International Postgraduate Program of Advanced Studies in Urbanism and Real Estate Development (ASURED)
Responsive Scenario Planning: Creating viable stories about the future Silviu-Vlad Pirvu 29 May 2017
â€œIon Mincuâ€? Center of Excellence in Planning Bucharest, Romania
PART I - INTRODUCTION: FROM THEORY TO PRACTICE
Uncertainty and Flexible Timelines
Evolution of Urban Planning: from a 2D approach to a 5D perspective
Towards a 5-dimensional approach
Responsive Urban Planning Cybernetics
PART II - RESPONSIVE URBAN PLANNING FOR REAL LIFE EXAMPLES
Long term planning for urban environments – Victoria, London UK
Enabling resilience in extreme environments – McMurdo Station, Antarctica
PART III – CONCLUSIONS
PART IV – EXPLORING NEW HORIZONS
A universal language of planning
Technology-Augmented Planners for Creativity-Augmented Plans
APPENDIX 1 – VICTORIA PLAN ACTION SCRIPTS
APPENDIX 2 – EXISTING AND PROPOSED PLANS OF MCMURDO STATION
APPENDIX 3 – MCMURDO PLAN ACTION SCRIPTS
1. Uncertainty and Flexible Timelines As humans, we perceive reality as 4-dimensional space â€“ the three-dimensional spatial component, plus the time as the fourth dimension. As our sentience is limited to a single timeline of the space-time fabric, the capability of planning and guessing the future is challenged by uncertainty, especially caused by the human factor. As Bayem Theorem shows, when the evolution of systems, objects and the relationships between them are measured and understood, the level of uncertainty can be reduced, and the capabilities of planning and forecasting improved. In the age of Big Data structuring and emerging synthetic intelligence some opportunities arise. By augmenting automation, advanced processing and machine learning, some patterns, hidden relationships, resources, opportunities and threats can be revealed quicker and provide a more comprehensive understanding, in comparison to an assessment done by the human intellect. Notwithstanding, when weâ€™re analysing urban settlements, we often observe that social, economic, political and other human factors are decisive in the way a place evolves. From a systemic approach, the human factor may also cause a sudden disruption in the urban system and also at regional, national and international level caused by a wide range of events (e.g. referendums, elections, technology adoption, recession, pop cultures and beliefs). As some outcomes can be predicted by analysing large sets of data, the lack of certainty in the human behavior and the limited capability of understanding the complex and dynamic urban systems makes urban planning a challenging task. It becomes even more challenging when the place has a high disruption rate and also if it is subject to sudden changes or extreme natural phenomena.
At this point, describing fine nuances of the social behavior is less challenging for human experts, compared to computing systems. Because of this, considering alternative timelines is a matter of combining existing information with knowledge, educated guess and our capability to imagine the possible futures. There is another critical challenge when designing a plan: emerging information and facts that are contradictory to the initial assumptions about the future. These often cause a revision of the plan or they make the plan irrelevant or even harming as it is not aligned with emerging needs and issues. Because of this, flexibility is key: ď‚ˇ
flexibility of the plan by absorbing and adapting new assumptions and aspirations,
flexibility in considering alternative timelines in order to adapt to the most plausible one in real time, and
flexibility of the plan in terms of being understood, accepted and updated by different actors.
2. Evolution of Urban Planning: from a 2D approach to a 5D perspective From 2D to 4D Since Ancient Mesopotamia, there was a growing need of planning settlements in order to fulfill the need for organized layouts that can enable a balanced growth, infrastructure management and good design for living, work, leisure, administrative, military and religious purposes.
Figure 1 - Plan of Pireus - Hippodamus of Miletus (5th Century BCE)
The first known masterplans were two-dimensional drawings, showing the layout defined by access, buildings and land (e.g. Figure 1). The tendency to establish a geometrical and logical order is revealed by analysing the Hippodamian plans and even older archaeological finds in Egypt â€“ among others, and descriptions in
ancient texts1. As settlements and the civilizational progress become more complex, the urban plans
dimensional approach, including more architectural and massing details, with more emphasis on the
Figure 2 - 16th Century representation of the Florence Plan
buildings, property use, piazzas and the environment. After the second Industrial Revolution, the rate of social and economic change was unprecedented, culminating to a new wave of globalisation. As major cities become competitive at global scale as engines of prosperity and innovation a more strategic approach was needed to embrace new opportunities and mitigate possible threats. Setting up objectives, scheduling actions and monitoring plans added time as an additional layer, propagated in the core of the urban plan, as a result, making planning a four-dimensional process. In time, it was shown that some assumptions about the future were inaccurate and some evidence base, opinions and commitments were changing, making the plan irrelevant, or in some cases, harmful. The root cause of failure was often the fact that the plan was unsynchronised in relation to an unexpected reality caused by lack of data, sudden changes (environmental, socio-economic, political) and the human factor (lack of commitment, various disruptions).
