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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"
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Map comparison methods for comprehensive assessment of geosimulation models
Alex Hagen-Zanker1,2, Pim Martens3
4. Research Institute for Knowledge Systems5. Urban Planning Group, TU/e6. International Centre for Integrated
assessment & Sustainable development
/ Urban Planning Group PAGE 2ICCSA 2008
Assessing GeoSimulation models
• GeoSimulation• Unfolding spatial patterns
(complex & non-linear)• Heterogeneous landscape
(boundary condition)• Good modelling practice
• Calibration• Validation• Model selection / development• Sensitivity analysis• Global behavioural tests
• Map comparison• Agreement between model and
realitysource: www.geosimulation.com
/ Urban Planning Group PAGE 3ICCSA 2008
Cellular Automata land use model
Time LoopTime Loop
&&
Stochastic perturbation
0 0.5 1
randvt ln1
Zoning
&
&
Accessibility
Transition-potential
=
Transition RuleTransition RuleChange cells to land-use for which they have the
highest transition potential until Regional
demands are met.
&
Suitability
Land use
& Interaction weights
Land use at time T+1
/ Urban Planning Group PAGE 4ICCSA 2008
Geographical scale
• Local → individual locations• Focal → moving window • Global → whole landscape
Local GlobalFocal
Agreement
1st map pair
2nd map pair
Geographical scale
Distinct
Identical
1st map pair
2nd map pair
Moving window / kernel
/ Urban Planning Group PAGE 5ICCSA 2008
Structure & Presence
• Similar in terms of presence
• Similar in terms of structure
/ Urban Planning Group PAGE 6ICCSA 2008
Comparison framework
• Global presence not of interest• exogenous model input
• Local structure not possible• Single cell has no internal spatial configuration
Cluster size distributionMoving Window Patch size-Structure
-Moving Window EuclideanKappaPresence
GlobalFocalLocal
/ Urban Planning Group PAGE 7ICCSA 2008
Local presence: Kappa
• Transition table the heart of local presence comparison• Kappa statistic• User / producer accuracy
25001042511501995Σ
75190848
23309618119
1651119261
202774154321767
Σ
Kappa = 0.38
/ Urban Planning Group PAGE 8ICCSA 2008
Focal presence: Euclidean distance
Mean Euclidean Distance
/ Urban Planning Group PAGE 9ICCSA 2008
Focal structure: Patch size
Map Patch size Moving window
Difference
Root Mean Squared Error
/ Urban Planning Group PAGE 10ICCSA 2008
Global structure: Cluster size distribution
• Kolmogorov-Smirnov distance expresses difference in distributions without any assumptions
/ Urban Planning Group PAGE 11ICCSA 2008
Reference level for the results
• Random Constraint Match• Minimize change given initial situation• Subject to total area constraints
Before
After+29
+18
-15
-32
Generated reference map
/ Urban Planning Group PAGE 12ICCSA 2008
Results: Local presence (Kappa)
0.900.910.910.92Nature
0.810.730.770.77Business
0.860.830.850.87Residential
0.910.880.890.91Agricultural
0.890.870.880.90Overall
ReferenceModelReferenceModel
Validation ’96-’00Calibration ’89-’96
+ -
/ Urban Planning Group PAGE 13ICCSA 2008
Results: Focal presence & structure
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 2 8 32 128 512Radius (500 m. cells)
Model
Reference
0
0.02
0.04
0.06
0.08
0.1
0 2 8 32 128 512Radius (500 m. cells)
0
5
10
15
20
25
30
35
0 2 8 32 128 512Radius (500 m. cells)
0
5
10
15
20
25
30
35
40
0 2 8 32 128 512Radius (500 m. cells)
Calibration period 1989-1996
Validation period1996-2000
PresenceEuclidean
StructurePatch size
+
+
-
- +
/ Urban Planning Group PAGE 14ICCSA 2008
Results: Global – Cluster size distribution
0.000
0.020
0.040
0.060
0.080
0.100
0.120
0.140
0.160
Residential Business Nature
Model
Reference
0.000
0.010
0.020
0.030
0.040
0.050
0.060
0.070
0.080
0.090
0.100
Residential Business Nature
0.000
0.050
0.100
0.150
0.200
0.250
Residential Business Nature
Calibration ’89 - ’96 Validation ’96 - ’00 Long term 2000 - 2030
• Calibration based on comparison 1996-1996• Validation based on comparison 2000 - 2000• Long term based on comparison 2000 – 2030
+ + +
/ Urban Planning Group PAGE 15ICCSA 2008
Results: Validation overview
• Strengths and weaknesses according to understanding of CA• Poor performance explained by short period and data quality• In the meanwhile, others found better results with same model
Cluster size distributionMoving Window Patch size-Structure
-Moving Window EuclideanKappaPresence
GlobalFocalLocal
++
-- -+
/ Urban Planning Group PAGE 16ICCSA 2008
Conclusion
• Assessing GeoSimulation model requires geospatial perspective• Spatial scale (Local – Focal – Global)• Spatial configuration (Structure – Presence)
• Interpretation of results requires reference level• Naïve predictor• Boundary conditions / constraints
• Otherwise?• Unbalanced judgement• Frustrating scientific progress
/ Urban Planning Group PAGE 17ICCSA 2008
Thanks for your attention
• Alex Hagen-Zanker - ahagen@riks.nl • Map Comparison Kit - www.riks.nl/mck
• Free software• User manual • Training materials• Publications
• Software developed for and shared by:
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