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AAG, NY, USA Feb. 28. 2012 COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva Department of Land Economy University of.

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Presentation on theme: "AAG, NY, USA Feb. 28. 2012 COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva Department of Land Economy University of."— Presentation transcript:

1 AAG, NY, USA Feb. 28. 2012 COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva es424@cam.ac.uk Department of Land Economy University of Cambridge Association of American Geographers, NY, USA Feb. 28, 2012

2 AAG, NY, USA Feb. 28. 2012 OUTLINE 1.The CVCA model 2.The peoples model 3.The DG-ABC model 4.Future: The importance of hybrid models in urban planning 5.Concluding remarks

3 AAG, NY, USA Feb. 28. 2012 URBAN MODEL SLEUTH ENVIRONMENTAL MODEL CVCA Expert Decision Input PEOPLES MODEL Data Base Urban Excluded Slope(RAN-REN) Transportation Hilshade PastTimeFuturePastTimeFuture SWOT

4 AAG, NY, USA Feb. 28. 2012 SLEUTH Urban Model CVCA Environmental Model Expert Inclusion peoples model

5 AAG, NY, USA Feb. 28. 2012 1. CVCA

6 AAG, NY, USA Feb. 28. 2012 Transition Rules: Number of pixels (pixels with a probability of change to urban) Action step 1. Protective0 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.Opportunistic0 but NN = NNI(and no transition cell nearby) than link to nearest neighbor 5. Grow Goal or Result D.Opportunisti c C. Offensive B. Defensive A. Protective Core Area Buffer Zone Corridor Supporting Landscape Matrix Non-Supporting Landscape Matrix Desired network elements are identified and protected through planning policy and land use control in advance of negative landscape matrix changes. 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. Existing Landscape

7 AAG, NY, USA Feb. 28. 2012 Metric - AMPValue Edges14964 Area24204 Num Clusters708 MCS34 MPS275 LSI7.7 MNND1.5 Metric - AMLValue Edges35171 Area106460 Num Clusters1134 MCS93 MPS577 LSI9.9 MNND1.6

8 AAG, NY, USA Feb. 28. 2012 CVCA Simulation

9 AAG, NY, USA Feb. 28. 2012 2. The Peoples model

10 AAG, NY, USA Feb. 28. 2012 TWO MAP DRAWINGS RESULTING FROM THE WORKSHOPS AFTERNOON

11 AAG, NY, USA Feb. 28. 2012 StrengthsVotes % Weaknessvotes % OpportunitiesVotes % Threatsvotes Transport system (road network, airport, harbor) 19.5Mobility, accessibility and transport 32.6Improve transportation system 17.6Uncontrolled urban sprawl 29.9 Tourism and world heritage (Lisbon and Porto) 17.8Lack of urban quality 17.0Urban renewal15.7Natural risks (e.g. coastal, flooding, earthquake) 16.4 Capital city13.0Uncontrolled urban sprawl 11.3Cultural tourism/ events 11.8Urban violence and drugs 14.2 SWOT RESULTS

12 AAG, NY, USA Feb. 28. 2012

13 AAG, NY, USA Feb. 28. 2012 3. DG-ABC MODEL 3.1 Concept model of DG-ABC model Intelligent agentsCellular automataTPB modelGenetic algorithm Dynamics capturing a-spatial dynamicsspatial dynamicsbehavioural regulations behavioural optimizations Factors oriented social-economic influences infrastructures/ ecosystems behaviours of agents Levelindividual individual levelhigh level changesalter behaviours by GA and themselves neighbourhoods navigation N/Aevolution by themselves Data requirement social-economics /policies quantifying GIS data agents beliefs/ profile information strategies/options Integrated model

14 AAG, NY, USA Feb. 28. 2012 3. DG-ABC MODEL 3.2 DG-ABC model 1.Model Environment 2.Heterogeneous agents 3.CA (SLEUTH) 4.Decision behaviors 5.Interactions 6.Synchronization The key decision tables: The Resident agents utility table. The developer agents development application table. The government agents approving table. Synchronization decision table. spatial data Source: Ning Wu and Elisabete A. Silva 2010a

