coupling with sleuth, the cvca and the dg-abc dr. elisabete a. silva [email protected]
DESCRIPTION
COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC Dr. Elisabete A. Silva [email protected] 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 - PowerPoint PPT PresentationTRANSCRIPT
AAG, NY, USAFeb. 28. 2012
COUPLING WITH SLEUTH, THE CVCA AND THE DG-ABC
Dr. Elisabete A. [email protected]
Department of Land EconomyUniversity of Cambridge
Association of American Geographers, NY, USAFeb. 28, 2012
AAG, NY, USAFeb. 28. 2012
OUTLINE
1. The CVCA model
2. The people’s model
3. The DG-ABC model
4. Future: The importance of hybrid models in urban planning
5. Concluding remarks
AAG, NY, USAFeb. 28. 2012
URBAN MODELSLEUTH
ENVIRONMENTAL MODEL CVCA
Expert Decision Input
PEOPLES’ MODEL
Data Base
Urban ExcludedSlope(RAN-REN)TransportationHilshade
Past
Time
Future
SWOT
AAG, NY, USAFeb. 28. 2012
urban roads slope
excluded hilshade
1975 1976
.
.
.
1997
test mode
calibrate
forecast
coarse
fine
final
DNA
data acquisition
1998 1999
.
.
.
. 2025
metrics images
images metrics
reclass excluded
calculate LA metrics
apply LA strategies
1998
1999
-
SLEUTH results CVCA results
workshop’s morning
workshop’s afternoon
SWOT analysis
Map-SWOT & analysis-critic
reclassify SLEUTH’s input data
urban roads slope
excluded hilshade
Keep existent DNA?
yes?
no?
new DNA
forecast
SLEUTH Urban Model
CVCA EnvironmentalModel
Expert Inclusion‘people’s model’
AAG, NY, USAFeb. 28. 2012
urban roads slope
excluded hilshade
1975 1976
.
.
.
1997
test mode
calibrate
forecast
coarse
fine
final
DNA
data aquisition
1998 1999
.
.
.
. 2025
metrics images
images metrics
reclass excluded
calculate LA metrics
apply LA strategies
1998
1999
CVCA - CA - Environmental Model
1. CVCA
AAG, NY, USAFeb. 28. 2012
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
4.Opportunistic 0 but NN = NNI (and no transition cell nearby) than link to nearest neighbor
5. Grow Goal or Result
D.Opportunistic
C. Offensive
B. Defensive
A. Protective
Core AreaBuffer Zone
Corridor
Supporting Landscape MatrixNon-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
AAG, NY, USAFeb. 28. 2012
Metric - AMP Value
Edges 14964
Area 24204
Num Clusters 708
MCS 34
MPS 275
LSI 7.7
MNND 1.5
Metric - AML Value
Edges 35171
Area 106460
Num Clusters 1134
MCS 93
MPS 577
LSI 9.9
MNND 1.6
AAG, NY, USAFeb. 28. 2012
CVCA Simulation
AAG, NY, USAFeb. 28. 2012
urban roads slope
excluded hilshade
1975 1976
.
.
.
1997
test mode
calibrate
forecast
coarse
fine
final
DNA
data acquisition
1998 1999
.
.
.
. 2025
metrics images
images metrics
reclass excluded
calculate LA metrics
apply LA strategies
1998
1999
-
SLEUTH results CVCA results
workshop’s morning
workshop’s afternoon
SWOT analysis
Map-SWOT & analysis-critic
reclassify SLEUTH’s input data
urban roads slope
excluded hilshade
Keep existent DNA?
yes?
no?
new DNA
forecast
2. The People’s model
AAG, NY, USAFeb. 28. 2012
TWO MAP DRAWINGS RESULTING FROM THE WORKSHOP’S AFTERNOON
AAG, NY, USAFeb. 28. 2012
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
AAG, NY, USAFeb. 28. 2012
AAG, NY, USAFeb. 28. 2012
3. DG-ABC MODEL
3.1 Concept model of DG-ABC model
Intelligent agents Cellular automata TPB model Genetic algorithm
Dynamics capturing
a-spatial dynamics spatial dynamics behavioural regulations
behavioural optimizations
Factors oriented
social-economic influences
infrastructures/ecosystems
behaviours of agents behaviours of agents
Level individual individual individual level high level
changes alter behaviours by GA and themselves
neighbourhoods navigation
N/A evolution by themselves
Data requirement
social-economics /policies quantifying
GIS data agent’s beliefs/ profile information
strategies/options
Integrated model
AAG, NY, USAFeb. 28. 2012
3. DG-ABC MODEL
3.2 DG-ABC model
1. Model Environment2. Heterogeneous agents3. CA (SLEUTH)4. Decision behaviors5. Interactions6. 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
AAG, NY, USAFeb. 28. 2012
3. DG-ABC MODEL
3.3 Theory of Planned Behavior
1 2 3( ) max{ , , .......... }nD f B Be Be Be Be
atraffic environment convenience tijEI a E b E c E
1 2 1 2Be W I W AbC W I W EI
a i iai p Ctd ctAp AW WA
1( ) /
na a ii ij ji neighbor
jSN M Inf N
1
ma a ai ik ki
kPBC Cb P
I
•A: the degree to which the performance of the behaviour ispositively or negatively valued.
•SN: an agent’s perception of social normative pressures, orrelevant others’ beliefs that the agent should (not) performsuch 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 givenbehaviour.
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
Intention
Behavior
Actual Behavioral Control
2 311 2 3( )highway citycenterroad
B D B DB Dt t ttrafficE w A e w A e w A e
AAG, NY, USAFeb. 28. 2012
Properties of resident agents
Properties of property developer agents
Properties of government developer agents
AAG, NY, USAFeb. 28. 2012
Spatial synchronization in the model
Temporal synchronization in the model
AAG, NY, USAFeb. 28. 2012
(a) run CA standby (b) run agents standby
(c) run integrated model (d) real urban data
AAG, NY, USAFeb. 28. 2012
4. FUTURE….HEXA-DPI
AAG, NY, USAFeb. 28. 2012
AAG, NY, USAFeb. 28. 2012
HEXA-DPI Data Structure and DME – Dynamic Model Environment
AAG, NY, USAFeb. 28. 2012
Key Issues:
-Historical expertise
-Interoperability.
-Spatial and A-Spatial dynamics
-MAUP- Transition matrix
- FUTURE…………. HEXA-DPI
AAG, NY, USAFeb. 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-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
2010 A 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.