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COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva es424@cam.ac.uk

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. OUTLINE The CVCA model The people’s model The DG-ABC model

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COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva es424@cam.ac.uk

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  1. 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. OUTLINE • The CVCA model • The people’s model • The DG-ABC model • Future:The importance of hybrid models in urban planning • Concluding remarks

  3. Data Base Expert Decision Input PEOPLES’ MODEL P a s t T i me Fu t u r e Urban Excluded Slope(RAN-REN) Transportation Hilshade URBAN MODEL SLEUTH ENVIRONMENTAL MODEL CVCA SWOT

  4. 1975 urban 1976 test roads . mode slope . coarse excluded . hilshade fine calibrate 1997 final data acquisition images metrics DNA forecast 1998 1998 apply LA calculate reclass SLEUTH 1999 strategies LA metrics excluded results 1999 . CVCA . yes? results . . Keep no? forecast 2025 existent workshop’s DNA? morning new DNA urban images metrics roads slope SWOT excluded reclassify analysis workshop’s hilshade SLEUTH’s afternoon input data Map - SWOT & analysis - critic - SLEUTH Urban Model CVCA Environmental Model Expert Inclusion ‘people’s model’

  5. 1. CVCA

  6. Transition Rules: Number of pixels (pixels with a probability of change to urban) Action step 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 A. Protective 4.Opportunistic 0 but NN = NNI (and no transition cell nearby) than link to nearest neighbor Desired network elements are identified and protected through planning policy and land use control in advance of negative landscape matrix changes. 5. Grow B. Defensive 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. C. Offensive D.Opportunistic 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 Goal or Result Core Area Supporting Landscape Matrix Buffer Zone Non-Supporting Landscape Matrix Corridor

  7. CVCA Simulation

  8. 1975 urban 1976 test roads . mode slope . coarse excluded . hilshade fine calibrate 1997 final data acquisition images metrics DNA forecast 1998 1998 apply LA calculate reclass SLEUTH 1999 strategies LA metrics excluded results 1999 . CVCA . yes? results . . Keep no? forecast 2025 existent workshop’s DNA? morning new DNA urban images metrics roads slope SWOT excluded reclassify analysis workshop’s hilshade SLEUTH’s afternoon input data Map - SWOT & analysis - critic - 2. The People’s model

  9. TWO MAP DRAWINGS RESULTING FROM THE WORKSHOP’S AFTERNOON

  10. Strengths Votes % Weakness votes % Opportunities Votes % Threats votes Transport system (road network, airport, harbor) 19.5 Mobility, accessibility and transport 32.6 Improve transportation system 17.6 Uncontrolled urban sprawl 29.9 Tourism and world heritage (Lisbon and Porto) 17.8 Lack of urban quality 17.0 Urban renewal 15.7 Natural risks (e.g. coastal, flooding, earthquake) 16.4 Capital city 13.0 Uncontrolled urban sprawl 11.3 Cultural tourism/ events 11.8 Urban violence and drugs 14.2 SWOT RESULTS

  11. 3. DG-ABC MODEL 3.1 Concept model of DG-ABC model Integrated model

  12. 3. DG-ABC MODEL 3.2 DG-ABC model • Model Environment • Heterogeneous agents • CA (SLEUTH) • Decision behaviors • Interactions • Synchronization The key decision tables: • The Resident agents’ utility table. • The developer agents’ development application table. • The government agent’s approving table. • Synchronization decision table. spatial data Source: Ning Wu and Elisabete A. Silva 2010a

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

  14. Properties of government developer agents Properties of resident agents Properties of property developer agents

  15. Spatial synchronization in the model Temporal synchronization in the model

  16. (b) run agents standby (a) run CA standby (d) real urban data (c) run integrated model

  17. 4. FUTURE….HEXA-DPI

  18. HEXA-DPI Data Structure and DME – Dynamic Model Environment

  19. Key Issues: -Historical expertise -Interoperability. -Spatial and A-Spatial dynamics -MAUP • Transition matrix • FUTURE…………. HEXA-DPI

  20. 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-Dpi’s. 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 2010A Planner’s 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 Planner’s 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 Planner’s 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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