"Go up on to the wall of Uruk and walk around. Inspect the foundation platform and scrutinise the brickwork. Testify that its bricks are baked bricks, And that the Seven Counsellors must have laid its foundations. One square mile is city, one square mile is orchards, one square mile is claypits, as well as the open ground of Ishtar's temple. Three square miles and the open ground comprise Uruk. Look for the copper tablet-box, Undo its bronze lock, Open the door to its secret, Lift out the lapis lazuli tablet and read." â€“ Epic of Gilgamesh (cca 2100 BCE)
Scenario Planning: Aligning viability with plausible stories Scenario planning offers the opportunity of exploring different outcomes and aspirations that can further bring a place closer to a viable and sustainable future. Using mathematical, parametrical and viability models2, a plan can be linked to logical structures and functionality that can be connected further to other platforms for processing, visualisation, management and engagement. Creating scenarios is not only useful for testing a range of assumptions and aspirations, but it can also reveal hidden opportunities, bottlenecks and, more importantly, creative solutions. However, the current state of the planning system generates rigidity in the morphological aspect of a plan. Whilst scenario planning could evolve the planning methodology towards a more five-dimensional approach by considering multiple timelines, usually the planning process pushes towards sticking to a singular plan. The revision process of a plan might be long, complicated and costly and also the result of a political tension. What is needed is a new definition of doing planning, one that promotes higher levels of responsiveness, â€˜multiple timelinesâ€™ thinking and interdisciplinary engagement. Considering this, some questions emerge:
1. How can we create plans that can be responsive to sudden changes? 2. How can we use data and technology enablers in order to create responsive plans? 3. Who can be involved in designing responsive plans? 4. When, where and how should we use a 5D planning approach? 5. How a 5D plan approach can be implemented?
e.g. Envision Tomorrow, Esri City Engine
3. Towards a 5-dimensional approach Assuming that a plan would be completely responsive to change if it is developed in a five-dimensional structure3 and it also has the capability of adapting and responding, the level of responsiveness can be described by the following formula:
Resp% = AccAsum * SupAspir * CapAct Where: ď‚ˇ
Resp% = Level of Responsiveness
AccAsum = % of accuracy of the assumptions (evidence base, consistent commitments etc)
SupAspir = % of the support (as relevant to the plan)
CapAct = % of the capacity to act (when change is required)
Mathematically speaking, determining the values for each variable requires an absolute knowledge of all factors and elements involved inside and outside the subject area of the plan. As this is currently unachievable from a technological and physical perspective, we can consider the function Resp(time) a simple asymptotical function defined by:
lim đ?‘…đ?‘’đ?‘ đ?‘?(đ?‘›) = 1
If the variables cannot be determined precisely, monitoring â€œmain elementsâ€? of the plan enable the fine-tuning of the level of responsiveness. In order to define the main elements of a responsive plan, a series of considerations in terms of types of existing information, structure, execution, users and the current state of planning were assessed.4
i.e. 3D + time + all possible outcomes User Research Insights Report - Prototyping the Future of Planning - (Future Cities Catapult, 2016) â€“ provides extensive insights regarding the current lack of innovation in the planning sector. 3 4
Even if achieving a high level of responsiveness is highly improbable, observing the
the current state,
the possible states, and
the tendency towards a defined or pre-defined scenario.
As a result, the Responsive Plan Framework was defined (Table 1) as a Logical structure based on flexible relationships of the 4A’s:
Assumptions Aspirations Actions Actors
Figure 3 - Responsive Plan as an interlinked system of Assumptions, Aspirations, Actions and Actors
Table 1 - Responsive Plan Framework
Logical structure based on flexible relationships of the 4A’s (Figure 3 above): ASSUMPTIONS
“We believe this will be the right thing to do because <evidence base, commitments, opinions>”
“Based on our Assumptions, we want to achieve these Aspirations <objectives, targets, designs etc.> set up in masterplans, policies, strategies etc.”
“In order to our reach our Aspirations, we will do these Actions, which are defined by some or all of the following characteristics: Scheduled (temporal dimension) Triggered (by any change in any of the 4A’s) Conditioned (in a predefined relationship)
“Assumptions, Aspirations and Actions will be made by <known> and/or <unknown> people or designated systems.”
Plan needs to be more responsive to changes in terms of: <evidence base>
<commitments> and/or <opinions> <opportunities> <threats> <other internal/external changing variables/elements> 10
Dynamic areas – e.g. prosperous centers of major cities, growing settlements, rapid changing places and sectors
Areas in high risk – low socio/economic/environmental resilience - e.g. climate change (flooding, extreme warming), earthquakes, social tension. Creative bespoke combination of ideas, solutions and linked technologies
e.g. parametric planning and design, viability and mathematical models, GIS, BIM, custom scripts, structured Big Databases, interactive platforms.
