Geostatistical Analyst - An Introduction

Transcription

1 Esri International User Conference San Diego, CA Technical Workshops July 2012 Geostatistical Analyst - An Introduction Steve Lynch and Eric Krause Redlands, CA

2 Presentations of interest EBK Robust kriging as a GP tool - Tuesday 3:00pm 3:30pm Demo theater Geostatistical Simulations - Tuesday 3:30pm 4:00pm Demo theater Areal Interpolation Performing polygon-to-polygon predictions - Thursday 9:00am 9:30am Demo theater Surface Interpolation in ArcGIS - Wednesday 1:00pm 2:00pm Demo theater Designing and Updating a Monitoring Network - Thursday 9:30am 10:00am Demo theater Concepts and Applications of Kriging - Tuesday 10:15am 11:30am 15B Creating Surfaces - Wednesday 8:30am 9:45am 15A

3 Outline What is geostatistics? Geostatistical Analyst? New in 10 and 10.1 Interpolation workflow Demonstrations Supplementary information Questions / Answers

4 What is geostatistics? The statistics of spatially correlated data Semivariogram Sill Nugget Range

5 Semivariogram Semivariogram(distance h) = 0.5 * average [ (value i value j ) 2 ]

6 What is geostatistics? The statistics of spatially correlated data

7 Geostatistical Analyst - Overview Interactive - Exploratory Spatial Data Analysis tools - Variography - Kriging - Other interpolation methods - Cross validation Geoprocessing toolbox - Interpolation - Sampling Network Design - Simulation - Utilities - Conversion

8 Where is Geostatistical Analyst used?

9 Where is Geostatistical Analyst used?

10 Experiment conducted by the US EPA 20 years ago Isaaks & Srivastava, An Introduction to Applied Geostatistics. 12 independent reputable geostatisticians Given the same data Asked to perform the same straightforward estimation Results were widely different Different - data analysis conclusions - variogram models and choice of kriging type - searching neighborhoods.

11 Geostatistical Analyst Toolbar and Toolbox

12 Geostatistical Wizard Demonstration

13 What s new in 10 Optimize buttons Local Polynomial Interpolation Kriging - Nugget, partial sill and other(s), are optimized using cross validation with focus on the estimation of the range parameter.

14 What s new in 10.1 Areal Interpolation - predictions can be made from one set of polygons to another set of polygons Empirical Bayesian Kriging (EBK) - builds local models on subsets of the data, which are then combined together to create the final surface. Normal Score Transformation - Multiplicative Skewing approximation method

15 Interpolation workflow Exploratory Spatial Data Analysis (ESDA) Interpolate - estimation of values at unsampled locations based on known values Goodness of fit

16 Exploratory Spatial Data Analysis Where is the data located? What are the values at the data points? How does the location of a point relate to its value?

17 Exploratory Spatial Data Analysis (ESDA)

18 Exploratory Spatial Data Analysis (ESDA)

19 Kriging Concepts and Applications of Kriging - Tuesday 10:15am 11:30am 15B Outline Introduction to kriging Best practices Fitting a proper model Variography, transformations, isotropy, stationarity Comparing models using cross validation Interpreting results Empirical Bayesian Kriging Robust kriging as a GP tool - Tuesday 3:00 3:30pm Demo theater

20 What is kriging? It is a geostatistical interpolation technique that models the spatial correlation of point measurements to estimate values at unmeasured locations. Associates uncertainty with the predictions Correlation Distance

21 Kriging Demonstration

22 Empirical Bayesian Kriging (EBK) automates the most difficult aspects of building a valid kriging model estimates the semivariogram through repeated simulations can handle non-stationary input data. Unlike other kriging methods (use weighted least squares), the semivariogram parameters in EBK are estimated using restricted maximum likelihood (REML). New in ArcGIS 10.1

23 For a given distance h, the semivariogram model: γ(h)= Nugget + b h α

24 Empirical Bayesian Kriging (cont) Advantages - Requires minimal interactive modeling - Allows accurate predictions of non-stationary data - More accurate than other kriging methods for small datasets - Geoprocessing tool Disadvantages - Processing is slower than other kriging methods. - Cokriging and anisotropy are unavailable.

25 Empirical Bayesian Kriging Demonstration

26 Goodness of fit / Model acceptance Subset Features Cross Validation

27 Cressie, 1990 Cross validation does not prove that the model is correct, merely that it is not grossly incorrect.

28 Cross validation Demonstration

29 Geostatistical layer Method and parameters Pointer to the data Dynamic

30 Output = Prediction, Prediction SE, Probability, Quantile, Condition number

31 Geostatistical layer Demonstration

32 Areal Interpolation reaggregation of data from one set of polygons to another set of polygons New in ArcGIS 10.1

33 Areal Interpolation Demonstration

34 Demo theater Areal Interpolation Thursday 9:00am 9:30am

35 Simple kriging N =

36 Demo theater Geostatistical Simulations Tuesday 3:30pm 4:00pm

37 Interpolation with Barriers Kernel interpolation Diffusion interpolation? Cost Raster

38 Create Spatially Balanced Points Monitor road pollution Convert roads to raster High value = busy road Low value = quite road Sampling Network Design

39 Create Spatially Balanced Points (cont.) Sampling Network Design

40 Densify Sampling Network Used kriging to create: - Prediction surface - Standard error of prediction Want to add 2 new sites Sampling Network Design

41 Densify Sampling Network (Cont.) Sampling Network Design

42 Designing and Updating a Monitoring Network Thursday 9:30am 10:00am Demo theater

43 resources.arcgis.com

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47 Thank you for attending Have fun at UC2012 Open for Questions Please fill out the evaluation: First Offering ID: 637 Second Offering ID: 812