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Uncertainty visualisation in the Model Web. Lydia Gerharz, Christian Autermann , Holger Hopmann , Christoph Stasch Institute for Geoinformatics ( ifgi ), University of Münster lydia.gerharz@uni-muenster.de. Overview. Introduction Uncertainty visualisation methods

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uncertainty visualisation in the model web

Uncertainty visualisation in the Model Web

Lydia Gerharz, Christian Autermann, HolgerHopmann, ChristophStasch

Institute for Geoinformatics (ifgi), University of Münster

lydia.gerharz@uni-muenster.de

overview
Overview
  • Introduction
    • Uncertaintyvisualisationmethods
    • UncertWeb visualisationclient
  • Hands-on Exercises
    • Vectordatavisualisation
    • Raster datavisualisation
    • Howtoprepareyourowndata
  • Wrap-up& Discussion
uncertainty visualisation
Uncertaintyvisualisation

Communicateuncertainties in geospatialdatato

  • allowmeaningfulinterpretationofmodelresultsormeasurementsfordecisionmaking
  • explorespatialand temporal distributionofuncertainties
uncertainty visualisation methods
Uncertaintyvisualisationmethods

Techniques

  • Adjacentmaps
  • Bi-variatemaps
  • Sequentialmaps

Modes

  • Static
  • Dynamic
  • Interactive

e.g. Animation ofrealisations

methods i focus metaphors
Methods (i) – Focus metaphors

Contourcrispness Fog

Fillclarity Resolution

MacEachren (1992)

methods ii adjacent maps
Methods (ii) – Adjacentmaps

Value anduncertaintymapsareshownnexttoeachother

Rodriguez et al. (2006)

Avoids visual overload, but hard to connect two maps mentally

methods iii probability of exceedance
Methods (iii) – Probabilityofexceedance

Descriptivestatistics: UseIPCC (2001) terminologytodescribeprobabilityofexceedance

van de Kassteele & Velders. (2006)

methods iv stochastical dimension in a gis
Methods (iv) – Stochasticaldimension in a GIS

Aguila software

  • Cumulativeprobabilitydistributionforeachpixelorobject

Browse eitherthroughprobabilityorvalues (thresholds)

  • Cumulative/exceedanceprobability
  • Confidenceintervals

Time seriesvisualisation

Scenario view

Pebesma et al. (2007)

other methods
Other methods

Hierarchicalspatialdatamodel

Whitening

Hengl (2003)

Kardos et al. (2003)

Confidence

intervals

uncertainty visualisation in the model web1
Uncertaintyvisualisation in the Model Web

Output

Output

Input

Input

Data service

e.g. meteorologicalmeasurements

Model service

e.g. meteorologicalforecastmodel

Model service

e.g. airqualitymodel

Final

result

Web-baseduncertaintyvisualisationclient

aim within uncertweb
Aimwithin UncertWeb

Develop a tool that

  • enablescommunicationofuncertainties in spatio-temporal datato different usergroups
  • allows easy integration into model workflows following the Model Web paradigm
  • visualises inputs, outputs and intermediate steps
  • supports different uncertaintyandgeospatialencodings
uncertweb visualisation tool
UncertWeb visualisationtool
  • Interactive, web-basedthinclient
  • Supports different encodings
    • Uncertainties: UncertML 2.0
    • Raster data: NetCDF, GeoTIFF
    • Vectordata: Observations&Measurements (O&M)
  • Open Source, based on JavaScript libraries
    • OpenLayers (spatial, temporal, spatio-temporal data)
    • jStat (non-temporal, non-spatialuncertainties)
    • ExtJS (interactive web applicationcontrols)

https://svn.52north.org/svn/geostatistics/main/uncertweb/

implementation details
Implementation details
  • Vectordata
    • Encodedas O&M and UncertML in XML/JSON format
    • Directlyreadbytheclient
  • Raster data
    • NetCDFandGeoTiffcannotbedirectlyreadbytheclient
    • RESTfulVisualisation Service (VISS)
      • Create visualisations (raster) fromcomplexsources
    • Web Mapping Service (WMS)
      • Stores createdrasters
      • Providestile-caching
      • Manyclientsavailable
u o m encoding
U-O&M encoding
  • Uncertainty Observation type toencode UncertML types (distribution, samples, statistics)
netcdf u encoding
NetCDF-U encoding
  • Encodeuncertaintyasdimensionorancillary_variable
  • refattributeto UncertML definition
architecture overview
Architecture overview

Web

client

SOS

Raster

map

U-O&M as XML or JSON

WMS

reference

VECTOR DATA

WMS

Stores createdraster

VISS

Createsvisualisation

Add layer

NetCDF-U

WCS

Stores sourcedata

RASTER DATA

visualisation methods
Visualisation methods

Support for:

  • Non-spatial & spatialdata
  • Temporal & Spatio-temporal data
  • Continuous & categoricaldata
  • Multivariate data
  • Different userbackgroundsandexperiences
    • Different usabilityofvisualisationmethods
      • Adjacentmapsfornoviceusers
      • Multidimensional mapsforexperts
visualisation methods basic plots
Visualisation methods – Basic plots

Continuous data

Categorical

data

visualisation methods adjacent maps
Visualisation methods – Adjacent maps

Continuous data

Categorical data

using the tool
Usingthetool

Menu toolbar

Mapnavigation

Mapwindow

Legend

adding new resources
Addingnewresources

1) By Add Resourcebutton

2) By URL Parameter

2a) http://giv-uw.uni-muenster.de/vis/v2/?url=http://giv-uw.uni- muenster.de/data/netcdf/biotemp.nc&mime=application/netcdf

2b) http://giv-uw.uni-muenster.de/vis/v2/?netcdf=http://giv-uw.uni- muenster.de/data/netcdf/biotemp.nc

exercises

Exercises

http://giv-wikis.uni-muenster.de/agp/bin/view/Main/UncertaintyVisualisationWorkshop

wrap up questionnaire

Wrap-up & Questionnaire

http://surveys.ifgi.de/

 UncertWeb Questionnaire Part B.1: Visualization Tool

Further comments/questions?!