We’re doing some fairly hard-core product development at the moment, and when the results hit the beta release stage we’ll have the details here. As the process continues, it’s interesting how every conversation we have with clients, would-be clients and partner suppliers seems to point to the same needs.

The core issue is how clients and consultants can work together in a way which maximises the value of the undoubted expertise of the consultant, and which enables the client to do something practical with the outcomes.

The villain of the piece? It’s the consultant’s report. Hours and hours of work collecting information, hours and hours more beautifully summarising and recommending, then whump! it lands on a few desks.

Then what? Who has time to read it properly? Who has the knowledge to draw relevant conclusions? How does the client implement recommendations?

If input information is collected in isolation from client stakeholders, and the process does not build capacity within that organisation to do something tangible with the outputs later, the insight is lost.

No matter how relevant, incisive and accurate, no matter the time or cost invested, the wisdom contained in the report is confined for the lack of a means to share and reveal it.

As seasoned GIS users will know, presenting data on maps does reveal key phenomena in a way that a paragraph of description doesn’t, and we know leading consultants in multiple fields who are waking up to the power of this.

Even this isn’t enough unless the maps, the data visualisations and the expert interpretation are exposed to multiple pairs of eyes. We need the ‘wisdom of the crowd’ to drag all that valuable knowledge out into the world and make it work for us.

We need the tools to make this process simple and engaging, and both consultants and their clients will benefit.

Better get back to the coalface then. It’s reassuring to know all this work is in a good cause.

Ruth Keily

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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Oracle occupies an interesting position in the GI world: at once a significant challenge to established GIS vendors, and also challenged themselves by online mapping and data platforms. Would Release 2 of Oracle 11g make clear how they will move forward, I wondered?

The new spatial features in Oracle 11gR2 are certainly impressive. New functionality includes more complex network analysis including hierarchical shortest path analysis and a travelling salesman algorithm. It all felt good to me, perhaps because it makes the database technology seem more, well, GIS-like.

Enhancing usability

Speakers touched on some intriguing ways Oracle databases are powering applications with enhanced usability. Olivier Bucaille from Autodesk advocated using wizards and preset analytical environments to increase accessibility for every user, which I wholeheartedly agree with, and also 3D mapping and graphics at the building scale, which I’m less sure I support.

(I’m not a visualisation Luddite, but 3D bar charts have questionable legibility just used in a document – overlaid on a 3D map they can ask even more of the reader. In addition to this, there are significant statistical issues with taking aggregate data and assigning it to back down to the scale individual buildings in 2D or 3D, pretty as they may look.)

Evolution or revolution?

Anirban Acharya from Infotech Enterprises predicted that use of Oracle databases would increasingly become a key differentiator, and signal the end of proprietary database engines supplied by the main GIS suppliers. His suggestion that spatial databases were an evolutionary, not disruptive technology (like Google Maps was) got me thinking though.

My own talk had also recalled the transformative moment in 2005 when Google took web mapping mainstream. Disruptive indeed, though a highly positive development for our industry. But why did the development of Oracle Spatial (which we first used at much the same time) not feel similarly revolutionary?

Was it because database technologies were better known and understood than mapping platforms among IT professionals? Did Oracle in effect slide in spatial functionality when we weren’t looking? I think this is an illusion, and that Oracle Spatial was disruptive too. The monopoly of enterprise GIS vendors was broken when it arrived on the scene, ready to power online, desktop GIS applications.

And the spatial elements still present a knowledge gap for many highly savvy database developers. My own developers and GIS analysts continue to educate each other to span the divide between the database and the spatial model. Another speaker, Andy Spears from South Gloucestershire council, agreed; his central ICT team have had to learn spatial skills from scratch to support the technology.

So is Oracle Spatial the quiet, persuasive advocate prepared to play the long game from the inside to establish itself at the heart of GIS, to Google Earth’s loud, media-hungry revolutionary, delighted to stir things up as it (literally) takes on the world? And which one do we back in the race?

Mark Thurstain-Goodwin

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.