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Data Quality in INSPIRE. Carol Agius Q-KEN , 5 – 7 May 2010, Brussels. Rational for discussion paper. discussion dealt with the widely diverging opinions ranging from introducing strict data quality requirements for all data included in the infrastructure to complete omission of requirements. - PowerPoint PPT Presentation
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Data Quality in INSPIRE
Carol Agius
Q-KEN , 5 – 7 May 2010, Brussels
Rational for discussion paper
• The question of data quality was a re-occurring issue both
in the course of data specifications development and the
consultations.
Page 2
• discussion dealt with the widely diverging opinions
ranging from introducing strict data quality requirements
for all data included in the infrastructure to complete
omission of requirements.
This interest can be explained in that quality is one of the data harmonisation components underpinning
interoperability.
• The paper prepares and guides the discussions by clarifying details
and giving the initial position to stimulate the exchange of views. It is
expected that the results of discussions can be incorporated in the
data specifications of Annex II and III data, and if necessary, the
modifications can be done in respect of Annex I and in the
documents of the conceptual framework.
Page 3
Discussion Paper ProcessDrafting the discussion paper• The discussion paper will scope the subjects and propose initial position in the subject.
Consultations in the Member States• The discussion paper sent for consultation in the MS nominated data quality contact points, for
review and a consolidated national position .
Analysis of the results of country-consultation• synthesise the responses, highlight the issues where further discussions are needed. The draft report
will be sent back to the national DQ contact points.
Face-to-face discussion• The DQ contact points will be invited to the workshop at INSPIRE Conference to present official
national position of their countries and should be able to provide reasoned arguments for their
particular position.
Final report• The draft report will be updated with the results of the Krakow DQ workshop and will be disseminated
to wider public. It will provide recommendations for possible updates of INSPIRE documents and how
data quality and metadata should be addressed in spatial data infrastructures in general.
Data quality requirements vrs Metadata
• Data are produced following data specifications which are fixed
prior to the production of the dataset
• The requirements are expressed as values of data quality measures
for each data quality element
• Metadata is a description of the data and includes a report of the
data quality achieved after the data are produced
Page 5
The SDI Point of view
• SDIs give access to existing data – setting data quality
requirements is not viewed as being so important
• SDIs provide the basis to interoperable spatial information
systems – setting data quality requirements is important
• Metadata on data is an indispensible aspect of an SDI while the
data quality requirements (specifications) might not be.
• Metadata will need to be updated because of the eventual
deterioration of the data quality due to transformations necessary
for reaching interoperability.
Page 6
INSPIRE Data Specifications – Annex I experience
The INSPIRE data specifications follow the structure of ISO 19131
which requires a data specification to cover the data quality
elements and data quality sub-elements defined in ISO 19113.
Those quality elements are:
• Completeness
• Logical Consistency
• Positional Accuracy
• Temporal Accuracy
• Thematic Accuracy
• Apart from logical consistency, the Directive does not
directly spell out requirements for data quality.
• Consequently the Methodology for Data Specification
Development (D2.6) does not recommend prescribing
minimum data quality requirements in general. Minimum
data quality requirements should be justified by the user
requirements. In this case introducing conformity levels is
recommended to be reported in the metadata records.
Metadata elements related to data quality
Elements defined:•MD Lineage (Statement on process history and /or overall DQ of the spatial data set)•Spatial resolution (Level of detail of the data set)
Draft IR for Interoperability of spatial data sets and services
• Mandatory elements:
• Logical Consistency – Topological Consistency (Hy, TN)
• Optional elements:
• Completeness – Commission (AD, AU, Hy, PS, TN)
• Completeness – Omission (All)
• Positional Accuracy – Absolute or external accuracy (All)
• Logical Consistency – Conceptual Consistency (AD, Hy, TN)
• Logical Consistency – Domain Consistency (AD, Hy, TN)
• Logical Consistency – Format Consistency (TN)
• Temporal accuracy – Temporal Consistency (AD)
• Thematic accuracy – Non-quantitative attribute correctness (AD, Hy, TN)
• Thematic accuracy – Quantitative attribute correctness (Hy)
• Thematic accuracy – Thematic Classification correctness (TN)Page 10
Problem .....
• The aim to enable the possibility to combine spatial
data from different sources across the Community in
a consistent way and share them between several
users and applications represents a strong data
quality demand.
Use cases
• ESDIN
• ERM
Page 12
What are the objectives?
The INSPIRE data quality and metadata discussions are expected to reach the following
objectives:
1. Find evidence whether specifying data quality requirements are appropriate for INSPIRE;
2. If yes, propose a methodological approach and data quality measures together with target
values;
3. Fix how metadata on data quality has to be presented;
4. Raise awareness about the role of data quality and metadata in spatial data
infrastructures.