urbdp 422 urban and regional geo-spatial analysis lecture 11: modeling spatial phenomena:
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URBDP 422 URBAN AND REGIONAL GEO-SPATIAL ANALYSIS Lecture 11: Modeling Spatial Phenomena: the Land Cover Change Model Lab: Exc. 11, Network Analysis FEB 11, 2014. Lecture’s Objectives. Land Cover Change Model Uncertainty Unresolved issues in modeling urban landscapes. Questions. - PowerPoint PPT PresentationTRANSCRIPT
URBDP 422 URBAN AND REGIONAL GEO-SPATIAL ANALYSIS
Lecture 11: Modeling Spatial Phenomena: the Land Cover Change Model
Lab: Exc. 11, Network Analysis
FEB 11, 2014
Lecture’s Objectives
• Land Cover Change Model
• Uncertainty
• Unresolved issues in modeling urban landscapes
Questions• How does urban development affect
– Hydrology? (Flooding, water supply)– Stream quality?– Salmon populations?– Bird diversity?
• What do we need to define in order to answer these questions?
Example: Percent Land Cover
100% Urban 16% Urban 3% UrbanPercent Land 1 Percent Land 2 Percent Land 3
100 % Urban
0% Mixed urban
0 % Forest
16% Urban
73% Mixed Urban
11% Forest
3% Urban
86% Mixed Urban
11% Forest
A
a
pLand
n
j
ij 1
Sum of the area of all patches of the corresponding patch type divided by total landscape area.
% TIA, % Trees, and B-IBI
B-IBI and %Trees
R 2 = 0.5978
0
10
20
30
40
50
60
0% 20% 40% 60% 80% 100%
%Trees
B-I
BI
B-IBI and %TIA
R 2 = 0.6151
0
10
20
30
40
50
60
0% 20% 40% 60% 80% 100%
%TIA
B-I
BI
Example: Aggregation Index
AI equals the number of like adjacencies divided by the maximum possible number of like adjacencies involving a specified class
High Impervious Aggregation Medium Impervious Aggregation Low Impervious Aggregation
92% Impervious Aggregation Index
72% Impervious Aggregation Index
28.5% Impervious Aggregation Index
High Medium Low
Aggregation Index and B-IBI
R 2 = 0.66
10
15
20
25
30
35
40
45
50
40 50 60 70 80 90 100
AI (Forest)
B-I
BI
R 2 = 0.60
10
15
20
25
30
35
40
45
50
40 50 60 70 80 90 100
AI (Urban)
B-I
BI
Questions• What might the consequences of development
look like in 2038?• What if
– we expand the highway system? – Or expand the light rail? – Or change development regulations?– Or… you ask me…
• What do we need to calculate to answer these questions?
Assess future land cover
• Land cover change model…
• LCCM is constructed using observed biophysical, land cover, and development data
LCCM Background
• LCCM is a multinomial Logit model based on a set of discrete choice equations of site-based land cover transitions
MU HU
LU HU
MU
Mixed Forest
HU
MU
LU
Conifer Forest
HU
MU
LU
Mixed Forest
LCCM Background
• LCCM calculates transitions probabilities for each pixel in the landscape
• Name some factors that might be associated with each transition.
MU HU
LU HU
MU
Mixed Forest
HU
MU
LU
Conifer Forest
HU
MU
LU
Mixed Forest
Land Cover Sample Variables
Landscape Composition Metrics
Percent Urban
Percent Light Urban
Percent Forest
Percent Steep Slope
Distance Metrics
Distance to Critical Areas
Distance to Primary Roads
Distance to Local Roads
Landscape Configuration Metrics
All Urban Aggregation Index
Forest Aggregation Index
Development Intensity Variables
Development Event Geometric Mean Parcel Size
Recent Development Variables
Commercial Area Added Time T-3
Residential Units Added Time T-3
Indicator Variables
Below Minimum Parcel Size
Is Inside Urban Growth Boundary
UrbanSim
UrbanSim Modules
Model Implementation
• Developed within the Open Platform for Urban Simulation (OPUS) and UrbanSim modeling platforms (Waddell 2002, Waddell et al. 2003
• Land cover change predictions were made for four counties (King, Kitsap, Piece, and Snohomish) every 4 years from 2003 to 2027.
• Model estimations were created using observed data from 1991, 1995, 1999 , and 2002
• Land cover change predictions will be generated for the Central Puget Sound region every year from 2000 to 2040.
Land Cover Transitions Modeled
k
j)β(i ts,Χ
j)β(i ts,Χj)(i
ts,Pe
e
Pst is the probability of land cover change at site s at time t.
X is the y x m matrix of the y independent variables and a unitary constant associate with each m site with initial class i.
ij is a vector of estimated logit coefficients.
k is the number of land cover states.
