ELF ICC Anja Hopfstock

the Competitiveness and Innovation framework Programme (CIP) ICT Policy Support Programme (PSP) Call 6 Grant 325140 EUROPEAN LOCATION FRAMEWORK

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By:

Date:

Automation of Data Quality Validation based on Common Rules for Pan-European Geoinformation Production

ICC2013

Anja Hopfstock (BKG Germany), Matt Beare (1Spatial), Antti Jakobsson (NLS Finland)

28.08.2013

28 August, 2013


Agenda

Introduction and Background

E.L.F. project

EuroGeographics

Why automation of DQ validation?

What has been done so far?

Results of the ESDIN project

Results of prototype implementation for ERM

Benefits and challenges

Next steps for E.L.F.

Conclusions

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The European Location Framework

technical infrastructure which delivers

authoritative,

interoperable,

cross-border

reference geo-information for analysing and understanding information connected to places and features

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ONE SOURCE FOR

REFERENCE GEO-INFORMATION

FOR EUROPE


NMCA Authoritative

data

… in the sense of turning authoritative reference geodata into a real European location framework

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Project partners and implementation

30 partners

EuroGeographics

15 NMCAs

3 service integrators

6 application developers

2 universities

3 user community representatives

Three phases

Global and Regional

Cluster Areas

New Cluster Areas

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EuroGeographics

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„The official and united voice of Europe's National Mapping and Cadastral Agencies“

Association of European National Mapping and Cadastral Agencies

Currently 59 organisations from 46 countries

www.eurogeographics.org


Pan-European Reference Datasets

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Reference Datasets Harmonisation

Data models Reference data

EuroGlobalMap 1:1 000 000

EuroBoundaryMap 1:100 000

EuroDEM EuroRegionalMap 1: 250 000


Why automation of DQ validation?

Change in how geo-information is produced and consumed

for variety of purposes

broad range of consumers

Multiple sources including VGI

Data models more complex

Reference geo-information needs quality (authoriative data)

INSPIRE directive of the EU(2007) Annex I, II

Connecting reference information to other information

Linked Open Data

Need for provision of cost/time effective and standardised framework to measure and improve quality

Meeting the changed needs

Increase of users’ trust

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Data Quality Management

Three approaches

Data Centric -> evaluating quality (ISO 19157 and ESDIN)

Process Centric -> evaluating capability (ISO 19158)

User Centric approach -> creating trust -> authorative sources , usability evaluation -> ELF

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ESDIN Metadata and Quality guidelines

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Automatic DQ - Pilot Implementation

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Quality reports

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Reports (Excel) Mark-ups (Shapefiles)

Info Title sheet (with information about the rules for Hydro)

Comply features that fail the ‘comply’ rule set

Comply summary of the high-level statistics for

o the whole dataset, o class by class,

o rule by rule basis.

Aspire features that fail the ‘aspire’ rule set

Aspire Same as above for desirable ‘aspire’ data quality rules

Vertex features that fail the specific rule for minimum vertex distance

Profiles set of tables relating to specific data characteristic distributions on specific classes, on the frequency of values across the data

Xborder (for Trans)

highlighting where data is not consistent across state borders


Quality metrics (1)

The “obj Count” column gives the number of features checked

The “No. Fail” column gives the number of feature that have failed the rule(s)

The “% pass” column gives the percentage of conformance regarding the rule, feature class or the whole dataset.

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Quality metrics (2)

By feature class

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Error Mark-up Layers

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Benefits and Challenges

Key benefits

Broadening scope of existing validation process for ERM

Providing measures for usability evaluation

Make informed qualitative assertions on the dataset quality and between national contributions

Challenges

Aggregation where measurements at different scales and units

Aggregation for inhomogeneous data

Reporting details

DQ for producers vs. Users

DQ requirements vs. recommendations

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Next Steps for E.L.F.

Deploy Automatic DQ Process and Rules to Cloud-based Service Environment

Easy access to commonly agreed rule sets for ELF

Consistent assessment across multiple datasets

Assist in provision of homogeneous data content

Define User-Centric Measures for an end user application

For example, establish key data measures needed to assure confidence in the data that will be used to determine risk scores in natural catastrophe risk assessment applications for insurance.

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Conclusions

There is a need to introduce a better management of quality for reference data

Government policies are key driver for the reference geo-information (Open Data, INSPIRE, European Location Framework)

Cost effectiveness is important -> automation of quality evaluation is a prerequisite

User demands > creating trust -> need for authorativiness, accreditation (ISO 19158)

Quality Automation based on ELF will decrease production cost and time -> faster and more frequent release of reference geo-information (e.g. European datasets)

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Thank you for your attention!

Questions

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Contact:

[email protected]

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ELF ICC Anja Hopfstock

Technology

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