IT IS IMPLEMENTED (the orientation of edges)
IT IS ACCESSED
As connected to a singular or multiple platforms of visualization, assessment and management
As a database with scripts and various privileges
Plan Makers – e.g. interdisciplinary groups of specialists
Contributes to it – e.g. consultants, critical friend reviewers, public consultation, stakeholder engagement, living beings, data, code, formulas
Approves it – e.g. Local Planning Authorities, Neighborhood Plan Forum, citizen referendum etc.
Manages – e.g. city manager, designated team, digital systems (AI, cognitive computing, scripts)
Uses it – e.g. citizens, service providers, entrepreneurs, city managers 11
Making changes or commencing procedures when there are:
predefined or relevant triggers
As they are
Pre-checked against predetermined conditions
4. Responsive Urban Planning Cybernetics A basic Responsive Urban Plan is defined by a cybernetic structure of data, scripts and users. The 4A’s and their interlinked relationships can be defined as:
Formulas that define the relationships between the 4’s
Defining the relationships between the 4A’s: e.g. A bidimensional Array describing the links between Assumptions and Aspirations, defined as Links[ASUM_i][ASPIR_j] will return the following values:
0 – when there is not link between Assumption i and Aspiration j
1 – when there is an oriented link from Assumption i to Aspiration j
2 – when there is an oriented link from Aspiration j to Assumption i
3 – when there is a codependent link from Aspiration i to Assumption j
Actions: o CATEGORIES:
Referring to the plan structure (e.g. changing data, links and formulas)
Implemented as part of the plan (e.g. providing new site allocations, mapping GIS data, triggering community engagement process)
Scheduled: if (current_date == specified_date) then action;
Scheduled & Additionally Conditioned:
if (current_date == specified_date && condition) then action;
Triggered: continuous checking against a falsifiable condition: o
o until (condition!=true)
Actors: o With different privileges in terms of viewing and editing the plan
As a responsive plan can have a complex structure, it can be visualised as a bidimensional or multidimensional graph. A mental map was chosen for a simplified illustration in the Figure 4 below.
Figure 4 - Basic Structure of a Responsive Plan
5. Long term planning for urban environments – Victoria, London UK Victoria is a small district in the City of Westminster in Central London, close to the Buckingham Palace and the Palace of Westminster, hosting the Victoria Station, Westminster Cathedral and a variety of massing, property uses, architectural styles, building periods, densities, layouts and materials.
Key issues Central London has key issues regarding housing, traffic and air quality, that also apply to Victoria district. A series of particular issues were identified by Victoria Neighbourhood Forum during the consultation event5 of the neighbourhood planning process regarding:
Architectural design: blank and inactive façades, inconsistency of architectural design of the new developments, unwelcoming arrival space;
Traffic congestion; Poor quality pedestrian environment; Noise and air pollution; Poor provision of high quality green spaces.
Figure 5 - Fragment of Victoria's potential Responsive Plan
Responsive Plan Preparation Based on the issues identified at district level and at a wider scale, the known commitments, current data and emerging trends, a fragment of a potential responsive plan framework for Victoria was designed (Figure 5 above). Using the framework and open data, some workflows between different enablers and Actions scripts6 were prepared. Main enablers: CityEngine and Envision Tomorrow Building Prototype Builder. Data used: Open Street Map GIS shapes (including max building height attribute), RightMove.co.uk and Zoopla.co.uk mapped data for the area.
Workflow 1: Changing the use class for Portland House Portland House is 29 storey office building in Victoria completed in 1963. It is currently subject to refurbishment and reconversion to residential. By integrating the massing and usage parameters in CityEngine (Figure 6), a set of reports were produced7 that were included further in an Envision Tomorrow Viability Model in order to assess the viability of the reconversion (Figure 7). Changing the property use in the shape attribute in CityEngine, propagated the changes further in the viability model. Including the data regarding average flat size and prices in the area, an extended report regarding the number of apartments (~470 of different types) and the expected rent was produced (Figure 10). Having all the buildings connected on the same platform could inform the retailers, the companies and the owners in the buildings nearby8 about the expansion of the catchment area and potential business opportunities that can be provided by the new residents.
See Appendix 2 - Scripted actions for Victoria plan Including Footprint Area, Gross Floor Area (GFA) and Floor Area Ratio (FAR) 8 Marked in blue for office and red for retail in Figures 12 and 13. 6 7
Figure 6 - Integrating massing and property use parameters + Reporting the Floor Area, GFA, FAR (sqm)
Figure 7 - Producing the Building Model for the current use (i.e. office)
Figure 8 - Changing the usage from office to residential
Figure 9 - Redefining the Building Model to assess viability implications
Figure 3 - Mix of residential units for rent â€“ using data regarding local prices provided by Rightmove and Zoopla.
