GeoViQua: GeoViQua: the quality the quality challenges for GEOSS challenges for GEOSS YANG Xiaoyu, BLOWER Jon, CORNFORD Dan, LUSH Victoria, MASO Joan, ZABALA Alaitz, Nüst Daniel Center of Research in Ecology and Forestry Applications (CREAF) [email protected]
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GeoViQua: the quality challenges for GEOSS YANG Xiaoyu, BLOWER Jon, CORNFORD Dan, LUSH Victoria, MASO Joan, ZABALA Alaitz, Nüst Daniel Center of Research.
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GeoViQua: GeoViQua: the quality challenges for the quality challenges for
GEOSSGEOSS
YANG Xiaoyu, BLOWER Jon, CORNFORD Dan, LUSH Victoria, MASO Joan, ZABALA Alaitz, Nüst Daniel
Center of Research in Ecology and Forestry Applications (CREAF)[email protected]
www.geoviqua.org
QUAlity aware
VIsualisation for the
Global Earth Observation system of systems
www.geoviqua.org
The problem
• Is there quality information in the GCI?– There is some in the form of ISO19115 DQ elements and lineage– Not enough
• The GEOSS Common Infrastructure does not follow a global model for quality
• The GEOPortal search and results – are not ranged by quality– quality indicators are not shown
• Common data viewers do not generally include quality information in parallel with the data
www.geoviqua.org
The aim
GeoViQua will provide a set of scientifically developed software components and services that facilitate the creation, search and visualization of quality information on EO data integrated and validated in the GEOSS Common Infrastructure.
Pilot case studies
CC RR OO SS SS
SS BB AA
Communitybuilding
GEO S&T Label
www.geoviqua.org
Time table
Start PrototypesValidation
Mobile Solutions
Search & Visualization
Data ready
Quality recommendations
Testing
solutionsPilot cases
User & technical requirements to CoP
User & technical solutions to CoP
Workshops
Proposals evaluation Final documentGeoLabel
Metadata extraction
Best practices quality encoding
Direct extraction from continuous variables
Quality elicitation User feedbackExtraction from categorical variables
• Many researchers refer to the ‘famous five’ as the common criteria for evaluating spatial data quality– lineage; completeness; consistency; positional accuracy; and
attribute accuracy.
• Broad scientific acceptance of the common spatial quality elements does not apply to all cases for “fitness-for-use” evaluation– user requirements can go far beyond the widely accepted ‘famous
five’.
• We used semi-structured telephone and face-to-face interviews with a variety of geospatial data users and experts from a number of countries and application domains.
www.geoviqua.org
What users want?
• Users are exceedingly interested in good quality metadata records – And information that can help to assess fitness-for-use of the data
• Users find metadata records typically incomplete with essential data omitted– The process of dataset discovery and selection is more difficult
• Users are also interested in ‘soft’ knowledge about data quality– Data providers’ comments on the overall quality of a dataset, known data errors, potential
data usage– Peers’ reviews and recommendations (they contact their peers to obtain suggestions)– Dataset provenance, citation and licensing information
• Citation is incomplete (lack of valid producer contact details), and licensing often missing• Citation: users rely on data from good reputation producers
• Currently, some of these cannot be recorded in standard metadata
• Need for easily and systematically compare metadata records– Side-by-side visualisation of all metadata elements would allow geospatial datasets to be
compared more effectively, • especially when datasets are very similar and differences are hard to distinguish
www.geoviqua.org
Producer’s-consumer’s quality
• Producer’s quality metadata– In the producers metadata records– Encoded in the classical ISO 19115/19139– Some extensions required– Stored in the current catalogues (GEOSS Clearinghouse, etc)
• Consumer’s quality metadata– In independent metadata repositories– Linked to producer’s metadata by id– Future component of the GCI?– Contains comments, “like it”, star rates, etc
