error propagation zandbergen · why modelbuilder? • easy to follow visual programming language...
TRANSCRIPT
Error Propagation Modeling for TerrainError Propagation Modeling for Terrain Analysis using Dynamic Simulation Tools in
ArcGIS ModelbuilderArcGIS Modelbuilder
Paul ZandbergenPaul ZandbergenDepartment of GeographyUniversity of New Mexico
Today’s topic:
Error Propagation Modeling
OutlineOutline
• Teaching error propagation modelingTeaching error propagation modeling• Modelbuilder for dynamic simulationC d d h d d li i• Case‐study: stream and watershed delineation
• Exercise details and results
MotivationMotivation• How to teach error propagation modeling to students
and early career GIS professionals? – Strong general GIS skills– Basic knowledge of data quality– Limited experience with (geo)statistics
• Concepts:• Concepts:– Uncertainty (inputs, outputs)– Spatial autocorrelation of error
Monte Carlo simulation– Monte Carlo simulation
• Applied to stream and watershed delineationpp
Class ExerciseClass Exercise
• Sample datasetSample dataset• Step‐by‐step instructions• Tbx file with modelsTbx file with models
Original DEM Error DEMError term
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StreamsStream Probability
Modelbuilder StructureModelbuilder Structure
Stream DelineationDEM Error ModelIteration Results
Inputs: Outputs:•Digital Elevation Model•Study area boundary•Root Mean Square ErrorSt th h ld
•Stream probability•Watershed probability
•Stream threshold•No. of iterations
Why Modelbuilder?Why Modelbuilder?
• Easy to follow visual programming languageEasy to follow visual programming language• Typically taught at intro GIS level• Models integrated into ArcGIS processingModels integrated into ArcGIS processing• Advanced functionality: if/then, loops, iteration
DEM Error ModelDEM Error Model
Random Gaussian Error Spatially Autocorrelated Error
Focal Statistics
Results from IterationResults from IterationIteration 1 CombinedIteration 2
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Final ResultsFinal Results100 iterations
Effect of RMSEEffect of RMSERMSE 0.1 ft RMSE 0.5 ft RMSE 1 ft
Comparison of Stream NetworksComparison of Stream Networks
Comparison of Stream NetworksComparison of Stream Networks
Watershed DelineationWatershed Delineation
1 iteration 100 iterationsDEM
Effect of RMSEEffect of RMSERMSE 0.1 ft RMSE 0.5 ft RMSE 1 ft
Class ExerciseClass Exercise
• Basic model provided:Basic model provided:– Model already designed as a tool– Explore effect of RMSE, no. of iterations
Assumptions / LimitationsAssumptions / Limitations
• Error model is relatively simpleError model is relatively simple– Spatially autocorrelated, yes– Tested (geo)statistically, no
• Error assumed to be unrelated to terrain complexity• Error assumed to be normally distributedy• Number of iterations is user‐defined
Class ExerciseClass Exercise• Students modify/enhance model:
– Improve error model• Scale of autocorrelation• Related to land cover, slope and/or roughness• Add non‐normal behavior
– Adapt model to different terrain derivatives• Visibility• Wetness index• Runoff model
– Set convergence parameter• Students apply simulation to other type of analysis
– Point‐in‐polygon overlayProximity analysis– Proximity analysis
Alternative Error ModelsAlternative Error ModelsGaussian Original Error
Slope Land coverGaussian
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Lessons from Teaching Error PropagationLessons from Teaching Error Propagation
• Pros:– Model tools help to get results quickly– Student “get” the error propagation concept
Leads to questions– Leads to questions• about the methodology, assumptions about the nature of error, applicability to other domains
• Cons:• Cons:– Model runs can take a while to complete– Complications
• Somewhat complicated to replicate approach to other application domains, students get stuck on issues with Modelbuilder
– Student still struggle with spatial autocorrelation
Longer Term ObjectiveLonger Term Objective
• Simulation approach has broader applicabilitypp y
• Toolbox with many tools• Make error propagationMake error propagation modeling easier…