Workflow 2: Unlocking new real estate opportunities Victoria Coach Station (right) is an art deco building opened in 1932, located at the intersection of Elizabeth Street and Buckingham Palace Road. It hosts 15 bus operators making it the largest coach station in London. With the emergence of on-demand transportation, automated fleet management and autonomous vehicles, a major disruption in the transport section is highly plausible, thus making the Victoria Coach Station a potential target for reconversion and refurbishment. In this case, a basic responsive scenario (stages in the right) was tested, where Victoria Station is gradually reconverted to:
Retail – at the Ground flood
Office – Floor 1 and 2
Residential – Floor 3, 4 and 5
As all buildings are linked in the plan and viability algorithms are checking potential opportunities in the back-end, when the residential floors are occupied by the new residents, it will notify the owner of the building across the street that there is a viable opportunity to reconvert the first floor to retail and/or leisure, as new demand is generated by the profile of the new residents and workers in the former Victoria Coach Station building. 24
6. Enabling resilience in extreme environments – McMurdo Station, Antarctica Key Facts about McMurdo Station: McMurdo has major strategic importance for the scientific research in Antarctica and the future of the United States Antarctic Program;
McMurdo is the main stop for a dynamic flux of people - connecting the South Pole, including tourists and non-scientific people that want to surpass records.
Population during summer time is reaching around 1000-1100 people – forming a vibrant community;
Built at the base of an active volcano - Mount Erebus; Key Issues and Critical Risks: Extended ecological footprint that affects the local ecosystem (Gramling, 2015)9;
McMurdo Station Masterplan 2.1 sets up a maximum capacity of 850 people and adding an extra provision of 200 beds in the Recreational area in case of contingencies (National Science Foundation, 2015). 9
Extreme warming that can melt the landing tracks (e.g. in 2017, Pegasus, the longest and most important land track was closed and had to be rebuilt);
Potential earthquakes - from 5 to 6 Richter; Adjacency of Mount Erebus – the second biggest active volcano in Antarctica.
Responsive Plan Preparation Based on the public information provided by scientific reports (Gramling, 2015) (Taylor & Francis, 2008) and the McMurdo Station Masterplan 2.1, a fragment of a potential responsive plan framework for McMurdo was developed (Figure 11 below) in order to address the current and future needs and to respond and mitigate potential threats caused by extreme conditions (e.g. eruptions, earthquakes, flooding, overcrowding). To have a consistent database that can be included and processed as part of the functional responsive plan, some GIS data was created using imagery provided by Masterplan 2.1 and Google Earth satellite imagery (Figure 12). Using OSM open data (GIS shapes and building heights) and CityEngine, the model of the existing station was developed (Figure 13) and the reports including the Gross Floor Area and Floor Area Ratio were retrieved. In order to compare the existing situation with the proposed masterplan, the newly created GIS data was imported in CityEngine and extruded using parametric rule scripts (Figure 14) and the reports exported for further scenario modeling. Enablers: CityEngine, Envision Tomorrow Prototype Building and Scenario Builder, QGIS, Google Earth Pro. Data used: Open Street Map GIS shapes (including max building height attribute) and own GIS data prepared from McMurdo Masterplan 2.1 plans and figures. 26
Potential risks caused by climate change and Mount Erebus were a strong reason in considering additional allocations to be included in developing scenarios that describe a critical situation: ď‚ˇ
Lodges A and B north of Lodge 1 of the proposed Masterplan 2.1,
2 additional potential areas for lodging (1.60 ha) and a command centre along existing trails.
Figure 11 â€“ Fragment of McMurdo Station potential Responsive Plan
Figure 12 - Mapped GIS files of McMurdo Station Masterplan 2.1 + additional Lodges A and B. Attributes added: Name, Status, Use, No of Levels, Percentage of risk and Priority for housing allocation
Figure 13- Existing McMurdo Station - extruded model in CityEngine using OSM open data and the information provided in McMurdo Station Masterplan 2.1 (National Science Foundation, 2015) – Lodges are marked with yellow
Figure 14 - Proposed McMurdo Station Masterplan 2.1 + Lodges A and B – simplified extruded model in CityEngine using the information provided in McMurdo Station Masterplan 2.1 (National Science Foundation, 2015) – Lodges and the unit allocated for contingencies (200 beds) are marked with yellow
Figure 4 - Proposed McMurdo Station Masterplan 2.1 + Lodges A and B – simplified extruded model in CityEngine using the information provided in McMurdo Station Masterplan 2.1 (National Science Foundation, 2015) – Lodges and the unit allocated for contingencies (200 beds) are marked with yellow
5D ‘Slicing’ using Scenario Modelling As the level of uncertainty in an unstable environment generates complex challenges in predicting the future accurately, ‘slicing’ the five-dimensional context in distinctive plausible scenarios generates a set of relevant parameters that can act as high-level markers in evaluating the current state. The parameters can be used further for scheduled monitoring and triggered actions (i.e. if certain thresholds or conditions are reached or exceeded). For McMurdo case study, five plausible scenarios were prepared in order to set up the expected parameters in terms of population and accommodation provision.