Observed 1991 Observed 1995 Predicted 1999 Observed 1999
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Set of all possibleLand cover transitions
1999 (t2) Land cover class (predicted)
Aggregation Index of Conifer - sample of light urban to medium urban
- Sample of conifer to light urban
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Set of all possibleLand cover transitions
Aggregation Index of Conifer - sample of light urban to medium urban
- Sample of conifer to light urban
Land Cover Change Model
Aggregation Index of Light Urban - sample of light urban to medium urban
- Sample of conifer to light urban
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
One set of equations for each land cover class including No Change3
Spatial data sets as potential explanatory
variables of LCCPixel-level probabilitiesof land cover transition from unique combination of input variables for each equation
Map Transitionprobabilities
1991 Land cover class
1999 (t2) Land cover class (predicted)
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
One set of equations for each land cover class including No Change3
Spatial data sets as potential explanatory
variables of LCCPixel-level probabilitiesof land cover transition from unique combination of input variables for each equation
Monte Carlo simulation to randomlyDetermine which transition (includingNo Change) occurs and where
Map Transitionprobabilities
1991 Land cover class
1999 (t2) Land cover class (predicted)
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
One set of equations for each land cover class including No Change3
Spatial data sets as potential explanatory
variables of LCCPixel-level probabilitiesof land cover transition from unique combination of input variables for each equation
Monte Carlo simulation to randomlyDetermine which transition (includingNo Change) occurs and where
Compare predicted with observed
Map Transitionprobabilities
1991 Land cover class
1999 (t2) Land cover class (predicted)
1999 (t2) Land cover class (observed)
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
One set of equations for each land cover class including No Change3
Spatial data sets as potential explanatory
variables of LCCPixel-level probabilitiesof land cover transition from unique combination of input variables for each equation
Monte Carlo simulation to randomlyDetermine which transition (includingNo Change) occurs and where
Spatially-constrain output Using observed rules of urban development patterns
Compare predicted with observed
Map Transitionprobabilities
1991 Land cover class
1999 (t2) Land cover class (predicted)
1999 (t2) Land cover class (observed)
Land Cover Change Model
1991 (t0) Land cover class
1995 (t1) Land cover class
Land cover Change (LCC)
Stratified 10% random Sample of 30 m pixels
Sample spatialData layers, export to database
Multinomial Logit used to estimate equations for Land cover transition
Set of all possibleLand cover transitions
One set of equations for each land cover class including No Change3
Spatial data sets as potential explanatory
variables of LCCPixel-level probabilitiesof land cover transition from unique combination of input variables for each equation
Monte Carlo simulation to randomlyDetermine which transition (includingNo Change) occurs and where
Spatially-constrain output Using observed rules of urban development patterns
Compare predicted with observed
Compare predicted with observed
Map Transitionprobabilities
1991 Land cover class
1999 (t2) Land cover class (predicted)
1999 (t2) Land cover class (observed)
Observed and Predicted Land Cover Change for Central Puget Sound 1986-2027
0%
20%
40%
60%
80%
100%
1986 1991 1995 1999 2002 2003 2007 2011 2015 2019 2023 2027
Obs Obs Obs Obs Obs Pred Pred Pred Pred Pred Pred Pred
Regerating Forest
Clearcut Forest
Coniferous Forest
Mixed Forest
Agriculture
Grass
Light Urban
Medium Urban
Heavy Urban
1995-1999spec fileall 4 counties
Sources of Agreement and Errors in Predicted Landscape
*Derived using methods from Pontius et al. 2004 Ecological Modeling 179
0
10
20
30
40
50
60
70
80
90
100
1 2 4 8 10
Resolution as multiple of pixel side
Perc
en
t o
f L
an
dscap
e disagreement due toquantity LCCM
disagreement due tolocation LCCM
agreement due tolocation LCCM
agreement due toquantity LCCM
agreement due tochance LCCM
Integrated Geo-Spatial Models
Alberti and Waddell 2001.
Model and Null Percent Correct at Multiple Scales*:Predictions of 1999 Landscape from 1991-95
*Derived using methods from Pontius et al. 2004 Ecological Modeling 179
50
55
60
65
7075
80
85
90
95
1001 2 4 8 10
Resolution as multiple of pixel side
Perc
en
t o
f L
an
dscap
e
LCCM Asymptote
NULL Asymptote
NULL % Correct
LCCM % Correct
Partitioning the Model
Spatial Segmentation
Spatial Dependence
Spatial structure may occur due to:
a) positive spatial autocorrelation among the locations and/or
b) spatial dependence due to human and ecological responses to underlying environmental conditions
(Wagner and Fortin 2005)
Modeling Spatial Pattern
k
j)β(i ts,Χ
j)β(i ts,Χj)(i
ts,Pe
e
k
)sδty jβ(i ts,Χ
)ty jβ(i ts,Χ)ty j(i
ts,Pe
e ss
Uncertainty• We need to identify and quantify uncertainty in land
cover change models– Uncertainty can lead to propagation of error when
connecting to other models
• Problem of equifinality (Beven 1990)– multiple parameter sets can produce “optimal model” and
“reasonable” results
• Research objective: To identify and quantify sources of uncertainty in input data and model structure, and produce ensemble model predictions of land cover change
Types of Uncertainty in LCCM
• Input data– Measurement errors
• Systematic errors– Spatial resolution and sampling design
• Model structure
• Stochasticity– Random number generators
Types of Uncertainty in LCCM
Proposed Uncertainty Approach for LCCM
• Combination of Bayesian melding (Raftery et al. 1995) and Bayesian model averaging (Hoeting et al. 1999)
Multinomial logit model
Model Estimation
Model Prediction
Bayesian Model Averaging
Bayesian Melding
Output
Step 1:
Step 2:
Step 3:
Implications for Future Models
- Problem definition
- Multiple actors
- Time
- Space
- Feedback
- Uncertainty
Modeling the Urban Landscape
- Multiple actors Models need to explicitly represent the diversity of actors to be realistic
- Time Dynamic models, representing time explicitly are more realistic
- Space Spatial resolution depends on the appropriate resolution at which phenomena occur
- Feedback It is important to consider key feedback mechanisms among variables driving the model dynamic
- Uncertainty Models should treat uncertainty explicitly to assess model results