15 AAG, NY, USA Feb. 28. 2012 3. DG-ABC MODEL 3.3 Theory of Planned Behavior A: the degree to which the performance of the behaviour is positively or negatively valued. SN: an agents perception of social normative pressures, or relevant others beliefs that the agent should (not) perform such behaviour. PBC: an individuals perceived ease or difficulty of performing the particular behaviour. I: an indication of a agents readiness to perform a given behaviour. Behavioral Beliefs Normative Beliefs Subjective Norm Perceived Behavioral Control Control Beliefs Intention Actual Behavioral Control Attitude toward the behavior Behavior TpB model (Icek Ajzen 2006) Attitude toward the behavior Subjective Norm Perceived Behavioral Control IntentionBehavior Actual Behavioral Control

16 AAG, NY, USA Feb. 28. 2012 Properties of resident agents Properties of property developer agents Properties of government developer agents

17 AAG, NY, USA Feb. 28. 2012 Spatial synchronization in the model Temporal synchronization in the model

18 AAG, NY, USA Feb. 28. 2012 (a) run CA standby (b) run agents standby (c) run integrated model (d) real urban data

19 AAG, NY, USA Feb. 28. 2012 4. FUTURE….HEXA-DPI

20 AAG, NY, USA Feb. 28. 2012

21 AAG, NY, USA Feb. 28. 2012 HEXA-DPI Data Structure and DME – Dynamic Model Environment

22 AAG, NY, USA Feb. 28. 2012 Key Issues: - Historical expertise -Interoperability. -Spatial and A-Spatial dynamics -MAUP -Transition matrix -FUTURE…………. HEXA-DPI

23 AAG, NY, USA Feb. 28. 2012 REFERENCES 2012 (forthcoming) Surveying Models in Urban Land Studies. Journal of Planning Literature. ( with N. Wu) 2011 Cellular Automata Models and Agent Base Models for urban studies: from pixels, to cells, to Hexa-Dpis. In: Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment. Edited by: Dr. XiaojunYang. Wiley-Blackwell. pp. 323- 345. ISBN: 978-0-470-74958-6 2010 A Planners Encounter with Complexity, (with G. de Roo) Ashgate Publishers Ltd, Aldershot (UK). 337 pages. ISBN: 978-1- 4094-0265-7. http://www.ashgate.com/isbn/9781409402657 2010 Waves of complexity. Theory, models, and practice. In: Roo, Gert de, and Elisabete A. Silva (2010), A Planners Encounter with Complexity, Ashgate Publishers Ltd, Aldershot (UK). pp. 309-331.. ISBN: 978-1-4094-0265-7 2010 Complexity and CA, and application to metropolitan areas. In: Roo, Gert de, and Elisabete A. Silva (2010), A Planners Encounter with Complexity, Ashgate Publishers Ltd, Aldershot (UK). pp..187-207. ISBN: 978-1-4094-0265-7 2010 Artificial intelligence solutions for Urban Land Dynamics: A Review. (with N.Wu) Journal of Planning Literature. 2010 24: 246- 265. ISSN: 0885-4122 2009 A Traffic Analysis Zone Definition: A New Methodology and Algorithm. (with LM Martinez and JM Viegas). Transportation. 36 (5): 581. 0049-4488 (Print) 1572-9435 (Online). DOI: 10.1007/s11116-009-9214-z 2009 (online 2008, print 2009), Modifiable Areal Unit Problem Effects on Traffic Analysis Zones Delineation. (with LM Martinez and JM Viegas) Environment and Planning B – Planning and Design. 36(4): 625-643 ( advance online publication, doi:10.1068/b34033. ISSN: 0265-8135 (print) ISSN: 1472-3417 (electronic - http://www.envplan.com/abstract.cgi?id=b34033). 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):133-177 - ISSN: 0305-9006 2007 Zoning Decisions in Transport Planning and their Impact on the Precision of Results. (with LM Martinez and JM Viegas)Transportation Research Record, 1994 (08): 58-65 - ISSN: 0361-1981 2005 Complexity, Emergence and Cellular Urban Models: Lessons Learned from Appling SLEUTH to two Portuguese Cities. (with K. Clarke) European Planning Studies, 13 (1): 93-115 – ISSN: 0965-4313 2004 The DNA of our Regions: artificial intelligence in regional planning. Futures, 36(10):1077-1094. – ISSN: 0016-3287 2002 Calibration of the SLEUTH Urban Growth Model for Lisbon and Porto, Portugal. Computers, (with K. Clarke) Environment and Urban Systems, 26 (6): 525-552 - ISSN: 0198-9715 Ajzen, I. 1985, From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp:11- 39). Heidelberg, Germany: Springer.


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