www.geoviqua.org
The ISO classical view
Quality indicators Provenance/Lineage
Usage
www.geoviqua.org
Add ‘soft’ knowledge to producer’s metadata
Data Quality
Quality Element Non-quantitative Quality Information
Positional Accuracy
Temporal Accuracy
Omission Commission
Missing Items
Number of Missing Items
••
Thematic Accuracy
Quality Parameter (ISO 19157)
Completeness
QualityScope
Metadata
Logical Consistency Usability
Quantitativeattribute accuracy
Non-quantitativeattribute correctness
Classification correctness
Misclassification rate
Misclassificationmatrix
••
Quality Indicator (ISO 19157)
Quality Measure (ISO19157, UncertML)
Dataset series
Dataset
Subset of data
Metadata Packages
0..*
Metaquality
User Feedback
Publication
••
Lineage
Discovered Issues Universe of
Discourse
Feature Type
www.geoviqua.org
Quality model is much more that positional accuracy
• There are many quantifiable aspects that can be recorded– Consistency, completeness, positional,
thematic and temporal accuracy…• There are many qualitative aspects that are
needed– Lineage (traceability), scientific papers, user
Explicit recognition that errors acceptably fit a Normal distribution with mean 1.2 • An overall positive bias was observed • A difficult feature to convey by traditional means)
www.geoviqua.org
The need for a measure dictionary
Absolute external positional accuracy 2Anweisung Straßeninformationsbank (Bundes… 1Codelist omission 2completeness 198Feature represented as a single object 2horizontal 3146Horizontal Positional Accuracy 3265Lagegenauigkeit 3Latitude Resolution 3437Longitude Resolution 3350Mean value of positional uncertainties (2D) 3Overlapping polygon 2Quantitative Attribute Accuracy Assessment 255Rate of missing items 87Sach- und Geodatenüberprüfung 7Temporal Resolution 2870Überprüfung der Toplogie 2Valid code Test 2Vertical Positional Accuracy 1826Vertical Resolution 812vertikal 348Vollständigkeit 4
• Current quality measure names in the GCI– Nothing to do with
ISO19138 list of possible measures
– Not well defined
www.geoviqua.org
Data Quality Measure Dictionary
• Some quality indicators are used, but the name and description of the measure used to derive the indicator are rarely well described.
• Problems can occur due to the lack of semantic definitions of quality measures.
– “uncertainty at 90% significance level” ??. • A Quality Measure Dictionary is proposed that
includes:– vocabularies for quality measures– associated semantic annotations – integrate UncertML concepts and vocabularies.
• Composed on quality measures provided by – ISO138 ISO19157 – UncertML.
• Measure has a unique ID– quality element, value type, quality basic measure,
description, example use, etc. • “uncertainty at 90% significance level” can be
annotated using UncertML vocabulary “ConfidenceInterval”(URI: http://www.uncertml.org/statistics/confidence-interval)
• Dark color represents poor quality and light color good quality
Blackmond Laskey K, EJ. Wright PCG da Costa (2009) Envisioning uncertainty in geospatial information
Quality aware visualisation
toolsExpress data quality using maps
Devillers R, Bédard Y, R Jeansoulin (2005) Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS
www.geoviqua.org
• 3D representations– representation of
estimated water balance surplus/deficit and their uncertainty (using bars above and below the surface).
• Map representations have some problems– Makes visualization more complicated
and difficult to understand
– Attracting the attention to the more uncertain objects!!
MacEachren AM, A Robinson, S Hopper, S Gardner, R Murray, M Gahegan, E Hetzler (2005) Visualizing Geospatial Information Uncertainty; What We Know and What We Need to Know
Pang A (2001) Visualizing Uncertainty in Geo-spatial Data
Quality aware visualisation
tools
Quality map visualization
www.geoviqua.org
Pilot Case scenarios
Agriculture
Based on many user stories among GEOSS SBA
Global Carbon
Air Quality
Please participate in the questionnaire:
http://geolabel.questionpro.com just a couple of days left!!