Last week, Cabinet Office minister Francis Maude announced the release of additional flood risk assessment data to help local communities plan their response.
Arup’s Damien McCloud agrees that open data could help planners and designers answer increasingly complex questions about the built environment. His blog was originally posted here.
Planners employ data to understand space through its physical attributes, as well as its human, social and functional characteristics. A variety of datasets are also used to understand how a space is currently used and experienced by those who inhabit it. Datasets are also used to identify what are the key factors that govern a space’s development.
Open data – defined as data that can be “freely used, reused and redistributed by anyone” subject to very few restrictions – can help answer these sorts of questions. For example, the London Heat Map combines data provided by local authorities and the Greater London Authority to create an interactive tool to identify new district heating opportunities. In this way, open data can provide richer data to answer more complex planning and design questions. For example, you could use sensors to identify friction points such as traffic bottlenecks – but only if the controlling public body opens the data.
Open data currently has a tendency to be at a coarse geographic and temporal timescale. With access to more granular data you could create a more sophisticated model of the problem and allow a better designed solution. By routinely collecting and publishing open data, you can create a wealth of data assets that enable multiple parties to evaluate previous planning and design decisions. This supports decision-making informed by data, not conjecture.
A data-centric approach could also help to reveal interactions that only show up at the larger scale. For example, how does a large development on higher ground, upstream, some distance away, affect urban planning or design decisions in a location that may suffer flash floods as a result?
The increased availability and use of open data will raise expectations that planners and designers can answer such questions in a quicker more efficient manner with a greater understanding. To ensure that we can, we need more data from more sources – academic and corporate sources as well as government ones.
We also need better quality data, which comes with clear information on how and when it was produced and processed and how accurate it is. Whether data comes from the public sector or private providers, there have to be incentives to sustain its publication.
Statutory obligations offer strong incentives, but we will need more of them to get the rich level of data required that is at an appropriate temporal and geographic scale. Finally, a data-centric approach to planning requires planners and designers who are comfortable with data. They must know which is the most appropriate and highest quality data to use in their decision-making and how to integrate datasets for greater value.
RIBA has called for the planning process to be digitised, with all data made publicly available – arguing this could unleash economic growth and help local authorities better inform their strategies. I think this would be an excellent way to start making better use of open data.
Damien McCloud leads the spatial technology team in the IT and communications group at Arup
Data quality is often used as a reason for not publishing data. However, sometimes that data can be the only definitive or best available source of that information. Publishing the data ‘warts and all’ whilst painful for some can actually lead to improvements in the quality. Certainly it can act as a barometer for its real usefulness in terms of feedback from users or number of software applications developed off the back oft it. This can also therefore provide the basis of a business proposal for funding for its improvement. Publishing data with the necessary metadata is essential, both at the dataset and feature (attributes) level. Often however people will not read this, and use with no thought to the original intended purpose. What may be a better strategy is to also use suitable graphic or cartographic techniques (e.g. red, amber, green quality rating) to really help people at a glance to understand how fit for purpose that data really is.