City Evolution - digital
All cities are made up of buildings. Like cells and atoms these constitute recognisable granular building blocks which collectively do much to determine a city's appearance, character, health and potential. Gathering and collating data on these buildings, and understanding the contribution of each to the city as a whole, and their rate of change, is therefore critical for those involved developing sustainable city policies for the future.
Building age in Reykavik, note the number of detached buildings
However little attention is currently being paid to the highly inefficient way in which we approach policy development relating to the building stock, inefficiency largely caused by tight controls imposed by the UK government on two key datasets. These are OS MasterMap, held by Ordnance Survey, which contains footprint data for all buildings in the UK , and the VOA Council Tax dataset held by the UK Valuation Office Agency (VOA) which holds property characteristics for every taxable building.
To equip themselves for the future, UK cities need to maximise efficiency, effective-ness and competitiveness, whilst at the same time reducing energy consumption.
They also require building stock of diverse enough form, flexibility and affordability to meet growing population demands. Stock must also be energy efficient and of sufficient quality and diversity to maximise liveability, and to maintain a city's
uniqueness to enable it attract intellectual capital and external investment.
It widely accepted that to increase efficiency and reduce resource wastage a more joined-up, efficient approach to the urban fabric that harnesses new technologies is required. However to achieve this a far more comprehensive and detailed understanding of the makeup and rate of change of the building stock, and embedded values within it is also necessary. This cannot be achieved without greater access to more detailed data. The value of open building attribute metadata is therefore predicted to grow.
The need for national attribute databases providing reliable data to inform models of national stock composition, has been recognised in many European countries since the 1990s, largely as a result of energy legislation, and the shifting the pattern of investment in industrialised countries from new build to adaptation.
Metadata is now required in the analysis of material stock flows, rates of change, stock diversity, resource reserves, building demand and energy efficiency. Though value also exists for those working in planning, housing, construction and property development, conservation, community engagement, education etc, energy legislation is driving demand in this field.
In 2011 demand was highlighted by the Buildings Performance Institute of Europe in its ambitious study into the makeup of European stock. This was undertaken to identify whether energy related policies and regulations could, based on existing data, be adequately monitored and assessed. The report concluded that ‘A key obstacle to this challenge is clearly our limited knowledge and understanding of existing buildings’.
At present many European countries’ view of their stock appears to be partial, with knowledge and access differing widely between professionals and countries. Kohler and Hassler argue that this largely stems from the focus of individual sectors on single stock areas with limited objectives. These include government interest in domestic and public stock, over commercial, owing to state investment particularly in social housing; construction industry interest in new build and technological innovation; conservation sector focus on designated assets which on average comprise only 1-2% of national stocks.
The most consistent and comprehensive records for building stocks relate to property tax and land ownership purposes. Countries and cities willing to release these records as open data, or even to share them under restricted terms, have a significant head start in effective policy making compared to those that do not, as this enables
knowledge sharing and cross sector problem solving on a much larger scale.
The UK context
Many of the above reasons for lack of comprehensive metadata availability are mirrored in the UK. However here the problem could be largely solved by the open release of OS footprints and VOA property data. The VOA’s ‘Property Details’ database for dwellings was introduced in the 1970s and contains sixteen attribute classifications including building type, area, number of storeys and construction date. Information is available for every taxable building in the UK. However this dataset is not yet open, and is restricted even for academic purposes. If it was, a detailed understanding
of morphological diversity and spatial trends within the stock of say Bristol, compared with that of Bath or Milton Keynes, could be rapidly assessed, and investigated in relation to other variables.
Despite the government's aspiration to develop smarter more sustainable cities, the use of new technologies to assist city-wide analysis of the building fabric, at granular level, and its rate of change, is as a result near impossible. Furthermore as no consistent guidelines or legislation exists to require planners to assess the wider socio economic and environmental value of buildings proposed for replacement, we are in a position where we the UK is predicted to demolish a significant proportion of its stock by 2050 without any assessment of its potential long term value. (see Demolition)
The aim of this section of the site is demonstrate the importance and relevance of building metadata and its role in increasing understanding of this wider impact of building loss. It has also been designed to set these issues in their international context and to stimulate Ordnance Survey and VOA data release.
Working with the tri London borough alliance of Westminster, Kensington and Chelsea, and Hammersmith and Fulham councils, and across Camden we will be showing, over the next two years, how local authority building attribute metadata (particularly datasets relating to building age), can be collected, collated, released, and analysed and visualised to identify long term 'value' within city stock.
We will be also be developing ways using current and historical spatial data to analyse and visualise demolition rates; assessing the potential use of conservation sector knowledge to identify the future spatial location of demolition, and experimenting with methods, developed by Kiril Stanilov, of employing historical spatial metadata and machine learning to predict urban change.
The research will look at the relevance of building metadata at four geographic scales: local, city-wide, national and European.