This qualitative knowledge makes sense of the quantitative evidence and often contains the insights you need to make a decision based on the findings. The more people you involve, the more reliable the consensus findings become.

Here’s an example of a visually distinct correlation between two datasets for Great Manchester which needs some local qualitative knowledge. We were thinking about this week’s debate over the status of some qualifications being downgraded in school league tables, and whether employment data could indicate any relationship between school attainment and the value delivered back to the surrounding community.

In an exploratory way, we looked at data for the city for residents with level 4 qualifications and above (level 4 is one higher than A levels, e.g. diplomas, professional certificates, on up to HNDs, degrees, masters and so on). Almost accidentally, we compared this city-scale data pattern with residents employed in manufacturing. The two maps are below:

Percent residents with level 4 qualifications & above (darkest shades = 30%+)

Percent residents employed in manufacturing (darkest shades 20%+)

If we image a slice of pie extending south from the centre of the city, the lack of manufacturing employment and the relatively high level of qualification is visually evident (and yes, Moss Side is a blob in the middle of the pie slice – but the inverse relationship between the two phenomena seems consistent even here). The tools we’re building allow you to add markers and annotations to illustrate something exactly like this, but we’ll have to make do with pie for now.

So does manufacturing still offer relatively high levels of employment to those workers with qualifications below level 4, as we might have expected 30 years ago perhaps? Or is it more significant that higher-qualified people are disproportionately likely to live south of the centre and be employed in the service sector?

With more and better data, could we test the hypothesis that qualifications relevant to manufacturing and other local employers would add even greater value to the community than traditional academic exams – perhaps in the shape of a reduced benefits budget and related regeneration effects?

The truthful answer here and now is that I don’t know, but I bet among the residents of Manchester and equivalent cities the ‘soft’ knowledge exists to make perfect sense of these patterns, once we know they’re there, and to shape policy accordingly.

Any insight to share? Let us know below. Meanwhile it’s back to the coding coalface…

It’s official – sustainability is no longer a brand issue or evidence of corporate social responsibility: it’s a key business driver.

We’re already directing our insight and analysis offer towards helping organisations make well informed long-term sustainability decisions, but it’s always nice to have your convictions reaffirmed. Companies aren’t investing in this field because they want to be seen to be green, but because it makes sound business sense, and that’s what will keep it going.

So here’s a short selection of views I’ve come across about why sustainability is central to business. What also emerges, of course, is that business is central to sustainability. Green campaigners have kept the torch alight for decades, but it will take the mass momentum of the business economy to make a difference at the scale that’s required.

“The scales literally fell from my eyes, and it was so obvious that any business which focuses on the things that are important to its customers will not be wasting resources anywhere in its business, as any unit of waste is one less unit of resource. The whole logic that smart business is a sustainable business came through in spades. Never mind the science; what business is about is being ruthlessly effective delivering value to our customers, and that must be about stripping out inefficiency of any kind. Why would you knowingly waste the resources of the Earth?” Ronan Dunne, Chief Executive, O2 UK

“For Addison Lee, it’s always been about the commercial benefits – more than the green agenda. We made the company more efficient, and as a result, its carbon footprint kept shrinking. It was a series of steps, starting with simple things like fitting windows with high insulation ratings. Our ‘green’ action plan said to put better windows on the first floor, but the cost benefits were so clear that we did the whole building.” John Griffin, CEO, Addison Lee

“Doing the right thing on climate change saves money, retains customers, creates new market opportunity and takes you beyond just compliance. It reduces your risk exposure and reduces risk to shareholders.” Dr Jonathan Foot, Chief Environmental Officer, EDF Energy

“Think about it…if we throw it away, we had to buy it first. So we pay twice, once to get it, once to have it taken away. What if we reverse that cycle? What if our suppliers send us less, and everything they send us has value as a recycled product? No waste, and we get paid instead.” Lee Scott, Chief Executive, Wal-Mart

“Business is the force of change. Business is essential to solving the climate crisis, because this is what business is best at: innovating, changing, addressing risks, searching for opportunities. There is no more vital task.” Richard Branson

“It is not the strongest species that survive, nor the most intelligent, but the ones who are most responsive to change.” Charles Darwin

Ruth Keily

Anything which will move us towards investment spending again after this prolonged period of post-election purdah must be welcomed, but even the least politically minded among us must wonder how it is all going to work.

