Adaptive planning policy - building and managing adaptive cities with adaptive policy using dynamic simulation

Transcription

1 Adaptive planning policy - building and managing adaptive cities with adaptive policy using dynamic simulation University of New South Wales UNSW Built Environment City Futures Centre 25th March 2015, Sydney, Australia Dr. Elisabete A. Silva es424@cam.ac.uk Senior Lecturer - Associate Professor Department of Land Economy & Robinson College University of Cambridge

2 OUTLINE 1. Dynamic Simulation: CA-ABM 2. Complexity theory: The right moment in time to link planning decision and urban models 3. Key areas to address: Calibration (temp-spatial), validation, randomness, uncertainty, data-mining, big data, temp/space variation, adaptive policy (utility+game) 4. The examples of models: The SLEUTH model; The CVCA model; DGABC; CCID model; IUBEA; climate change negotiation and COP 5. Concluding remarks

3 1. Dynamic Simulation

4 1. Complexity theory: The right moment in time to link planning decision and urban models Mismatch between technology, theory, data of the 70s resulted in Lees Requiem for large scale models XXI century of Big Data, high computation capability, vast numbers of experts, more data-aware policy, metrics, calibration, validation, randomness, scalability Key contributions: Von Neumann and Morgenstern (1944, 1966), Ulam (1960, 1974), Turing (1941), Prigogine (1977, 1999, 1984), Tobler and Burks (1979), Kauffman (1984, 1993),Wolfram (1994), Holland (1995, 1999), and Crutchfield (1995); John Nash exploring research results by Merrill Flood and Melvin Dresher at RAND corporation (1950s)

5 Theory Processes Models focus Physical /regions Systems theory Rational Planning Deterministic time 50s Advocacy of Planning People/ social Beyond modernity Participative Planning Incremental Planning Mix-Scanning (Zoom in-out, Stochastic Top/down-b/up) Feedback loops of learn/adapt/enrich_knowledge Complexity Theory present

6 Starting the study of complex systems in Spatial Analysis. Waldo Tobler in contact with Arthur Burks was exposed to Von Neumann s works, and published Cellular Geography (1979). At NCGIA-Santa Barbara, Helen Couclelis and Keith Clarke, published respectively Cellular Worlds (Couclelis, 1985) and SLEUTH the first fully operational and implementable CA (Clarke and Gaydos, 1998). Michael Batty initially at NCGIA-Buffalo and afterwards at CASA-UCL, developed the theory and practice that culminated in the publication of the seminal books Fractal Cities (1994) and Cities and complexity (2005). ES = 3 rd Generation (consolidation, re-assemblage, expansion, big-data, randomness, validation, calibration, standardization)

7 CAs (raster base) (i) A grid or raster space organised by cells which are the smallest units in that grid/space; (ii) (ii) Cell States cells must manifest adjacency or proximity. The state of a cell can change accordingly to transition rules, which are defined in terms of neighbourhood functions; (iii) (iii) The neighbourhood and dependency of the state of any cell on the state and configuration of other cells in the neighbourhood of that cell; (iv) (iv) Transition rules that are decision rules or transition functions of the CA model and can be deterministic or stochastic; (v) (v) Sequences of time steps. When activated, the CA proceeds through a series of iterations Cell base study of random complex CA came an understanding of its basic patterns: as they appear to fall into four qualitative classes, in what concerns one-dimension (1-D) CA evolution leads to: (i) a homogenous state; (ii) a set of separated simple stable or periodic structures; (iii) a chaotic pattern; (iv) complex localised structures, sometimes long-lived (Wolfram, 1984:5)

8

9 ABM-GAs (Vector-object based) are constituted of: (i) agents that do not have the constraints of neighbourhood effects, (ii) behavioural roles among agents and the environment itself, (iii) independence from central command/control, but able to act if action at a distance is required, (iv) states of agents tend to represent behavioural forms. The most basic model environment of an ABM-GA will have a set of attributes per agent (or group of agents), (one)a set of decision trees and trigger points that will allow to set the context for a new movement (upgrade of the spatial/temporal environment) in time/space. Object based

