Uncertainty visualisation in the Model Web

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

• Introduction
Overview
– Uncertainty visualisation methods
– UncertWeb visualisation client
• Hands-on Exercises
– Vector data visualisation
– Raster data visualisation
– How to prepare your own data
• Wrap-up & Discussion

Introduction to uncertainty
visualisation

Uncertainty visualisation
Communicate uncertainties in geospatial data to
– allow meaningful interpretation of model results or
measurements for decision making
– explore spatial and temporal distribution of uncertainties

Techniques
Uncertainty visualisation
– Adjacent maps
– Bi-variate maps
– Sequential maps
Modes
– Static
– Dynamic
– Interactive
e.g. Animation of realisations
methods

Methods (i) – Focus metaphors
Contour crispness Fog
Fill clarity Resolution
MacEachren (1992)

Methods (ii) – Adjacent maps
Value and uncertainty maps are shown next to each other
Avoids visual overload, but hard to connect two maps
mentally
Rodriguez et al. (2006)

Methods (iii) – Probability of
exceedance
Descriptive statistics: Use IPCC (2001) terminology to
describe probability of exceedance
van de Kassteele & Velders. (2006)

Methods (iv) – Stochastical
dimension in a GIS
Aguila software
– Cumulative probability
distribution for each
pixel or object
Browse either through
probability or values
(thresholds)
– Cumulative/exceedance
probability
– Confidence intervals
Time series visualisation
Scenario view
Pebesma et al. (2007)

Whitening
Hengl (2003)
Other methods
Confidence
intervals
Hierarchical spatial data model
Kardos et al. (2003)

Uncertainty visualisation in the
Data service
e.g. meteorological
measurements
Output
Input
Model Web
Model service
e.g. meteorological
forecast model
Output
Input
Model service
e.g. air quality
model
Web-based uncertainty visualisation client
Final
result

Aim within UncertWeb
Develop a tool that
– enables communication of uncertainties in spatiotemporal
data to different user groups
– allows easy integration into model workflows
following the Model Web paradigm
– visualises inputs, outputs and intermediate steps
– supports different uncertainty and geospatial
encodings

UncertWeb visualisation tool
• Interactive, web-based thin client
• Supports different encodings
– Uncertainties: UncertML 2.0
– Raster data: NetCDF, GeoTIFF
– Vector data: Observations&Measurements (O&M)
• Open Source, based on JavaScript libraries
– OpenLayers (spatial, temporal, spatio-temporal data)
– jStat (non-temporal, non-spatial uncertainties)
– ExtJS (interactive web application controls)
https://svn.52north.org/svn/geostatistics/main/uncertweb/

• Vector data
Implementation details
– Encoded as O&M and UncertML in XML/JSON format
– Directly read by the client
• Raster data
– NetCDF and GeoTiff cannot be directly read by the client
– RESTful Visualisation Service (VISS)
• Create visualisations (raster) from complex sources
– Web Mapping Service (WMS)
• Stores created rasters
• Provides tile-caching
• Many clients available

U-O&M encoding
• Uncertainty Observation type to encode
UncertML types (distribution, samples,
statistics)

NetCDF-U encoding
• Encode uncertainty as dimension or
ancillary_variable
• ref attribute to UncertML definition

SOS
VECTOR DATA
Architecture overview
U-O&M as
XML or JSON
Web
client
WMS
reference
VISS
Creates visualisation
WCS
Stores source data
NetCDF-U
Add layer
Raster
map
WMS
Stores created raster
RASTER DATA

Support for:
Visualisation methods
– Non-spatial & spatial data
– Temporal & Spatio-temporal data
– Continuous & categorical data
– Multivariate data
– Different user backgrounds and experiences
• Different usability of visualisation methods
– Adjacent maps for novice users
– Multidimensional maps for experts

Visualisation methods – Basic
Continuous data
plots
Categorical
data

Visualisation methods – Time
series plots

Visualisation methods –
Categorical data
Adjacent maps
Continuous data

Visualisation methods –
Multidimensional approach
Categorical data
Continuous data

Using the tool
Menu toolbar
Map navigation
Map window
Legend

Adding new resources
1) By Add Resource button
2) By URL Parameter
Parameter Value MIME-Type
oc application/xml
json application/json
tiff image/geotiff
netcdf application/netcdf
rasterOM application/vnd.ogc.om+xml
2a) http://giv-uw.uni-muenster.de/vis/v2/?url=http://giv-uw.unimuenster.de/data/netcdf/biotemp.nc&mime=application/netcdf
2b) http://giv-uw.uni-muenster.de/vis/v2/?netcdf=http://giv-uw.unimuenster.de/data/netcdf/biotemp.nc

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

Wrap-up & Questionnaire
http://surveys.ifgi.de/
� UncertWeb Questionnaire Part B.1: Visualization Tool
Further comments/questions?!