2009 July | Geofutures

Archive for July, 2009

Foodsheds, the mashup

Thursday, July 23rd, 2009

Fresh off the Geofutures GIS mashup assembly line is an interactive version of the maps we produced for the ‘foodshed’ surrounding Totnes and its neighbouring towns in Devon. This is a static image – please link through to see the functioning mashup.

These maps are the results of our food security analysis published together with the Transition Network this month – you’ll find details of our methodology and a link to the full report in our earlier post.

The analysis is based on Defra land classifications, a permaculture model and a ‘food zoning’ model based on perishability and labour intensity, which places fruit and vegetable growing areas closest to the town, followed outwards by cereals and other food crops, dairy and beef, and finally sheep farming on the poorest soils furthest from the town.

Have a play and see how you can zoom in to see the component parts of the foodshed. Doing so against an aerial photography background brings home how a relocalised food economy might look around this classic market town.

Of course, the analysis raises many more questions: about the overlap between towns’ foodsheds, the lack of sufficient woodfuel and the urgent need for more fine-scale land use data among other issues. As Transition founder Rob Hopkins wrote in his blog, food scarcity is how wars start – unless, we hope, we’ve done much more analysis of this kind to plan for it effectively in advance.

It’s a good example of how GIS, spatial analysis and mapping data can bring possible future scenarios to life, igniting debate and making results widely accessible to experts and non-experts alike. For us, it’s satisfying applied to any sector, organisation or data type, but food security analysis probably has the widest implications of anything we do.

Tags: food security, mapping, Sustainability
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Hotspots leave a warm glow

Friday, July 10th, 2009

Mark Thurstain-Goodwin enjoys seeing Ipsos MORI put spatial data in front of local authorities

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.

Tags: clients, data, GIS, mapping
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Retail vacancies soar, TCI reveals

Friday, July 10th, 2009

Town Centre Intelligence (TCI), the new urban data management tool we developed for The Local Data Company, reveals that UK retail vacancy rates rose from 4% to 12% in the 6 months to March.

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).

Tags: clients, products, town centres
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Food footprints: re-localising UK food supply

Wednesday, July 8th, 2009

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.

The UK’s future food security depends upon domestic farmers‚ the market network and some clever use of data. Planning for our food future needs to start now.

In December 2008, Geofutures founder Mark Thurstain-Goodwin told the National Food Markets Conference in Blackpool that the UK’s food security is more precarious now than before we faced the WW2 U-boat blockade.

We are heavily dependent on the global food economy. When oil supplies diminish and prices inevitably rise in future‚ we will no longer be able to afford to import our foods. The answer must lie in re-localising our production of food‚ fibre and fuel‚ but as Mark argues‚ there are ways in which we can use data to hugely improve how efficiently this is done. The map here is part of that analysis.

Peak Oil and food security

Many argue that Peak Oil (the time when extraction from the world’s oilfields hits its physical maximum‚ beyond which it can only diminish with corresponding increases in price) is imminent‚ or even past. The time when oil prices start to affect food supplies doesn’t begin when oil runs out completely‚ but long before that‚ when oil-fuelled global distribution becomes increasingly uneconomic.

This is a central concern of the Transition Network‚ the fast-growing movement enabling communities to plan for increasing their resilience for a post-oil economy now‚ including re-localising food production.

Calculating food footprints

A food footprint is only a very basic representation of the land required around a town to feed its population‚ based on the calculation below.

The map above illustrates circles around communities with a population of over 800, and we can view them as ‘overlapping’ i.e. the absolute size of the land required by that community irrespective of whether this overlaps another footprint, or ‘non-overlapping’ i.e. a footprint size reflecting the size a footprint needs to be according to availability of ’free’ land not occupied by another footprint. In both cases, the size of the circles reflects land which is currently occupied by farmland and gardens‚ i.e. technically available for food production.

The map also allows the footprints of the major towns in the region (Bournemouth, Bristol, Cheltenham, Exeter, Gloucester, Plymouth, Poole and Swindon) to be switched on and off to see the demand that these centres create, although the non-overlapping footprint sizes always reflect the footprint of major towns even when they are not visualised.

Food footprints illustrate simply‚ but powerfully‚ how large an area is needed to fulfil the basic needs of an urban population. It’s a good example of the use of geographic information (GI) science – putting data onto a computerised map‚ in order to create a picture of what’s going on in a way anyone can understand – in which Mark’s company Geofutures specialises.

Can the UK feed itself?

Permaculture expert Simon Fairlie performed a series of calculations on the potential for land to produce enough food‚ fibre and fuel under a series of agricultural regimes. Taking one which Fairlie calls ’Livestock Permaculture’‚ 1 hectare of combined agricultural and forestry land supplies 4.4 people.

Crudely on this basis‚ the whole UK landmass could feed 98 million people – many more than our current population of about 61m – but of course the population is not evenly distributed‚ nor is all land equally productive.

A supporter of the Transition movement‚ for these reasons Mark nonetheless warns against individual communities becoming insular as they plan to re-localise. They may have plenty of surrounding productive land‚ but if it falls within the food footprint of a larger settlement‚ there will be competition for its resources.

How do we plan for the food future?

So how do we plan for a future without cheap food imports‚ without oil-fuelled central distribution depots? Mark argues that the data and technology we have available now can point the way to a domestic food economy in which food can still be moved from areas of lower population to the nearest areas of food deficit‚ having been produced in those areas which best suit farming of grain‚ fruit‚ dairy or vegetables.

GI maps and analysis show us where the population hotspots are‚ and where certain farming types predominate. They also highlight additional future issues for the mix‚ like areas at risk from sea level rise and changes in rainfall and temperature.

Advanced spatial analysis can provide the key to planning how centres of agricultural production can supply their regional hinterlands‚ how the footprint of London and the home counties can co-exist with the footprints of the towns it encompasses‚ and how we can avoid serious food shortages in future.

The scale of a study of this kind and the investment required would not be large – especially compared with the risk of heading into a food crisis blindfold – and Geofutures is seeking research partners and funding to continue this work.

For more information about the Geofutures food footprint analysis, or how GI can help you achieve spatial insight in this or another field, please contact us.

More information about the Transition Network can be found here.

Tags: data, food security, mapping, Sustainability
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How clean is your data?

Thursday, July 2nd, 2009

Geofutures’ Simon Lewis explains that the success of a major new information resource demands a thorough approach to cleaning up the underlying data

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.

Tags: clients, data, databases, GIS, technology, town centres
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