Tim Stonor_SMART modelling
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Presentation given at the NBS Live conference, London, 4th November 2014.
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- 1. SMART modelling Developing an Urban BIM NBS Live London 4th November 2014 Tim Stonor Architect & Urban Planner
- 2. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 What is a Smart City? How can a Smart City be planned & governed? Where is the place for technology in the Smart City? Part One The “Smart” context
- 3. Tim Stonor Integrated Urbanism Space Syntax © 2014 Part Two The UK Smart & Future City effort
- 4. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 The legacy of uban failure
- 5. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 How people live
- 6. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Beware fanciful ideas
- 7. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Embrace the new science of cities
- 8. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Avoid introverted architecture
- 9. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Plan at a human scale Construct an Urban BIM Special project development guidelines Primary routes Land use mix Commercial 30% Residential 65% Social Infrastructure 5% FAR 4.5 Plot coverage (ground) 80 – 100% Plot coverage (upper) 50 – 75% Building height 5 – 7 Secondary routes Land use mix Commercial 10% Residential 75% Social Infrastructure 15% FAR 3.0 Plot coverage (ground) 70 – 80% Plot coverage (upper) 50 – 60% Building height 4 – 5 Self organising development guidelines Plot area FAR 60 - 99 sq m 1.0 100 – 299 sq m 3.0 300 – 599 sq m 4.0 600+ sq m 5.0
- 10. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 data porn
- 11. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 The new art of urban data science 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 y = .784x + 2.147, R-squared: .589 Spatial accessibility Pedestrianmovement Spatial accessibility -1 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 y = 1.235x + 1.048, R-squared: .702 Vehiclemovement
- 12. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Sense Map Analyse React Test …
- 13. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Sense Map Analyse React Test … S M A R T
- 14. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 1. Focus on space The data framework
- 15. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Space Syntax Spatial Layout Accessibility
- 16. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Courtesy of The TLRN Central London Pedestrian Study by Atkins 80% retail located on 20% most spatially accessible streets
- 17. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Trafalgar Square, London Observe Explain Forecast Deliver
- 18. Tim Stonor Integrated Urbanism Space Syntax © 2014 Darwin, Australia
- 19. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Disconnected Layout Spatial Accessibility Score = 35 Connected layout Spatial Accessibility Score = 75 Disconnected Layout Connected Layout Difference IRR 15% 18% 3% NPV $1.6 billion $1.1 billion $0.5 billion Real estate value analysis 不動産価値の分析
- 20. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 2. Co-create between academia & industry University College London Space Syntax
- 21. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 3. Focus on human outcomes
- 22. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 4. Model the impact of cities on resource flows
- 23. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 4. Model the impact of cities on resource flows
- 24. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 5. Model the impact of cities on the environment
- 25. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 1. Focus on space 2. Co-create between academia & industry 3. Focus on human outcomes 4. Model the impact of cities on resource flows 5. Model the impact of cities on the environment Space Syntax Key lessons
