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URBDP 422 URBAN AND REGIONAL GEO-SPATIAL ANALYSIS Lecture 11: Modeling Spatial Phenomena:

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.

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URBDP 422 URBAN AND REGIONAL GEO-SPATIAL ANALYSIS Lecture 11: Modeling Spatial Phenomena:

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  1. 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

  2. Lecture’s Objectives • Land Cover Change Model • Uncertainty • Unresolved issues in modeling urban landscapes

  3. 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?

  4. Example: Percent Land Cover Sum of the area of all patches of the corresponding patch type divided by total landscape area. 100% Urban 16% Urban 3% Urban

  5. % TIA, % Trees, and B-IBI

  6. Example: Aggregation Index AI equals the number of like adjacencies divided by the maximum possible number of like adjacencies involving a specified class High Medium Low

  7. Aggregation Index and B-IBI

  8. 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?

  9. Assess future land cover • Land cover change model… • LCCM is constructed using observed biophysical, land cover, and development data

  10. LCCM Background • LCCM is a multinomial Logit model based on a set of discrete choice equations of site-based land cover transitions MU HU Mixed Forest HU Conifer Forest HU MU MU LU HU LU LU MU Mixed Forest

  11. LCCM Background • LCCM calculates transitions probabilities for each pixel in the landscape • Name some factors that might be associated with each transition. MU HU Mixed Forest HU Conifer Forest HU MU MU LU HU LU LU MU Mixed Forest

  12. Land Cover Sample Variables 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

  13. UrbanSim

  14. UrbanSim Modules

  15. ModelImplementation • 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.

  16. Land Cover Transitions Modeled Predicted 1999 Observed 1999 Observed 1991 Observed 1995 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. ij is a vector of estimated logit coefficients. k is the number of land cover states.

  17. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC)

  18. Land Cover Change Model Aggregation Index of Conifer - sample of light urban to medium urban - Sample of conifer to light urban 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC) Stratified 10% random Sample of 30 m pixels Sample spatial Data layers, export to database

  19. Land Cover Change Model Aggregation Index of Light Urban - sample of light urban to medium urban - Sample of conifer to light urban Aggregation Index of Conifer - sample of light urban to medium urban - Sample of conifer to light urban

  20. Land Cover Change Model 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC) Stratified 10% random Sample of 30 m pixels Sample spatial Data layers, export to database Multinomial Logit used to estimate equations for Land cover transition

  21. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC) Stratified 10% random Sample of 30 m pixels Sample spatial Data layers, export to database Spatial data sets as potential explanatory variables of LCC Pixel-level probabilities of land cover transition from unique combination of input variables for each equation Map Transition probabilities Multinomial Logit used to estimate equations for Land cover transition One set of equations for each land cover class including No Change3 1991 Land cover class

  22. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC) Monte Carlo simulation to randomly Determine which transition (including No Change) occurs and where Stratified 10% random Sample of 30 m pixels Sample spatial Data layers, export to database Spatial data sets as potential explanatory variables of LCC Pixel-level probabilities of land cover transition from unique combination of input variables for each equation Map Transition probabilities Multinomial Logit used to estimate equations for Land cover transition One set of equations for each land cover class including No Change3 1991 Land cover class

  23. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1999 (t2) Land cover class (observed) 1995 (t1) Land cover class 1991 (t0) Land cover class Set of all possible Land cover transitions Land cover Change (LCC) Monte Carlo simulation to randomly Determine which transition (including No Change) occurs and where Stratified 10% random Sample of 30 m pixels Compare predicted with observed Sample spatial Data layers, export to database Spatial data sets as potential explanatory variables of LCC Pixel-level probabilities of land cover transition from unique combination of input variables for each equation Map Transition probabilities Multinomial Logit used to estimate equations for Land cover transition One set of equations for each land cover class including No Change3 1991 Land cover class

  24. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1999 (t2) Land cover class (observed) 1995 (t1) Land cover class 1991 (t0) Land cover class Spatially-constrain output Using observed rules of urban development patterns Set of all possible Land cover transitions Land cover Change (LCC) Monte Carlo simulation to randomly Determine which transition (including No Change) occurs and where Stratified 10% random Sample of 30 m pixels Compare predicted with observed Sample spatial Data layers, export to database Spatial data sets as potential explanatory variables of LCC Pixel-level probabilities of land cover transition from unique combination of input variables for each equation Map Transition probabilities Multinomial Logit used to estimate equations for Land cover transition One set of equations for each land cover class including No Change3 1991 Land cover class

  25. Land Cover Change Model 1999 (t2) Land cover class (predicted) 1999 (t2) Land cover class (observed) 1995 (t1) Land cover class 1991 (t0) Land cover class Spatially-constrain output Using observed rules of urban development patterns Compare predicted with observed Set of all possible Land cover transitions Land cover Change (LCC) Monte Carlo simulation to randomly Determine which transition (including No Change) occurs and where Stratified 10% random Sample of 30 m pixels Compare predicted with observed Sample spatial Data layers, export to database Spatial data sets as potential explanatory variables of LCC Pixel-level probabilities of land cover transition from unique combination of input variables for each equation Map Transition probabilities Multinomial Logit used to estimate equations for Land cover transition One set of equations for each land cover class including No Change3 1991 Land cover class

  26. Observed and Predicted Land Cover Change for Central Puget Sound 1986-2027

  27. Sources of Agreement and Errors in Predicted Landscape *Derived using methods from Pontius et al. 2004 Ecological Modeling 179

  28. Integrated Geo-Spatial Models Alberti and Waddell 2001.

  29. 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

  30. Partitioning the Model

  31. Spatial Segmentation

  32. Spatial Dependence • Spatial structure may occur due to: • 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)

  33. Modeling Spatial Pattern

  34. 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

  35. Types of Uncertainty in LCCM • Input data • Measurement errors • Systematic errors • Spatial resolution and sampling design • Model structure • Stochasticity • Random number generators

  36. Types of Uncertainty in LCCM

  37. Proposed Uncertainty Approach for LCCM Multinomial logit model Step 1: • Combination of Bayesian melding (Raftery et al. 1995) and Bayesian model averaging (Hoeting et al. 1999) Model Estimation Step 2: Bayesian Melding Model Prediction Step 3: Output Bayesian Model Averaging

  38. Implications for Future Models • Problem definition • Multiple actors • Time • Space • Feedback • Uncertainty

  39. 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

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