10

11

12 2013 Selecting artificial intelligence urban models using waves of complexity. Urban Design and Planning. 166 (1): Surveying Models in Urban Land Studies. (with N. Wu) Journal of Planning Literature.27 ( ): Artificial intelligence solutions for Urban Land Dynamics: A Review (with N. Wu). Journal of Planning Literature : Complexity, Emergence and Cellular Urban Models: Lessons Learned from Appling SLEUTH to two Portuguese Cities. (with K. Clarke) European Planning Studies, 13 (1): ISSN: The DNA of our Regions: artificial intelligence in regional planning. Futures, 36(10): ISSN: Measuring space: a review of spatial metrics for urban growth and shrinkage (with J. Reis). In: The Routledge Handbook of Planning Research Methods (Eds. Elisabete A. Silva, Patsy Healey, Neil Harris and Pieter van den Broeck), Routledge pages

13 What Models?

14 What Algorithms?

15 What Metrics?

16 Operational Dynamic Urban Models

17 images metrics coarse fine final DNA test mode calibrate forecast urban roads slope excluded hilshade data acquisition 1.1SLEUTH Urban Model Keith Clarke, E. A Silva SLEUTH results CVCA results workshop s morning images metrics apply LA strategies forecast yes? no? new DNA calculate LA metrics Keep existent DNA? SWOT workshop s analysis reclassify SLEUTH s afternoon input data Map - SWOT & analysis - critic Adaptive Planning Policy, UNSW, Future Cities Centre, Sydney, - Australia reclass excluded urban roads slope excluded hilshade 1.2.CVCA Environmental Model E. A. Silva, J. Ahern, J. Wileden 1.3.Expert Inclusion people s model E. A. Silva, J. Mullin

18 1.1 - SLEUTH

19 images metrics coarse fine final DNA apply LA strategies test mode calibrate forecast calculate LA metrics urban roads slope excluded hilshade data aquisition reclass excluded CVCA - CA - Environmental Model 2008 Strategies for Landscape Ecology in Metropolitan Planning Applications Using Cellular Automata Models. (with J. Wileden, J. and J. Ahern), Progress in Planning, 70(4): ISSN: CVCA Model images metrics

20 Transition Rules: Number of pixels (pixels with a probability of change to urban) Action step A. Protective Desired network elements are identified and protected through planning policy and land use control in advance of negative landscape matrix changes. 1. Protective 0 but NN > MNND than add protective pixels around all outer patch and add protective pixels until arriving at closest neighbor 2.Defensive <=50% *,** than add defensive pixels to all outer patch cell where transition cell exists 3.Offensive >50% add offensive pixel to all outer patch cells and add offensive cells until nearest neighbor 4.Opportunistic 0 but NN = NNI (and no transition cell nearby) than link to nearest neighbor B. Defensive C. Offensive D.Opportunisti c Isolated core area in non-supportive landscape matrix is subject to isolation from disturbance to corridors and to incremental reduction in size of the core area that can be protected through a new buffer zone. Isolated core area is protected with a buffer zone and linked into a greenway network with corridors that are newly developed within a non-supportive landscape matrix context. The offensive strategy employs a range of tactics, including nature development, to achieve a desired landscape configuration. Isolated core area is linked with an existing corridor, buffered, and anew supporting landscape matrix is developed. The opportunistic strategy takes advantage of unique circumstances that may only support some greenway uses, e.g. recreation. 5. Grow Existing Goal or Result Landscape Core Area Buffer Zone Corridor Supporting Landscape Matrix Non-Supporting Landscape Matrix

21 CVCA Simulation

22 1. The image of the city The image of a city-region Identification/quantification Urban forms (existent /possible) same future different simulations 22

23 Helin Liu, Qian Wang, Elisabete A. Silva 2. CI Agent Base Model new policies or CIs, CWs nd new landse plan variation in CIs firms number & size influence upon land-use type and citizens of neighbouring plots variation in CWs population locational determinants for CWs housing choosing spatial structure of CIs firms spatial structure of CWs habitation determinants for CIs firms location choosing demand for office interaction & feedback development of CIs interaction & feedback demand for CWs interaction & feedback demand for housing supply new housing estate existing housing estate advocating, regulating & controlling supply advocating, regulating & controlling urban government existing housing estate new housing estate expectation & feedback expectation & feedback to accept compensation land expropriation, urban regeneration compensation to accept individual citizens to refuse derelict factories, warehouses slum, illegal buildings dilapidated housing area farmland, unworked acres to refuse individual citizens