- 26. Actions Decisions Consultations Ideas for change Uncertainties People 1.Behaviour 2.Wealth 3.Health 4.Safety 5.Cultural identity Resources 1.Materials 2.Energy 3.Finance 4.Utilities supply 5.Waste handling Urban Form 1.Spatial Form 2.Physical Form - capacity - location - condition - value v3 Environment 1.Sun 2.Wind 3.Rainfall 4.Groundform 5.Biodiversity Data Mapping Data Quantification Map Urban Data Collective Integrated platform for visualising performance data How is the place working? Opportunities & Constraints Creation of Conceptual Options React Urban Strategy Integrated urban policy and practice What are the opportunities for change? Test Urban Forecast Model Scenario testing & performance forecasting How will performance be affected by change? Simulation & Optimisation of Options Development of Preferred Option Understandings Analyse Urban Performance Model Integrated urban diagnostics: spatial analytics Why does the place work the way it does? Urban Diagnosis Issues, Objectives & Principles SMART STEPSUSER INTERFACES Decision groups Data out DATA INPUTS 1 2 3 4 5 6 Space Syntax © 2014 Sense Health Wealth Happiness Datain Behaviour change Integrated Urban Model
- 27. Tim Stonor Spatial layout as critical infrastructure IGEBC UK Seminar Space Syntax © 2014 Spatial Accessibility High Low Spatial masterplanning Rapid design development & testing 空間マスタープラン 迅速なデザイン検討と評価
- 28. Tim Stonor Spatial layout as critical infrastructure IGEBC UK Seminar Space Syntax © 2014 Spatial Accessibility High Low Spatial masterplanning Rapid design development & testing 空間マスタープラン 迅速なデザイン検討と評価
- 29. Tim Stonor Spatial layout as critical infrastructure IGEBC UK Seminar Space Syntax © 2014 Spatial Accessibility High Low Spatial masterplanning Rapid design development & testing 空間マスタープラン 迅速なデザイン検討と評価
- 30. Tim Stonor Spatial layout as critical infrastructure IGEBC UK Seminar Space Syntax © 2014 Spatial Accessibility High Low Spatial masterplanning Rapid design development & testing 空間マスタープラン 迅速なデザイン検討と評価
- 31. Tim Stonor Spatial layout as critical infrastructure IGEBC UK Seminar Space Syntax © 2014 Spatial Accessibility High Low Spatial masterplanning Rapid design development & testing 空間マスタープラン 迅速なデザイン検討と評価
- 32. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 GO-Science Foresight Future of Cities Future Cities/Transport Catapult/Demonstrators Professional Institutes UK Research Councils UKTI Smart Cities Innovate UK Cities Programme UK Cities & Citizens Manufacturing, Construction, Consulting & Utilities Industries Academic Institutions British Standards/ Open Data Institute GO-Science Foresight Future of Cities Smart Cities Ministerial Forum Government Depts. UK Smart & Future Cities Strategy 英国貿易投資総省 英国研究会議 学術機関 製造、建設、コンサルティン グ、公益事業 の各業界 未来都市、交通システムカタ パルトの運動 各省庁の スマートシティ部門 英国のスマートシティ戦略 イノベーティブＵＫ プログラム 英国の都市・市民 ビジネス・イノベーション ・技能省 未来都市フォーサイト ・プロジェクト 各専門家団体・組織 英国規格協会 オープンデータ協会
- 33. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 GO-Science Foresight Future of Cities Future Cities/Transport Catapult/Demonstrators Professional Institutes UK Research Councils UKTI Smart Cities Innovate UK Cities Programme UK Cities & Citizens Manufacturing, Construction, Consulting & Utilities Industries Academic Institutions British Standards/ Open Data Institute GO-Science Foresight Future of Cities Smart Cities Ministerial Forum Government Depts. UK Smart & Future Cities Strategy
- 34. The Future of Cities Foresight Project Overview Project aim Provide central and local government with an evidence base to support decisions in the short term which will lead to positive outcomes for cities in the long term. Spatial scales UK system of cities and city systems. Time horizons 2040, 2065.