Scenario 1 – Existing Situation: Initial allocations
Scenario 2 – ‘As Expected’: Masterplan 2.1 allocations
Scenario 3 – ‘Winter is Coming’: Masterplan 2.1 + Contingencies allocations
Scenario 4 – ‘Unexpected Guests’: Masterplan 2.1 + Additional Lodges A and B
Scenario 5 – ‘Nature’s Wrath’: Masterplan 2.1 minus All Lodges + Contingencies + 2 additional potential areas for lodging and a command centre 32
Development types and Scenario modeling The data retrieved from CityEngineâ€™s reports was used to prepare the prototypes for different lodges of the plan (Figure 15), which were further imported in Envision Tomorrow Scenario Builder for all five scenarios (Table 2), generating the values for Population and No. of Units (Figure 16),
Figure 15 - Prototype Buidlings for McMurdo Station
Table 2 - Scenario Builder using Prototype Buildings
DEVELOPMENT TYPE EXISTING
Beds in case of Contingency Existing Berthing
MASTERPLAN 2.1 + CONTINGENCIES
MASTERPLAN 2 + LODGES A AND B
New Lodging: 2 Levels
New Lodging: 3 Levels
MASTERPLAN 2.1 LODGES + CONTINGENCIES + POTENTIAL
Figure 16 - Population and No. of Units
1,200 1,000 800
Figure 5 - Population and No. of Units
1,800 1,600 1,400 1,200 1,000 800 600 400
In Chapter 2, a series of questions were considered, triggered by the concepts of uncertainty and flexibility of potential timelines. Following the research and exploring the case studies approached through responsive plan preparation, some conclusions addressing the initial questions have been drawn:
1. How can we create plans that can be responsive to sudden changes? The plans can become responsive to sudden changes by integrating real time exchange of data and procedures between linked Assumptions, Aspirations, Actions and Actors. Ensuring the right balance between open data and custom access is important in order to ensure a cohesive functionality.
2. How can we use data and technology enablers in order to create responsive plans? Every case has its own particularities. Engaging the actors involved and exploring creative solutions and combination of tools and platforms that are familiar, compatible and viable is key.
3. Who can be involved in designing responsive plans? As the level of complexity is higher compared to the traditional planning approaches, it is important to also add specialists from other sectors than construction, planning and urban design â€“ e.g. coders, system architects, UX/UI designers and other professionals from the creative and innovation sector that can ensure a long term usability and viability of the plan.
4. When, where and how should we use a 5D planning approach? Implementing a 5D planning approach should happen only if the there is a high level of risk10 or if we are witnessing often changes11 that have sustainability and viability implications in the area. A 5D planning approach can have one or more uses: as a monitoring tool, automation system, engagement platform, facilitation enabler, open data source, management
crowdsourcing, crowdfunding, codesign, e-citizenship, e-democracy etc.
5. How a 5D plan approach can be implemented? The 5D plan can be implemented gradually, for instance by creating an initial network of the 4Aâ€™s and feeding it with data for monitoring purposes at first - only in those sections where often changes are expected. After that it, can be expanded further in terms of complexity, linked data, scripted actions and engaged platforms, using the elements of the former plan and updating the structure and the tools used when new innovations and capabilities are available. It can also be implemented as a completely new plan, by engaging a wide range on actors in the plan making process and establishing standards, triggered and scheduled revisions that should keep the plan up-to-date.
e.g. flooding, earthquakes, climate change, pollution, overcrowding, social tension, hurricanes e.g. areas with dynamic economy, vibrant communities, districts with accelerated progress, residential areas with high demand and potential supply, places with social instability, areas with mix of property uses subject to reconversions and renewal, culturally diverse neighbourhoods where planning policies might have to adapt quickly to satisfy local needs and aspirations. 10 11
7. A universal language of planning Designing a transparent and consistent digital structure of Assumptions, Aspirations, Actions and Actors, a distinctive language of planning is created, enhanced by transposing the content of the plan into code. As more and more information and processes are digitized (Tercek, 2015), having the capability of connecting the structure to different platforms12 opens a window of opportunity in establishing interdisciplinary and symbiotic collaboration across the world13. By creating a 5D responsive plan with well-established relational networks, integrating symbiotic collaboration becomes more accessible and more transparent, enabling secure exchanges of data14 between a wide range of actors: PEOPLE and PUBLIC SECTOR: citizens, city managers, local planning authorities, developers, investors, specialists (e.g. architects, planners, engineers, designers, lawyers).
consultancies, start-ups, SMEs, retail, infrastructure.
CITIES: e.g. part of the same district, region, urban system, country, or cross-border and international exchange.
