Getting SMART with digital design

Fundamentally, the ways in which built environment data is used must change. Planners need to think at a much finer grain than before and architects at a broader one. From pedestrian precincts to cul-de-sacs and upper level walkways, many innovations in urban planning and design have been launched with great optimism, only to blight new developments with massive social and financial costs, writes Tim Stonor of Space Syntax.

I believe there are two key reasons why this process of trial and error continues to happen: first, the scarcity of real knowledge about how people behave and, second, a shortage of accurate and reliable forecasting tools to test plans in advance. Until recently it has been expensive and time-consuming to overcome these issues: teams of observers with clip boards are costly; transcribing video is time-consuming. However, the rise of the “smart” era has witnessed an explosion of data capture and analysis techniques that can give us accurate and useful insights into how people behave. This matters because professional failure creates public concern.

If using big data effectively to design places can result in developments that work better for the public and local economy, then local authorities should not only seek to use the best analytics to capture it themselves, but should also demand the same of the private sector. But how can they and others use the newly available data effectively?

Fundamentally, the ways in which built environment data is used must change. Planners need to think at a much finer grain than before and architects at a broader one.

At present, architects use Building Information Modelling (BIM) systems that handle data at the building level. While BIM can stretch to small clusters of buildings, it does not usually allow buildings to be set in their wider urban contexts. As a result, the important influence of context on place is lost and too many buildings are designed in isolation, with obviously negative results once built.

Planners on the other hand tend to work from regional and city-wide scales down to ward and postcode levels, where their engagement with urbanism stops. But this can prove too crude to get an accurate picture of what is going on at the important human scale. What they need is to be able to analyse data to inform decisions - such as transport plans or changing land values – at least down to the level of the individual street segment and ideally to the different buildings that make up the street.

A digitised system of planning and design should allow all of the buildings to talk to each other, then all the blocks in a neighbourhood to talk with each other, then all the neighbourhoods within a district to talk to each other and so on. This would be an “Urban BIM”: a system that integrates professional activity and leaves no spatial voids.

But what does “talking to each other” actually mean? It certainly involves visualisation of data on a common platform. But it also means going beyond visualisation into data analysis, correlation and modelling. I am troubled by many of the conference presentations and discussions about smart cities that focus, sometimes obsess, on the visualisation of data - the creation of pretty maps and video clips - then go no further. A Digitised Planning System should be able to understand relationships between a number of different issues combined together, as the most important potential benefits desired by planners or developers are likely to be the result of a combination of these. In my own experience this means being able to associate “input” decisions on spatial layout and land use to “outcome” phenomena such as land value, movement, crime risk and carbon emissions.

A Digitised Planning System should equip architects, planners and stakeholders generally to properly weigh up the pros and cons of different options in delivering outcomes.

I offer the following SMART approach:

Sense/Survey - Capture useful “urban performance” data such as the demographics of a particular place, location of different types of retail, types of employment and typical travel patterns as well as “urban form” data including spatial accessibility, topography, building location, capacity and condition.


Map - Spatially visualise that data e.g. develop maps that geo-locate the various urban performance and urban form characteristics.


Analyse - Use statistical tools to search the data for patterns, associations and correlations e.g. link observed pedestrian movement data with spatial accessibility levels and factor in the land use attraction created by shops and transport nodes. Infer via a software model simulation where residents are likely to want to travel to in the city and what sort of uptake there might be for a new bus route or cycle path. Use that software model to try out different options for changing the area and review how they would impact on the way the city works, in order to decide on which one would be most appropriate.


React - Produce evidence-based policy, plans and detailed designs.


Test - Use models to forecast the impacts of proposals in advance. Use the results of these forecasts to discuss ideas with stakeholders.

 

Once a particular option has been decided on and implemented, monitor how accurate the predictions were, in order to help refine and further develop the model. In other words, repeat the SMART cycle through further sensing, mapping, analysis, reaction and testing.
 
By taking such an approach, a Digitised Planning System would equip local authorities with their own live models of how their areas work across a range of scales, and use these to evaluate the likely impacts of designs from developers on the wider city. This then would make it possible for evidence-based planning decisions to be taken, giving local authorities firm grounds, for example, to negotiate design changes with developers.

One example of this approach is the City of London’s current development of a model to describe pedestrian movement within the city that will enable it to test how particular development proposals would affect this. Much experience has been gained over the last few years in the methods and tools required to develop models such as that sought by the City of London: models that are comprehensive enough to enable the overall impacts of different options to be thoroughly and reliably tested against each other yet sufficiently detailed to inform architectural and landscape design discussions.

My belief is that this approach can lead to the creation of a coherent Digital Planning System in the UK, which reconciles forecasting with accuracy, and the public with the planning process.


Tim Stonor is an architect and urban planner and managing director of the strategic consulting firm Space Syntax Limited, which he founded in 1996.

This is one of a series of think pieces on the opportunities of big data and digital platforms available now at http://www.architecture.com/DigitisingthePlanningSystem