- 35. 1. In which locations, and in what numbers, are people going to live, work and take leisure? 2. What forms of movement will exist to connect people together? 3. What flows of resources will need to occur - for example with energy, water and waste – to support human behaviour patterns? 4. What technologies will exist to support these interactions? 5. What impacts will these have on the natural environment? 6. How will future places be funded? 7. How will decisions be made to address each of these questions? The Future of Cities Foresight Project Key urban policy questions
- 36. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 GO-Science Foresight Future of Cities Future Cities/Transport Catapult/Demonstrators Professional Institutes UK Research Councils UKTI Smart Cities Innovate UK Cities Programme UK Cities & Citizens Manufacturing, Construction, Consulting & Utilities Industries Academic Institutions British Standards/ Open Data Institute GO-Science Foresight Future of Cities Smart Cities Ministerial Forum Government Depts. UK Smart & Future Cities Strategy
- 37. Copyright © 2012 BSI. All rights reserved. o Existing standards mapped onto smart city model ✓ Mostly Technical: “What” needs to be done to implement ✓ Some Process: “How” actions and steps to be taken to implement ✓ Few Strategic: “Why” planning and management guidance www.bsigroup.com/smartcitymapping Mapping smart city standards
- 38. Copyright © 2012 BSI. All rights reserved. ISO Advisory Group on Smart Cities ISO/TC 268 Sustainable development in communities Strategic Process Technical Current ISO work Current CEN work ISO/IEC JTC1 Study Group on Smart Cities IEC Systems Evaluation Group on Smart Cities CEN CENELEC ETSI Co- ordination Group on Smart & Sustainable, Cities & Communities Current IEC work Current ITU work ISO/TC 268/SC 1 Smart Community Infrastructure ITU-T Focus Group on Smart, Sustainable Cities BSI Lead International smart city standardisation activities
- 39. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 0. Spatial Layout 1. Movement 2. Land Use 3. Crime & Safety 4. Land Value 5. Carbon Emissions Integrated Urban Model
- 40. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Darwin, Australia CBD Expansion
- 41. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Pedestrian movement Hourly average Weekday Pedestrian movement people per hour 800- above 500- 800 200 - 500 100 - 200 50 - 100 0 - 50
- 42. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Darwin City: $1,525/m2 Larrakeyah: $1,230/m2 Stuart Park: $759/m2 The Gardens: $687/m2 (UCV taken from NT government data in 2011) Building attraction Land values 5,000 to 200,000 2,500 to 5,000 2,000 to 2,500 1,750 to 2,000 1,500 to 1,750 1,250 to 1,500 1,000 to 1,250 750 to 1,000 500 to 750 250 to 500 0 to 250 Study area Unimproved Capital Value (UCV) in $ per m2
- 43. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Building attraction Land use Community Retail Residential Medical Storage Catering Offices Education Leisure Under construction Industry Parking Ground floor land use Services Hotels Vacant Civic Agriculture Transport Empty or abandoned site Utilities Green open space Study area
- 44. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Building attraction Building height Study area 20 toabove 6 to 9 3 to 5 2 1 10 to 15 Building storeys
- 45. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Transport attraction Bus routes & bus stops Bus routes Bus stops Study area
- 46. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Transport attraction Parking zones & spaces Parking space Zone A ($2.20/hr for up to 2 hours) Zone B ($1.60/hr for up to 3 hours) Zone C ($1.10/hr or $6.50 full day) 15-minute zone (Free of charge) Taxi zone Study area
- 47. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Building attraction Active frontages Study area
- 48. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Spatial Layout Attraction Spatial Accessibility High
- 49. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014
- 50. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Spatial Layout Attraction Masterplan design
- 51. @Tim_Stonor SMART modelling Developing an Urban BIM NBS Live Space Syntax © 2014 Darwin, Australia CBD Expansion
Notes de l'éditeur
- Good morning. It’s a pleasure to have been invited to give this presentation today (at the NBS Live conference). The subject of my talk is urban modelling - using the power of new technology to be better at predicting the future: something that architects and designers should be good at!
- In the first part of my presentation I’d like to cover three important questions that bear directly on urban modelling: First, what is a Smart City? Second, how can a Smart City be planned & governed? Third, where is the place for technology in the Smart City? And I will relate each of these questions to the architecture of space.
- In the second - and brief - part of my presentation I’ll try to summarise the very significant effort that the UK is making to plan for its urban future, embracing the opportunities that new technologies provide.
- Rapid global urbanisation provides the opportunity to create striking, new forms of city living, supported by the proliferation of new “smart” technologies. Nevertheless, the delirious enthusiasm of architects and planners should be set against the sobering reality of recent urban history. From pedestrian precincts to cul de sacs and upper level walkways, many innovations in town planning and design have been launched with great optimism over the years, only to create blight in the form of massive social and economic cost. Past results matter greatly for the future planning of cities because professional failure creates public concern and this affects political confidence. So, before beginning to create visions for novel urban futures, those responsible – the architects, planners and engineers as well as the policymakers and politicians – should first ask why past plans didn’t deliver what was intended. Why, in seeking social harmony, wealth and resilience have cities become divided, unequal and car-dependent? There seem to be two key reasons for urbanism’s inability to accurately forecast the effects of its actions:
- …first, the scarcity of real knowledge about how people behave in cities and what the impacts of these behaviours are on, for example: social cohesion, economic impact and safety.