ARTIFICIAL SYSTEMS: cognitive computing, machine learning, autonomous systems
e.g. platforms of processing, management, engagement, visualisation and interaction As the communication technologies are progressing, the symbiotic collaboration can be expanded outside the Earthâ€™s atmosphere, thus giving the possibility of connecting space stations and extraterrestrial colonies and sharing information, resources and technologies efficiently. In this way, a macro-system of engaged and interlinked plans from different types of settlements and geographies can be established. 14 e.g. blockchain technology 12 13
Strategic and political commitments are key for competitive cities. Collaboration has a major importance in creating prosperity and social cohesion and multiple types can be established using an integrated approach, for instance: 1. Collaboration between players involved in the planning process – by giving ranked access to the database and functionalities. 2. Collaboration between people with other people and people with interactive plans – engaging multiple disciplines and sectors of the population by connecting the plan to accessible platforms and apps. 3. Collaboration between companies with other companies, customers and cities – by exchanging information and connecting through social media. 4. Collaboration between two or more cities – by connecting the plans, thus supporting the Duty to Cooperate and also unlocking resources and opportunities in synergy 5. Collaboration between people and artificial cognition and automation – enhancing the monitoring15, analysis and decision making process by integrating cognitive computing, machine learning, automation and AI interfaces. On longer term, augmenting the plan with neural links, AI and sensors 16 might evolve in creating a sentient settlement that is becoming more and more selfaware by its own morphology, socio-economic and environmental interactions and concerns.
e.g. using real time imagery and data provided by automated Unmanned Aerial Vehicles – LIDAR, radar, vector shapefiles, raster. 16 Combining the sensors with biodegradable artificial brain cells scattered around the city provides a symbiotic and sustainable way of integrating Urban AI that can be innovated and renewed without generating major ecological footprint. 15
8. Technology-Augmented Planners for Creativity-Augmented Plans The 4th Industrial Revolution will generate an unprecedented impact in the economy and society as automation and AI are replacing existing jobs. According to PwC, â€œup to 30% of existing UK jobs could be impacted by automation by early 2030sâ€?17. If neural links will be largely adopted and diffused in the next years, the competition sparked by AI-augmented/connected workers might generate even wider replacements and mutations in multiple economic sectors. If during the first Industrial Revolutions, the machines were coming to replace the muscle power, we are expecting accelerated replacements of the intellectual capabilities as more technical tasks can be automated with the existing and future processing power. Focusing the human capabilities in collaborative and interdisciplinary engagement is essential. Emphasizing collaboration, creativity and empathy can support designing innovating ideas and finding ways to solve emerging issues through a combination of creative workflows and tools. Urban planning is a complex profession, struggling to keep up with the rate of innovation and disruption of other sectors. Many existing processes related to plan making and planning applications can and should be automated as the demand for innovation, integration, real-time updating and visualisation is getting higher in this fast-changing world with fast-changing needs and fast-changing commitments. The cities of tomorrow will be created by the people of today. In order to innovate planning, planners must innovate themselves by combining tools, learning new skills and engaging interdisciplinary support to make responsive and upgradable projects. When a significant amount of technical tasks will be automated, planners will have the chance to focus their time and knowledge on exploring solutions, simulating scenarios and engaging people.
UK Economic Outlook. Retrieved May 9, 2017, from http://www.pwc.co.uk/economic-
services/ukeo/pwc-uk-economic-outlook-full-report-march-2017-v2.pdf (PwC, 2017)
Architectural Design. (2013). System City - Infrastructure and the Space of Flows. (M. Weinstock, Ed.) Londra. Bar-Yam, Y. (2002). Complexity rising: From human beings to human civilization, a complexity profile. Encyclopedia of Life Support Systems. Retrieved from http://www.necsi.edu/projects/yaneer/Civilization.html Bayes, T. &. (1763). An Essay towards solving a Problem in the Doctrine of Chance. By the late Rev. Mr. Bayes, communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S. Philosophical Transactions of the Royal Society of London. Benveniste, G. V. (2013). How to succeed at technology push innovation? Retrieved from The Innovation and Strategy Blog: http://theinnovationandstrategyblog.com/2014/02/howto-succeed-at-technology-push-innovation/ Beqiri, R. (2016, May 4). Architecture and Urban Planning in the age of Artificial Intelligence. Retrieved from Future Architecture Platform: http://futurearchitectureplatform.org/news/28/ai-architecture-intelligence/ Bostrom, N. (2014). Superintelligence. Oxford: Oxford University Press. Brownlee, J. (2016, November 2). How To Implement The Perceptron Algorithm From Scratch In Python. Retrieved from Machine Learning Mastery: http://machinelearningmastery.com/implement-perceptron-algorithm-scratch-python/ Campbell, P. (2017, March 8). Self-driving cars and AI get boost from UK Budget. (Financial Times) Retrieved May 8, 2017, from https://www.ft.com/content/994ddf0a-6cbe-3fbd-8283deb954855c68 Christa Brelsford, T. M. (2015). The Topology of Cities. Santa Fe: Sante Fe Institute. CIA. (2014). The World Factbook - Exports - Partners. Retrieved from Central Intelligence Agency: https://www.cia.gov/library/publications/the-world-factbook/fields/2050.html#pl City of Westminster. (2016). Westminster City Plan. London. Retrieved from www.westminster.gov.uk/revision-westminsters-city-plan City of Westminster. (2017). City for All 2017/2018. London. David, T. (2015, January 5). In 10 years, your job might not exist. Hereâ€™s how to make sure youâ€™re still employable. Retrieved January 5, 2017, from The Washington Post: http://www.washingtonpost.com/posteverything/wp/2015/01/05/in-10-years-the-jobmarket-will-look-totally-different-heres-how-to-make-sure-youre-ready/?tid=sm_fb
Emerging Technology from the arXiv. (2017, January 13). Mathematical Model Reveals the Patterns of How Innovations Arise. Retrieved from MIT Technology Review: https://www.technologyreview.com/s/603366/mathematical-model-reveals-thepatterns-of-how-innovations-arise/ Emerging Technology from the arXiv. (2017, June). New Model of Evolution Finally Reveals How Cooperation Evolves. Retrieved from MIT Technology Review: https://www.technologyreview.com/s/608139/new-model-of-evolution-finally-revealshow-cooperation-evolves/ Emily Moir, Tim Moonen, Greg Clark. (2014). What are Future Cities? Origins, meanings and uses. London: Government Office for Science. Preluat pe iunie 2015, de pe https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/33754 9/14-820-what-are-future-cities.pdf ESRI. (2016). Python Scripting. Retrieved from City Engine: http://cehelp.esri.com/help/ ETH Zurich. (2014). ETHx Future Cities - edX MOOC. Finnerty, J. (2017, April 24). Driverless cars: everything you need to know about autonomous vehicles. (Auto Express) Retrieved May 8, 2017, from http://www.autoexpress.co.uk/cartech/85183/driverless-cars-everything-you-need-to-know-about-autonomous-vehicles Foreman, J. W. (2013). Using Data Science to Transform Information into Insight. Londra: Wiley. Foresight. (2014). A visual history of the future. London: Government Office for Science. Fregonese Associates. (2017, January 30). What is Scenario Planning. Retrieved from Envision Tomorrow: http://envisiontomorrow.org/what-is-scenario-planning/ Future Cities Catapult. (2016). User Research Insights Report - Prototyping the Future of Planning. London: Future Cities Catapult. Future of United States Antarctic Program. (2015, July 2). Future USAP. Retrieved from McMurdo: https://future.usap.gov/mcmurdo/ Geoffrey G. Parker, M. W. (2016). Platform Revolution. New York: W.W. Norton & Company. Google. (2017). Google Earth. Retrieved from https://earth.google.com Gramling, C. (2015, December 8). Overhaul in the works for aging U.S. Antarctic station. Retrieved from American Association for the Advancement of Science: http://sciencemag.org/news/2015/12/overhaul-works-aging-us-antarctic-station Harrel, W. (2014, august 17). Computers that calculate data at the speed of light could arrive as early as 2020. Preluat pe ianuarie 13, 2015, de pe Digital Trends: http://www.digitaltrends.com/computing/computers-that-calculate-data-at-the-speedof-light-could-arrive-as-early-as-2020/
House of Lords - Science and Technology Select Committee. (2017, March 15). UK Parliament Autonomous Vehicles Inquiry. Retrieved May 9, 2017, from http://parliament.uk/autonomous-vehicles IEEE. (2015, April). IEEEx Metrics for Smart Cities - edX MOOC. Retrieved April 2015 Jesus, C. D. (2016, December 3). Overpopulation: Where Do We Put the Next Billion Humans? Retrieved from Futurism: https://futurism.com/overpopulation-where-do-we-put-thenext-billion-humans/ Koenig, R. &. (2014). Evolutionary multi-criteria optimization for building layout planning: Exemplary application based on the PSSA framework. Preluat pe January 30, 2017, de pe ETH Zürich: http://e-collection.library.ethz.ch/eserv/eth:9019/eth-9019-01.pdf Krishna, A. (2017). The Power of Thinking Big: IBM Research’s “5 in 5”. Retrieved from https://medium.com/@IBMResearch/the-power-of-thinking-big-ibm-researchs-5-in-58f1450cfb1e0#.4qlx17t0q Local Government Association. (n.d.). Basic facts about Victoria. London. Lonhard, G. (2016). Technology vs. Humanity - The coming clash between man and machine. London: Fast Future Publishing Ltd. Maria Cerreta, P. D. (2012). Integrated Spatial Assessment (ISA): A Multi-Methodological Approach for Planning Choices. În D. J. Burian (Ed.), Advances in Spatial Planning. InTech. Mark Deakin & Husam Al Waer. (2011). From intelligent to smart cities. Intelligent Buildings International, 140-152. Marr, B. (2015). Big Data – Using SMART Big Data Analytics and Metrics to Make Better Decisions