Hierarchical Modeling for non-gaussian Spatial Data

Transcription

1 Hierarchical Modeling for non-gaussian Spatial Data Sudipto Banerjee 1 and Andrew O. Finley 2 1 Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, U.S.A. 2 Department of Forestry & Department of Geography, Michigan State University, Lansing Michigan, U.S.A. July 25,

2 Spatial Generalized Linear Models Often data sets preclude Gaussian modeling: y(s) may not even be continuous Example: y(s) is a binary or count variable species presence or absence at location s species abundance from count at location s continuous forest variable is high or low at location s Replace Gaussian likelihood by exponential family member Spatial GLM, Diggle Tawn and Moyeed (1998) 2 Graduate Workshop on Environmental Data Analytics 2014

3 Spatial Generalized Linear Models First stage: y(s i ) are conditionally independent given β and w(s i ), so f(y(s i ) β, w(s i ), γ) equals h(y(s i ), γ) exp (γ[y(s i )η(s i ) ψ(η(s i ))]) where g(e(y(s i ))) = η(s i ) = x (s i )β + w(s i ) (canonical link function) and γ is a dispersion parameter. Second stage: Model w(s) as a Gaussian process: w N(0, σ 2 R(φ)) Third stage: Priors and hyperpriors. No process for y(s), only a valid joint distribution Not sensible to add a pure error term ɛ(s) 3 Graduate Workshop on Environmental Data Analytics 2014

4 Spatial Generalized Linear Models Comments We are modeling with spatial random effects Introducing these in the transformed mean encourages means of spatial variables at proximate locations to be close to each other Marginal spatial dependence is induced between, say, y(s) and y(s ), but observed y(s) and y(s ) need not be close to each other Second stage spatial modeling is attractive for spatial explanation in the mean First stage spatial modeling more appropriate to encourage proximate observations to be similar. 4 Graduate Workshop on Environmental Data Analytics 2014

5 Illustration from: Finley, A.O., S. Banerjee, and R.E. McRoberts. (2008) A Bayesian approach to quantifying uncertainty in multi-source forest area estimates. Environmental and Ecological Statistics, 15: Graduate Workshop on Environmental Data Analytics 2014

6 Binary spatial regression: forest/non-forest We illustrate a non-gaussian model for point-referenced spatial data: Objective is to make pixel-level prediction of forest/non-forest across the domain. 6 Graduate Workshop on Environmental Data Analytics 2014

7 Binary spatial regression: forest/non-forest We illustrate a non-gaussian model for point-referenced spatial data: Objective is to make pixel-level prediction of forest/non-forest across the domain. Data: Observations are from 500 georeferenced USDA Forest Service Forest Inventory and Analysis (FIA) inventory plots within a 32 km radius circle in MN, USA. The response y(s) is a binary variable, with { 1 if inventory plot is forested y(s) = 0 if inventory plot is not forested 6 Graduate Workshop on Environmental Data Analytics 2014

8 Binary spatial regression: forest/non-forest We illustrate a non-gaussian model for point-referenced spatial data: Objective is to make pixel-level prediction of forest/non-forest across the domain. Data: Observations are from 500 georeferenced USDA Forest Service Forest Inventory and Analysis (FIA) inventory plots within a 32 km radius circle in MN, USA. The response y(s) is a binary variable, with { 1 if inventory plot is forested y(s) = 0 if inventory plot is not forested Observed covariates include the coinsiding pixel values for 3 dates of m resolution Landsat imagery. 6 Graduate Workshop on Environmental Data Analytics 2014

9 Binary spatial regression: forest/non-forest We fit a generalized linear model where y(s i ) Bernoulli(p(s i )), logit(p(s i )) = x(s i ) β + w(s i ). 7 Graduate Workshop on Environmental Data Analytics 2014

10 Binary spatial regression: forest/non-forest We fit a generalized linear model where y(s i ) Bernoulli(p(s i )), logit(p(s i )) = x(s i ) β + w(s i ). Assume vague flat for β, a Uniform(3/32, 3/0.5) prior for φ, and an inverse-gamma(2, ) prior for σ 2. 7 Graduate Workshop on Environmental Data Analytics 2014

11 Binary spatial regression: forest/non-forest We fit a generalized linear model where y(s i ) Bernoulli(p(s i )), logit(p(s i )) = x(s i ) β + w(s i ). Assume vague flat for β, a Uniform(3/32, 3/0.5) prior for φ, and an inverse-gamma(2, ) prior for σ 2. Parameters updated with Metropolis algorithm using target log density: ln (p(ω y)) ( σ a n ) ln ( σ 2) σ b 2 σ 1 1 ln ( R(φ) ) 2 2 2σ 2 w R(φ) 1 w n + y(s ( i) x(s i) β + w(s ) n i) ln ( 1 + exp(x(s i) β + w(s ) i) i=1 i=1 + ln(σ 2 ) + ln(φ φ a) + ln(φ b φ). 7 Graduate Workshop on Environmental Data Analytics 2014

12 Posterior parameter estimates Parameter estimates (posterior medians and upper and lower 2.5 percentiles): Parameters Estimates: 50% (2.5%, 97.5%) Intercept (θ 0 ) (49.56, ) AprilTC1 (θ 1 ) (-0.45, -0.11) AprilTC2 (θ 2 ) 0.17 (0.07, 0.29) AprilTC3 (θ 3 ) (-0.43, -0.08) JulyTC1 (θ 4 ) (-0.25, 0.17) JulyTC2 (θ 5 ) 0.09 (-0.01, 0.19) JulyTC3 (θ 6 ) 0.01 (-0.15, 0.16) OctTC1 (θ 7 ) (-0.68, -0.22) OctTC2 (θ 8 ) (-0.19, 0.14) OctTC3 (θ 9 ) (-0.46, -0.07) σ (0.39, 2.42) φ ( , ) log(0.05)/φ (meters) (932.33, ) Covariates and proximity to observed FIA plot will contribute to increase precision of prediction. 8 Graduate Workshop on Environmental Data Analytics 2014

13 Median of posterior predictive distributions 9 Graduate Workshop on Environmental Data Analytics 2014

14 97.5%-2.5% range of posterior predictive distributions 10 Graduate Workshop on Environmental Data Analytics 2014

15 CDF of holdout area s posterior predictive distributions F[P(Y(A) = 1)] cut point P(Y(A) = 1) Classification of pixel areas (based on visual inspection of imagery) into non-forest ( ), moderately forest ( ), and forest (no marker). 11 Graduate Workshop on Environmental Data Analytics 2014