- Second, the 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 clipboards have been costly; transcribing video has been a slow and intensive process; computational power has been inadequate and expertise limited. Too often, science has had to give way to fanciful ideas from architects and planners: creations that have little resemblance to real human needs.
- However, the rise of the “smart” era has witnessed an explosion of data capture and analysis techniques, as well as the technology-literate operators trained to use them. The opportunity now exists for organisations, both public, private and community-based, to behave differently: to build databases of urban performance, analyse the patterns within these, then create future plans in a more robust manner. The prospect of a new science for cities is real: a new, evidence-informed and analytic approach to urban planning and design that might obviate, or at least reduce, uncertainty in future urban decision-making.
- Such an opportunity raises fundamental questions about what datasets should be collected and how these should then be analysed. Fundamentally, the ways in which built environment data is used must change. To put it simply, architects need to think at a broader scale than before, planners at a finer grain and both professions need tools that fit these purposes. Currently, 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: turning their backs to each other, or surrounding themselves with empty landscaping.
- Planners on the other hand tend to work from regional and city-wide scales down to local neighbourhood levels, where their engagement with urbanism stops. But this approach can prove too crude to provide an accurate picture of what is going on at the important human scale. Planners should 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 level. An integrated modelling system 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”: an Urban Building Information Management System that links different professionals working across multiple spatial scales. 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. Too many conference presentations and media articles about so-called smart cities focus on - sometimes obsess about - the visualisation of data, creating dazzling maps and video clips, but often going no further.
- Captivating an audience is an important first step in raising awareness of the potentials of a data-driven approach; demonstrating that the data contains the intelligence to inform real urban change is the next key step.
- Urban modelling systems should deliver the full potential of recent technological change by not only visualising data but, more importantly, identifying patterns within those data and establishing correlational relationships between different datasets. After all, urban planning and design practice necessitates the simultaneous analysis and resolution of multiple, complex issues. Urban modelling should seek to serve these ends, being able, for example, to associate input decisions on spatial layout and land use allocation to outcome phenomena such as land value, movement, crime risk and carbon emissions. Urban modelling systems - based on algorithms that make sense of urban complexity - should equip architects, planners and stakeholders generally to properly weigh up the pros and cons of different options in delivering outcomes. In short, a smarter approach is required, one that lets users take a sequence of considered actions:
- This approach has five important steps: Sense 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. For example: develop maps that geolocate the various urban performance and urban form characteristics. Analyse Use statistical tools to search the data for patterns, associations and correlations such as links between observed pedestrian movement data and spatial accessibility levels. Factor in the land use attraction created by shops and transport nodes. Identify algorithms that make sense of the data. React Produce evidence-based policies, plans and detailed designs. Test Use algorithm-based predictive analytics to forecast the impacts of proposals in advance. For example, infer via a 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 the results of these forecasts to discuss ideas with stakeholders, trying out different options for changing the area and reviewing how these would impact on the way the city works, in order to decide on which one would be most appropriate.
- A SMART approach. which includes the monitoring of a building or urban development post-construction to provide feedback that will help refine and further develop the forecast models. In other words, repeat the SMART cycle through further sensing, mapping, analysis, reaction and testing.
- My company Space Syntax has pioneered the creation of SMART urban models for over 25 years, using the increasingly sophisticated capabilities of 2-dimensional and 3-dimensional digital platforms to produce ever more capable urban planning decision-making systems. Much experience has been gained during this time in the methods and tools required to develop models that meet the needs of public, private and community organisations. These are models that can be used in a strategic mode to robustly test the wide-scale urban planning impacts of various options, but are sufficiently detailed to inform architectural and landscape design discussions at a later point in the process. The first fundamental characteristic of our SMART approach is a focus on space: Space Syntax has created an approach to spatial network analysis that uses the pattern of street connections as a “data framework” onto which other datasets are hung. We do this because we have discovered that spatial networks have fundamental influences on human behaviour. More connected places in red, for example, can generate higher movement flows, land values and social potentials. Disconnected places in blue can suffer from social isolation and economic underperformance. We have found that it is essential to put spatial analysis at the heart of urban modelling, not least because it is in space that people meet each other and through space that they move. Our experience from stakeholder discussions shows that people understand space intuitively and respond positively to spatial analysis because, it seems, we all “read” space naturally. Our clear conviction is that, without spatial network analysis, Smart City models are missing their most essential ingredient.
