Commenting on the latest release of shop vacancy figures from our old friends at the Local Data Company, she suggested that “the horse has bolted” from some towns – she simply cannot see a retail future for them. The Local Data Company evidence suggests these are mainly second-tier centres, and it’s the second-tier off-prime locations within them which are hardest hit.

The conclusion Mary Portas suggests is that some towns need to rethink the model entirely. If they cannot compete as retail centres, their only hope for rejuvenation is other uses. So far, where this has happened it has tended to be leisure-led, hence closed shops re-opening as bookmakers, cafes and bars. Better than vacant shops, perhaps, but a major change in mood which can have anti-social effects if not managed effectively.

Are there other potential ways that town centre regeneration could happen? I was pondering this as I looked again at the location of twelve new English Enterprise Zones announced by PM David Cameron in August.

This is the second round of new Zones, and the first lot included ex-industrial heartlands like the Black Country and Tees Valley, true. But these latest Zones are not designed for redistribution of opportunity: last I heard, Oxford, Lowestoft and Hereford were not high on the list of economic blackspots.

A quick mapping exercise backs up that conclusion. The Income Score is part of the Index of Multiple Deprivation, and the higher the score, the higher the deprivation – shown here in the brown shades.

As you can see, the areas surrounding the new Zones are not strongly characterised by below-average incomes, with the exception of Warrington.

So the Zones have been decided for other reasons, and my bet’s on “because they are most likely to be successful in bringing in new industry” and that will be counted as a win for the policy. Places where industries are already doing well attract other industries, and tend to be where skilled employees can be found. Locating them next to transport hubs is helpful, and a bit of government subsidy just adds to the mix that’s already there.

If enterprise is being encouraged where it’s happening already, what chance have second-tier retail centres have to be regenerated? Just maybe, the clue is in the question. Enterprise Zones are being given lower business rates, reduced planning controls and superfast broadband. Could this be achieved for smaller-scale ex-retail locations too – and would this be effort better targeted at genuine transformation?

I imagine that retail premises redeveloping as small business offices, live-work accommodation or even light industrial and distribution units might have a more balanced economic effect on a town’s economy than a night-time entertainment quarter. And if Dewsbury, Dudley, Hartlepool, Margate, Stockport and West Bromwich are the towns suffering the worst retail vacancies in England, I can think of worse places to start.

Mark Thurstain-Goodwin

Individually, each change of tenant may have only a limited impact and certainly these new openings may be more welcome than empty shops. It’s only when the balance of a town centre’s characteristics tips beyond a certain point that we can see the whole local economy skewed towards night-time visitors, for example, which can then make the remaining traditional shops struggle even more.

Having worked in the town centres field since 1996 I’m aware of their pace of change over that time, and also of the need to track this kind of fundamental shift with consistent measurement based on well-defined parameters. To coin a phrase, we have the technology (and the data). The government halted the town centres statistical series a few years ago, but even better data is now available, requiring only an existing model to be refined and re-run.

If I were in the retail sector I’d certainly want to know how town centre developments are likely to perform, especially after the recent performance results from non-food retailers including HMV, Dixons, Thorntons and Game Group. The food sector seemed to have survived the snow and unemployment fears better, but then today Tesco announced a poorer than expected showing.

This, to be fair, it has attributed to its non-food business, although underlying growth in its food sales was also behind that of Morrison’s and Sainsbury’s. More intriguingly, its non-food sales were, by its own account, impacted disproportionately because Tesco has more large out of town stores than its rivals, which were more difficult to get to in December’s bad weather.

I’d be a fool to predict the imminent demise of Tesco but I do wonder whether out of town retail locations have finally succumbed to their Achilles heel: accessibility by car. All of these retail developments need people will drive to them. Snow and ice are clearly more of a barrier than high petrol prices now, but how far must these rise before a trip to a superstore is priced out of the market?

Mark Thurstain-Goodwin

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.

You couldn’t move for stories about retail vacancies derived from TCI data last week, and no wonder – our high streets have a gap-toothed look about them just now, and the information from TCI is really too good to ignore. See how the BBC covered the story here.

TCI allows easy (and statistically robust) comparisons between town centres – defined consistently across Great Britain by the government boundaries defined by a Geofutures methodology.

This reveals significant regional variations in the vacancy rate – southern towns and cities are still faring much better than their northern counterparts, where vacancy rates have doubled since mid-2008, while Wales and the West are performing better than average with only a 25% increase in the same period.

A similar pattern was revealed when LDC researched what has happened to empty Woolworths premises. About 70% of all the stores are still empty, but within this national picture, fewer than 50% of Greater London Woolworths premises have not been re-let, while 90% of those in north-east England are still vacant.

Of those Woolworths stores which have been re-let or are in negotiation, LDC found 30% becoming supermarkets and 42% opening as discount stores including 99p Stores, Poundland and Bargain Madness – an interesting trend which will have long-lasting impact on the profile of town centre street scapes.

We’re continuing to work with The Local Data Company to mine more insight from the data. A special area of interest is the difference in performance between traditional high streets and shopping centres, where trends like the tide of discount stores in lower-rent locations may prove to be highly relevant. We’ll have an update soon.

We were chuffed to see that the Financial Times used this information as a source for a headline story on 16 May 2009, also using the data to highlight the worst-affected sectors – predictably perhaps, these are fashion, electrical, furniture DIY and jewellery retailers.

The application delivers constantly-updated data on 675 town centres across Great Britain, giving instant insights to planners, developers and investors into the retail mix and the health of the high street.

TCI highlights that the last two quarters have seen fast growth in the rate of shop vacancies, with particularly high levels in the north east of England and the West Midlands, as the contour map created by Geofutures for the FT illustrates.

Map of retail vacancies Q4 08-Q1 09 prepared by Geofutures for the Financial Times

The specific effects of the credit crunch can be seen in the nature of these vacancies: the overall closure rate has not increased significantly, but the numbers of new openings – often reliant upon bank finance – have shown a sharp reduction.

The main idea behind TCI is the ability to manage vast volumes of data seamlessly, drilling down through these kinds of numbers, comparing town centres like for like and at successively fine scales. For more information about the product, please visit The Local Data Company website.

See the news story on ft.com (registration required).

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.