and Improve Performance. Londra: Wiley. Mayor of London. (2017, January 30). The London Plan. London: Greater London Authority. Retrieved from Mayor of London: https://london.gov.uk/sites/default/files/the_london_plan_2016_jan_2017_fix.pdf Moretti, E. (2012). The New Geography of Jobs. Houghton Mifflin Harcourt. National Platform for the Build Environment. (n.d.). ICT and Automation (ICTA) Scoping Study Report. National Science Foundation. (2015, December 16). McMurdo Station Masterplan 2.1. Retrieved from Future of the United States Antarctic Program: https://www.usap.gov/news/documents/McMurdoMasterPlan_2.1.pdf Nino, E. D. (2012). Evolutionary Algorithms Based on the Automata Theory for the MultiObjective Optimization of Combinatorial Problems. (D. O. Roeva, Ed.) Preluat pe January 2015, de pe http://www.intechopen.com/books/real-world-applications-of-genetic-
algorithms/evolutionary-algorithms-based-on-the-automata-theory-for-the-multiobjective-optimization-of-combina Pacione, M. (2010). Urban Geography - A Global Perspective. London and New York: Routledge. PwC. (2017, March). UK Economic Outlook. Retrieved May 9, 2017, from http://www.pwc.co.uk/economic-services/ukeo/pwc-uk-economic-outlook-full-reportmarch-2017-v2.pdf Scott-Morgan, D. P. (2012). The Reality of Our Global Future . Silva, N. W. (2010, May 20). Artificial Intelligence Solutions for Urban Land Dynamics: A Review. Retrieved from SAGE Journals: http://journals.sagepub.com/doi/abs/10.1177/0885412210361571 Sloane, P. (2011). A Guide to Open Innovation and Crowd Sourcing. London: Kogan Page Ltd. Stephen Goldsmith, S. C. (2014). The Responsive City. San Francisco: Jossey-Bass. Stewart, G. (n.d.). SW1 London Victoria Past/Present/Future. London: Victoria Business Improvement District. T.M. Vinod Kumar. (2015). E-Governance for Smart Cities. Singapore: Springer. Taylor & Francis. (2008). The historical development of McMurdo Station, Antarctica, an environmental perspective. Retrieved from Amundsen-Scott South Pole Station: http://www.southpolestation.com/news/mcmurdohistoryATCM34.pdf Tercek, R. (2015). Vaporized - Solid Strategies for Success in a Dematerialized World. LifeTree Media Ltd. Tibbalds. (2016). Victoria Neighbourhood Plan - Workshop Report. London. Verebes, T. (2014). Masterplanning the Adaptive City. New York: Routledge. Victoria Business Improvement District (BID). (2014). Victoria 2020 - A vision for a vibrant Victoria. London. West, G. (2017). Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies. New York: Penguin Random House.
Change of the use class, reconversion, refurbishment if approved.reduction_W && approved.growth_H: provision.W-=allocated.H else: recheck(allocated.W) recheck(allocated.H) recheck(DwgPerAnnum) // The script will check on a scheduled basis if there is an approved reduction of working space and simultaneously an approved growth for housing. If yes, the provision of work space will be reduced by the number of housing allocations. If not, the parameters regarding allocations for work, housing and DwellingsPerAnnum will be rechecked.
Check annual target for housing if DwgPerAnnum!=AnnualTarget.H: Objective.Dwg[CurrentYear]=0 else: Objective.Dwg[CurrentYear]=1 // The script will check on a scheduled basis if the annual target for housing is achieved. If not, the objective for the current year will be marked as unaccomplished. If yes, the objective for the current year will be marked as unaccomplished
Adjust number of polluting cars permitted in the area if CA<80%: polluting.cars-= excess.pollutingcars
else: recheck(pollutingcars.targets) // The script will adjust in real time the number of polluting cars permitted in the area depending on the percentage of CA (clean air).
Figure 17 - Source: McMurdo Station Masterplan 2.1 (National Science Foundation, 2015)
Adequate allocation of new residential use by reconversion if needed.reduction_L && needed.growth_H: provision.L-=allocated.H else: recheck(allocated.W) recheck(allocated.H) recheck(DwgPerAnnum) // The script will check on a scheduled basis if there is a need to reduce the space allocated for leisure and simultaneously a need to grow the housing allocation. If yes, the provision of leisure space will be decrease by the number of sqm allocated for reconversion to housing. If not, the allocations for work and housing and the number of DwellingsPerAnnum will be rechecked.
Adequate allocation of new dwellings if DwgPerAnnum[CurrentYear]>MaxAnnual.H[CurrentYear]: MaxAnnual.H[CurrentYear+1]-=DwgPerAnnum[CurrentYear] else: MaxAnnual.H[CurrentYear+1]+=MaxAnnual.H[CurrentYear]DwgPerAnnum[CurrentYear] // The script will check on a scheduled basis if there is a need to reduce the space allocated for leisure and simultaneously a need to grow the housing allocation. If yes, the provision of leisure space will be decrease by the number of sqm allocated for reconversion to housing.
Change building use for contingencies if floodrisk.A[i]>minimal_value: safety.A[i]=0 j=optimal_site(i) use.B[j]=H else: safety.A[i]=1 // The script will check in real time if the flood risk for a building exceeds the minimal value. If yes, its status will be switched to unsafe and an optimal site for that profile will be provided to change the use to housing. If not, the building will be marked as safe.