- For example, the spatial hierarchy of London’s street network - calculated using Space Syntax software - highlights the most accessible streets in red. These are the routes that are most likely to be travelled along by people journeying across the city.
- Analysis of London’s land use pattern - evolved over centuries - shows that the retail uses in red have located themselves on these same, highy connected routes. 80% of the retail has located on the 20% most spatially accessible streets. This is no coincidence - shops locate where the people are going to be. And people locate, we have discovered, according to the spatial hierarchy.
- We have found that the sensing, mapping and analysis of datasets - as we did here in the redesign of Trafalgar Square in London - provokes an informed, science-based reaction among policy-makers, planners and designers. In a similar way, our experience is that stakeholders respond more positively to objectively tested proposals - that are evidence-based and supported by visualised analysis - than they do to instinctive, unsupported, opinion-based ideas. At Trafalgar Square we showed how the seemingly complex patterns of pedestrian movement were actually very simply explained by the spatial hierarchy of the square - people followed the most direct routes around the outside of the space. This helped us work with Norman Foster to redesign the square around a new staircase that would allow new, direct routes through the heart of the space. Space Syntax’s SMART approach to urban modelling equips local authorities with agile models of how urban areas work across a range of scales. These models are used to evaluate the likely impacts of planning and design proposals on local places as well as on the wider city. They work quickly to inform constructive discussions, which might otherwise degenerate into protracted and emotional conflict. This expedites evidence-based decisions, giving local authorities firm grounds, for example, to negotiate design changes with property developers.
- And, to create sensational designs for new urban places, as in our masterplan for the city of Darwin, Australia, where the modelling has been used to measure the impact of the new development on transport patterns of people on foot, on bicycles and in vehicles - as well as the impact on land values.
- By measuring the correlations between spatial accessibility and land values it is possible to compare a Disconnected layout on the left with a Connected layout on the right - and, in this case for a development in central London, to identify half a billion US dollars of difference in land values.
- Our 25-year experience as one of the world’s leading urban modelling and design companies is itself based on a 40-year research programme at University College London, pioneered by Professor Bill Hillier. A close working relationship between consultancy and world-leading university brings deeply studied academic techniques into urban planning and building design practice. In return, the data and the learning from project applications is taken back into the rigorous research environment of a university setting. This two-way process of co-creation is the second fundamental characteristic of Space Syntax’s SMART approach: continuous co-creation – between academic researchers and professional practitioners for mutual benefit.
- The third key feature of Space Syntax’s methodology is its focus on human outcomes above data inputs. The approach is fundamentally concerned with the social, economic and environmental impacts of the planning and design process on people. In practice, this means collecting data on human behaviour patterns: movement patterns on foot, bicycle and in vehicles; standing, sitting and interaction patterns in streets and public spaces; as well as data on socio-economic demographics, crime and health patterns.
- Modelling the impact of resource flows is a fourth key feature: the supply of transport connections, energy and other utilities such as water and telecoms,…
- …as well as the handling of waste. Spatial network analysis is again powerful because many utilities are laid in line with street networks, both above and below ground. When urban planners set out a street pattern they are not only determining patterns of movement and human interaction but also the flows of resources that support those behaviours.
- The city is the largest intended object of collective human creation and its interactions with the environment need to be analysed and understood with modelling. This relationship is handled by the fifth fundamental characteristic of Space Syntax SMART models, which connects spatial network analysis to environmental features. For example, sun path analysis is linked to spatial accessibility analysis to modify the overall attraction of a highly connected street if that street is in full sun and a highly shaded, and if less direct, alternative is available. In a similar way, linked analyses can be used to optimise the layout of an urban grid by aligning the geometry of the street pattern to the angle of the prevailing breeze.