24 2013 Simulating the dynamics between the development of creative industries and urban spatial structure: an agent-based model (with H. Liu). S. Geertman et al. (eds.), Planning Support Systems for Sustainable Urban Development, Lecture Notes in Geoinformation and Cartography, DOI: / _4, _ Springer-Verlag Berlin Heidelberg, pp

25

26

27

28 3. Agents and terms for negotiation Agents:196 countries Annex1 (42) No Annex1 (149) Others (5) Terms for negotiation Technology trade Carbon trade GDP growth support Negotiation rules Doug Crawford-Brown Helin Liu Elisabete A. Silva

29 Condition-action rules Not included in the Negotiation process, Focusing on GDP growth Negotiation, aiming to lift GDP growth rate Negotiation, to lift GDP growth, consider carbon reduction Negotiation, following current agreements and strategies Negotiation, aiming to carbon reduction

30

31 Three scenarios Parameter s name Connection to the dynamics scenario A: Low Ambition gdp-per-capita-emissionconcerned (USD/Capita*Year) carbon-per-capita-critical value (t CO 2 /Capita*Year) critical-carbon-emission-pergdp (t CO 2 /USD) critical-minimum-co2pergdp (t CO 2 /USD) mean-agreement-duration (Years) gdp-growth-rate-decreaserate-critical (%/Year) carbon-emission-increaserate-critical (%/Year) A global variable for recording the critical value for GDP/capita. Countries with GDP/capita lower than this value will not join in the bilateral negotiation. A global variable for recording the critical value for carbon emission / capita. Only countries with carbon emission/capita lower than this value can be free from the responsibility to reduce carbon emission. A global variable for recording the critical value for carbon emission / GDP. Each country involved in the negotiation process is responsible to reduce its carbon emission intensity to a level lower than this value A global variable to describe the ultimate ability of our society to reduce carbon emission / GDP A global variable to describe the average duration of the agreements signed between two countries. The duration of each signed agreement may be different but their mean duration is constrained to this critical value. A global variable for recording the acceptable decrease rate of GDP growth rate for all the countries due to signed agreements. A country will not sign too many agreements which may reduce its GDP growth rate by more than this critical value. A global variable for recording the acceptable increase rate of carbon emission for all the countries due to signed agreements. A country will not sign too many agreements which may increase its GDP growth rate by more than this critical value. scenario B: Medium Ambition non-annex 1 Scenario C: High Ambition

32 Scenario analysis: Average GDP growth rate (%) High ambition medium ambition Low ambition 6 7

33 4 - An Integrated Spatial Analysis Environment for Urban-Building Energy Analysis in Cities (I-UBEA) Sun, Y. ; W. R. Choudhary; E.A. Silva and W. Tian Research objectives To track energy change Energy change of London Energy change per local authority / buildings To estimate and analyze Energy Usage Intensities (EUI) EUI of Local Authorities EUI of sub-categories of buildings To explain energy consumption Explain how the distribution of land use influences energy consumption in local authorities and the entire city of London Explain how the distribution of floor area influences energy consumption To evaluate energy performance Evaluate energy performance of London while adapting different energy change policies. Interactive Simulation Model for predicting of energy performance in the future on the basis of population change.

34 Department of Engineering Department of Land Economy Function Overview of I-UBEA GIS Boundary Data Attribute Data GIS Data Visualization & Data query in different spatial scales, for both polygon and point data I-UBEA Policy impact simulation and energy prediction (with population dynamic using statistics model) GIS Boundary Data Attribute Data Energy & EUI Attribute Data Explain energy consumption with attribute data by using Statistics Analysis

35 Energy scenario analysis (what-if analysis) This method is more flexible compared to regression analysis because it is suitable for the situation where energy consumption data are unavailable in some spatial scales or some areas of a city. Baseline scenario The baseline scenario is based on prior knowledge on energy use intensity in terms of building types. Three methods are provided: benchmark values median values Monte Carlo method Analysts may choose one or all these three methods based on data availability. (1) Electrification (2) Building type conversion (3) Energy efficient This scenario explores how the total energy or carbon emissions would change if building sector would use electricity instead of gas for heating. This Scenario investigates the change of building types on the energy consumption in cities. This scenario allows analysts studying the influences of change of energy use intensities for different building types. Note: The benchmark values are based on low, typical, and high values (EUI) from previous literature.