The idea, as we know, is for the Fund to “re-balance” the economy of England (the devolved governments and London will be funded separately) by funding projects which will “encourage private sector enterprise” and “create additional sustainable private sector employment”, which have been proposed by local enterprise partnerships (LEPs) or directly by private sector organisations.

What I hope this consultation exercise picks up is that any organisation responsible for setting local economic strategy and bidding for considerable sums of cash needs good quality information on which to base its decisions and justifications.

By definition, Lord Heseltine and his colleagues who will be assessing the LEPs’ proposals will also need to know whether the priorities set by business-led groups adequately reflect the overall needs of the community.

This isn’t to imply that local public-private groupings can’t do a better job than large and possibly less accountable regional development authorities: the freedom to operate within self-defined “economic geographies” and to prioritise what matters to them has clear potential to improve things.

But prioritisation, whoever does it, requires expertise and sound, reliable data. As we know from our work, the investment that’s urgently required in the east of a city may be entirely different to what’s needed in the west. Rural communities will need something else again. And new jobs need to be the right kind of jobs. We need to know before we decide.

The Fund is initially committed for only two years, though it may continue, which is more likely to encourage quick-win job creation schemes than building long-term resilience into our economy to meet challenges such as climate change.

I have great respect for anyone willing to work on a voluntary basis, presumably in addition to a demanding private sector job, to help manage a LEP. Time will tell whether the attractions of local participation tempt the brightest and best to do it: I hope so, since we will inevitably lose local knowledge and expertise with the disbanding of the RDAs and other quangoes.

For my money (and it is my money, as well as yours) I’d like to see some of the savings made in abolishing RDAs invested in the economic, social and environmental information made available to the public-spirited individuals who are expected to replace them. If we want accountable and transparent local development decisions from our LEPs, it’s a small price to pay.

Have your say on the Regional Growth Fund Consultation on the BIS website [external link] before 6 September 2010.

Ruth Keily

The analysis takes the number of individual shop premises in the town centres surveyed every six months by The Local Data Company, then visualises these numbers in three dimensions over a map of London’s West End and surrounds.

(Note that a similar analysis could also be done for total floorspace, but this one is for the number of retail units – giving rise to interesting peaks like the one for Brixton in the right-hand foreground).

We can see the highest peaks around Oxford Street and Knightsbridge, with notable neighbours going East to the City, north to Camden and Islington and a clear mountain range along the length of the King’s Road. Through the semi-transparent data layer we see the importance of the road network to peak retail locations, even in a city with a well-developed public transport infrastructure.

Also significant is the clear peak of retail density at the new Westfield shopping centre at White City, as new a feature as an Icelandic volcano emerging from the sea.

Not only are these peaks immediately identifiable by location, but the 3-D treatment makes a map legend almost unnecessary, and makes comparison of relative heights (i.e. retail densities) at different locations immediate and straightforward. The simple visual metaphor of ‘highs’ and ‘lows’ across a landscape perfectly complements our understanding.

The underlying data here, mapped and available online with vacancy rates, churn, multiple / independent mix, floorspace and more for 1,300 UK town centres via LDC’s Town Centre Intelligence (powered by Geofutures), is acknowledged to be the most up to date available.

But actually I like this map for what it shows us about all data – that if we put information on a map we reveal its highs, lows and hidden insights.

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?

Want to read more of Simon Lewis’ article? Please register here and we’ll email you the rest of this article. We never share your data and you can unsubscribe to Geofutures updates at any time.

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.

What happens when oil is too expensive to transport food around the world?

To avoid famine and food conflicts‚ we need to plan to re-localise our food economy. This map is part of that process – showing the food requirement ’footprints’ around settlements in SW England.

Use the pan and zoom controls to view your chosen area‚ and read more about how Geofutures is mapping our food future below.

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.