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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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Presentation on theme: "GeoViQua: the quality challenges for GEOSS YANG Xiaoyu, BLOWER Jon, CORNFORD Dan, LUSH Victoria, MASO Joan, ZABALA Alaitz, Nüst Daniel Center of Research."— Presentation transcript:

1 GeoViQua: the quality 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)

2 QUAlity aware VIsualisation for the Global Earth Observation system of systems

3 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

4 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 C R O S S S B A Community building GEO S&T Label

5 Time table Start Prototypes Validation Mobile Solutions Search & Visualization Data ready Quality recommendations Testing solutions Pilot cases User & technical requirements to CoP User & technical solutions to CoP Workshops Proposals evaluation Final document GeoLabel Metadata extraction Best practices quality encoding Direct extraction from continuous variables Quality elicitation User feedback Extraction from categorical variables February 2011 January 2012 January 2013 December Requirements and Data Model phase finished,

6 Community Views on Data Quality 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.

7 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

8 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

9 The ISO classical view Quality indicators Provenance/Lineage Usage

10 Add ‘soft’ knowledge to producer’s metadata

11 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 feedback, data usage…

12 GeoViQua Data model: statistical uncertainties m 3.6 Value of the vertical DEM accuracy m 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)

13 The need for a measure dictionary Absolute external positional accuracy2 Anweisung Straßeninformationsbank (Bundes…1 Codelist omission2 completeness198 Feature represented as a single object2 horizontal3146 Horizontal Positional Accuracy3265 Lagegenauigkeit3 Latitude Resolution3437 Longitude Resolution3350 Mean value of positional uncertainties (2D)3 Overlapping polygon2 Quantitative Attribute Accuracy Assessment255 Rate of missing items87 Sach- und Geodatenüberprüfung7 Temporal Resolution2870 Überprüfung der Toplogie2 Valid code Test2 Vertical Positional Accuracy1826 Vertical Resolution812 vertikal348 Vollständigkeit4 Current quality measure names in the GCI –Nothing to do with ISO19138 list of possible measures –Not well defined

14 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:

15 Quality Metadata Levels

16 Registered Community Resources Community Portals Client Applications Client Tier Business Process Tier Community Catalogues Alert Servers Workflow Management Processing Servers Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers GEOSS Clearinghouse GEO Web Portals GEOSS Common Infrastructure Components & Services Standards and Interoperability Best Practices Wiki User Requirements Registries Main GEO Web Site GEOSS common infrastructure

17 Before GEOSS Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Capacity Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity

18 GEOSS Common Infrastructure How GEOSS worked yesterday Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Capacity Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity Components & Services Registry GEO Web Portal GEOSS Clearinghouse Catalogue DB

19 GEOSS Common Infrastructure How GEOSS is going to work Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Community Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity Components & Services Registry GEO Web Portal GEOSS Clearinghouse Catalogue DB Community Catalogue Community Catalogue Community Catalogue Capacity Catalogue EuroGEOSS Broker

20 GEOSS Common Infrastructure How GEOSS is going to work Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Community Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity Components & Services Registry GEO Web Portal GEOSS Clearinghouse Catalogue DB Community Catalogue Community Catalogue Community Catalogue Capacity Catalogue EuroGEOSS Broker EuroGEOSS Broker

21 GeoViQua quality model EuroGEOSS Broker model GeoViQua Model

22 GEOSS Common Infrastructure Quality in GEOSS Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Capacity Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity Components & Services Registry GEO Web Portal GEOSS Clearinghouse Catalogue DB Community Catalogue Community Catalogue Community Catalogue Capacity Catalogue EuroGEOSS Broker Enhanced geo-search tools

23 Including data quality in search SELECT  WHERE positional_accuracy 90% FROM GEOSS_GCI Devillers R, Bédard Y, R Jeansoulin (2005) Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS Enhanced geo-search tools

24 Consumer’s data quality More informal Based on social network patterns –Comments –Linked data –Like it –Star ratings More dinàmic Need for an encoding Need for an independent repository

25 GEOSSBack Just a prototype to play with and demonstrate a concept.

26 Producer’s+consumer’s GeoViQua Broker

27 Quality Metadata comparison

28 Conclusions After user interviews Producer’s quality model –GeoViQua quality model is based in ISO –With extensions for ‘soft’ knowledge –Inclusions of uncertML –Quality measure dictionary Consumer’s quality model –Based on social network patterns –Encoded independently (from producers) Linked by the GeoViQua broker (extension/complement of the EuroGEOSS broker)

29 What is it? –The GEO Label is intended to “assist the user to assess the scientific relevance, quality, acceptance and societal needs of the components” (ST Task Team, 2010). Purposes? –be a quality indicator for GEOSS geospatial data and datasets Problem: Usability depends on data application; there is no defined threshold. –improve user recognition and trust in validated datasets. Problem: who is going to certify this? –assist in searching by providing users with visual clues of dataset quality and relevance. –provide accreditation, provenance, monitoring –increase visibility of EO data –Emphasize in open access and easy availability Possible shape? –Certification label –A formal way to present quality indicators provenance attribution GEOLabel Task performed in collaboration with EGIDA FP7 project and the GEO task ST-09-02

30 Until the end of this week Publicly available in the web We encourage you to participate! GEOLabel

31 Please participate in the questionnaire: just a couple of days left!! Thanks (CREAF)

32 Please participate in the questionnaire: just a couple of days left!! Thanks (CREAF)

33 GEOSS Common Infrastructure How GEOSS is going to work Access Tier GEONETCast Product Access Servers Sensor Web Servers Model Access Servers Business Process Tier Capacity Catalogues Capacity Resource User SBA Disasters Health Energy Climate Water Weather Ecosystems Agriculture Biodiversity Components & Services Registry GEO Web Portal GEOSS Clearinghouse Catalogue DB Community Catalogue Community Catalogue Community Catalogue Copacity Catalogue EuroGEOSS Broker Quality Access Broker Quality aware visualisation tools

34 Quality map visualization 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 tools Express data quality using maps Devillers R, Bédard Y, R Jeansoulin (2005) Multidimensional Management of Geospatial Data Quality Information for its Dynamic Use Within GIS

35 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

36 Pilot Case scenarios Agriculture Based on many user stories among GEOSS SBA Global Carbon Air Quality

37 Please participate in the questionnaire: just a couple of days left!! Thanks (CREAF)


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