- Together, these five fundamental characteristics define Space Syntax’s approach to the creation of Integrated Urban Models that help people make decisions to influence behaviour change towards more resilient, more sustainable ways of living. Built on open source code and increasingly moving to open data platforms, Space Syntax’s aim is to disseminate the approach into general urban practice - to share the benefits of a SMART approach to urban planning, design and governance that has been tried and tested for over a quarter of a century. We believe that this approach sets a new standard for the planning and management of cities.
- This diagram summarises Space Syntax’s approach to the creation of Integrated Urban Models. Our process begins at the top left, with the inputting of data, and ends bottom right with data coming out of the model. In between is a process of visualisation and analysis that leads to the creation of urban planning and design ideas, which are then discussed by stakeholders, leading to actions being taken that can change human behaviour patterns for more sustainable and resilient outcomes.
- Architects and urban planners can use this modelling approach as a creative design tool. The model lets people - both professional and non-professional - to test ideas in a quick and highly visual way, providing rapid feedback that saves time and money.
- For example this first grid like option…
- …produces a weak pedestrian movement structure. This plan is therefore not going to work.
- However, a more integrated route structure that links development into the right strategic connections surrounding the site…
- …is shown by the computer modelling to produce a powerful and coherent movement structure.
- So, let me turn to the second part of my presentation. The UK government - supported by academia, business and civic society - is putting a huge effort into planning its urban future. This diagram describes the numerous organisations that are networked into the overall UK effort, which is coordinated by the Smart Cities Ministerial Forum under the chairmanship of the Minister for Cities, Greg Clark MP. As you can see it is a complex web of connections. Let me highlight some of the elements in which I am personally involved.
- The UK Government Office for Science is currently running a major Foresight project on the Future of Cities and I am a member of its Lead Expert Group. We are looking to establish a scientific evidence base on what is known about cities and what the trends are for their long-term futures, looking up to 50 years ahead.
- The aim of the project is to provide central and local government with an evidence base to support decisions in the short term which will lead to positive outcomes for cities in the long term. We are working across many different spatial scales, looking at the interrelationships between the UK system of cities and at the interrelationships between the many different systems that make up any particular city: transport systems, education systems, energy and waste systems.
- The future of cities will be decided by answers to key urban policy questions. In which locations, and in what numbers, are people going to live, work and take leisure? What forms of movement will exist to connect people together? What flows of resources will need to occur - for example with energy, water and waste – to support human behaviour patterns? What technologies will exist to support these interactions and what impacts will these have on the natural environment? How will future places be funded and, ultimately, how will decisions be made to address each of these questions? These are the key issues that the Government Office for Science project is addressing.
- I am also assisting the British Standards Institute in the creation of standards for Smart and Future Cities. Our aim is to provide city leaders and practitioners with consistent guidance on how to achieve a Smart City.
- There are three kinds of standard: first, technical standards that describe outcomes; second, process standards that describe the methods that can be followed to achieve these outcomes; and third, strategic standards that describe why a Smart City is a desirable kind of city. We are emphasising the human aspects of the Smart City as much as the technological performance of, for example, sensors and displays.
- The UK’s work on Smart City standards sits within a rapidly emerging international framework.
- So, in summary, what I have show you is an urban modelling system, created in the UK but now being used and developed worldwide by well over 5,000 different people. It brings together data sets from various sources and the key to its operational effectiveness is its Spatial Layout Analysis – the base layer in the data stack.
- These tools are increasingly being used internationally. Recently we launched a vision for the expansion of the Central Business District in Darwin, Australia, where the predictive analytics were used to identify optimal pattern of new streets and public spaces, as well as a distribution of land uses. Key to this was modelling of land value impact, both on the new development and on existing land.
- These tools are increasingly being used internationally. Recently we launched a vision for the expansion of the Central Business District in Darwin, Australia, where the predictive analytics were used to identify optimal pattern of new streets and public spaces, as well as a distribution of land uses. Key to this was modelling of land value impact, both on the new development and on existing land.