The sort of neighbourhood where local authorities really need to send their outreach workers?

Intriguingly, no. Zoom into an aerial image of Hendon Wood Lane and you’ll find leafy open spaces, substantial detached houses, gardens and even a smattering of swimming pools. This is where the hotspots of community dissatisfaction and perception of poor social cohesion are undoubtedly to be found, but not I suspect because of social deprivation.

Again, a map visualisation proves its worth, hinting at a fascinating little area for further exploration.

For the last six months the Geofutures development team have been working on a stimulating project in partnership with The Local Data Company (LDC). Town Centre Intelligence is a web-based application designed to provide insight and information on the economic health of town centres.

LDC collects a huge wealth of data on retail premises, obtained and updated directly by their own team through street surveys. Until now the company has had a thriving business supplying clients such as Yell with data in the form of database extracts, but they rightly identified the opportunity to create even more value from this enormous resource.

Town Centre Intelligence provides subscribers – town planners, town centre managers, retailers, property investors and master planners among others – with all the retail data on 1,300 town centres and the means to sort, search and visualise it via a user-friendly map based interface. Delivered over the web, TCI delivers context-specific information on town centre performance at successively fine scales at the click of a button.

TCI offers instant online retail data, including the independent / multiple mix, shown here for Edinburgh

Geofutures’ part of the game has been the development of this online data platform. In building it, some of the thornier challenges we’ve faced have involved the database structures which underpin the application. Dealing with spatial data is our stock in trade, but it’s certainly not for the fainthearted, as some of the issues set out below will illustrate.

Creating locations

TCI is based on The Local Data Company’s data, but it also incorporates town centre boundary data from the Dept for Communities and Local Govt (CLG), and floorspace information from the Valuation Office (VOA). Look at any three organisations’ data and you’ll see that there is no such thing as a universally accepted address standard in the UK, notwithstanding BS7666, PAF and Address Point. None are wrong, they are just all slightly different, and this is the issue we addressed (no pun intended) with bringing these three sources together.

The key thing they have in common is that they refer to a place on the ground (with a few exceptions for things like house boats).  Instead of trying to match between the sets, we allow a point on the ground to have multiple addresses and we match to the point. With the volumes of data involved (some 300,000 business premises records in total) we needed to build a specialised ‘data cleanser’ application to perform these matches, which also allowed us to add non-address attributes such as floorspace to these points.

You can’t provide a picture of UK town centre retailing without dealing with shopping centres, of course. In each shiny mall lurks an addressing hornets’ nest all of its own. Multi-level, multi-concession shopping centre addresses bear little relation to normal addressing, and being privately-owned estates, collecting data and taking photos is often restricted.

Beyond this, we have to deal with the granularity of different types of address. A department store and a shopping centre may both contain multiple businesses but these are treated differently in different databases: the VOA may match on one level and the Ordnance Survey on another level. TCI offers unique added-value information such as churn rates of retail premises. This calculation is deceptively complex anyway, and to achieve this within acceptable bounds of accuracy, TCI has to recognise different addressing schemas and calculate churn rates accordingly.

Creating ‘towns’

There’s lots more elsewhere on this site about the Geofutures project to define town centres for what’s now CLG (previously ODPM, DETR and DoE; it was a long project). The need for it arose because the definition of a town centre – precisely where it begins and ends – depends upon whom you ask, so no consistent and comparable boundaries could be drawn.

This was significant when the health of town centres appeared to be under threat from out-of-town retail parks, and the success of planning changes to improve this had to be evaluated against standardised boundaries. These were created by tying multiple relevant datasets to town centre locations, creating an Index of Town Centre Activity based upon economic activity, property and diversity measures, and deciding a nationally consistent threshold value which would delineate every boundary.

The boundaries are used in TCI to allow like for like comparisons between town centres (London and other major cities comprise many smaller centres, for example, and only retail data within the government boundaries are included in the application). This too requires a lengthy data matching process, using what us GISers call ‘point in poly’[gon] to link locations to towns.

Generating statistics

Town centres are vibrant, dynamic things, and to be useful to those assessing and planning them, TCI has to allow for changes over time. Towns change size, both due to physical changes in their fabric, and due to shifting town centre boundaries based on their changing index of activity (see above). As the boundary moves, a retail location may move into or out of the town centre.

The size of individual retail premises may also change due to extensions or merging / de-merging with adjacent premises. Independents and multiples are analysed and compared within TCI, including data on independents which become multiples the moment they open a second shop. All of these flexibility requirements can be met with the right data structure, but reaching this point has sometimes been an interesting journey.

In human thought processes, we move between wide helicopter views and fine-scale information all the time. For a tool to aid this process, we need it to aggregate data for us and then break them down again. The magic is in how we expand out into huge arrays of data which lend themselves to statistical modelling, and then aggregate the data back into more manageable volumes that are quick enough for downloads and interactive analysis through the web interface.

There’s more about TCI here, or please comment on this article below.