36 Model function: Energy optimization Data: UKMap, floor area of London, Gas Energy consumption for each MSOA in London. I-UBEA Calculate EUI for MSOA and find polygon with best EUI. calculate area percentage for all polygons. pick best performing ratio and calculate mean ratio. calculate difference to best performing ratio and difference to mean ratio, respectively, for compositional data. calculate overall difference to best performing ratio and to mean ratio, respectively. Part 1. display overall difference to best performing ratio with scaled color Part 2. display overall difference to mean ratio with scaled color Note: Each part of step 6 can be executed independently.

37 Result: Energy optimization based on building types at MSOA level in London To compute the overall difference of floor area percentage to best-performing area in terms of gas use intensity at London MSOA level. The overall differences of floor area percentage based on gas use intensity still presents the characteristics of spatial distribution to some extent, though not clustered as large area. Spatial distribution demonstrates that energy consumptions in an area are to some extent influenced by where the area is located in a city. best-performing MSOA MSOAs with similar overall difference are likely to cluster together. MSOA without gas consumption data small difference large difference

38 Some Papers 2013 Simulating the dynamics between the development of creative industries and urban spatial structure: an agent-based model (with H. Liu). S. Geertman et al. (eds.), Planning Support Systems for Sustainable Urban Development, Lecture Notes in Geoinformation and Cartography, DOI: / _4, Springer-Verlag Berlin Heidelberg pp Selecting artificial intelligence urban models using waves of complexity. Urban Design and Planning. 166 (1): Surveying Models in Urban Land Studies. (with N. Wu) Journal of Planning Literature.27 ( ): Artificial intelligence solutions for Urban Land Dynamics: A Review (with N. Wu). Journal of Planning Literature : Strategies for Landscape Ecology in Metropolitan Planning: Applications Using Cellular Automata Models. (with J. Wileden, J. and J. Ahern), Progress in Planning, 70(4): ISSN: Complexity, Emergence and Cellular Urban Models: Lessons Learned from Appling SLEUTH to two Portuguese Cities. (with K. Clarke) European Planning Studies, 13 (1): ISSN: The DNA of our Regions: artificial intelligence in regional planning. Futures, 36(10): ISSN: Calibration of the SLEUTH Urban Growth Model for Lisbon and Porto, Portugal. (with K. Clarke) Computers, Environment and Urban Systems, 26 (6): ISSN:

39 Some book chapters 2014 Measuring space: a review of spatial metrics for urban growth and shrinkage (with J. Reis). In: The Routledge Handbook of Planning Research Methods. (Eds. Patsy Healey, Neil Harris and Pieter van den Broeck), Routlege 2014 DG-ABC: An Integrated multi-agent and cellular automata urban growth model (with N. Wu). Technologies in Urban and Spatial Planning: Virtual Cities and Territories (Eds. Nuno Norte Pinto, José António Tenedório, António Pais Antunes and Josep Roca ), IGI-Global, pp Simulating the dynamics between the development of creative industries and urban spatial structure: an agent-based model (with H. Liu). S. Geertman et al. (eds.), Planning Support Systems for Sustainable Urban Development, Lecture Notes in Geoinformation and Cartography, DOI: / _4, _ Springer-Verlag Berlin Heidelberg, pp Cellular Automata Models and Agent Base Models for urban studies: from pixels, to cells, to Hexa-Dpi s. In: Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. Edited by: Dr. XiaojunYang. Wiley-Blackwell. pp ISBN: Waves of complexity. Theory, models, and practice. In: Roo, Gert de, and Elisabete A. Silva (2010), A Planner s Encounter with Complexity, Ashgate Publishers Ltd, Aldershot (UK). pp ISBN: Complexity and CA, and application to metropolitan areas. In: Roo, Gert de, and Elisabete A. Silva (2010), A Planner s Encounter with Complexity, Ashgate Publishers Ltd, Aldershot (UK). pp ISBN:

40 Elisabete LISA Lab New Book: "The Routledge Handbook of Planning Research Methods"