Data Quality in INSPIRE

1. Data Quality in INSPIRE Carol Agius Q-KEN , 5 – 7 May 2010, Brussels

2. 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. This interest can be explained in that quality is one of the data harmonisation components underpinning interoperability. The question of data quality was a re-occurring issue both in the course of data specifications development and the consultations.

3. 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.

4. Discussion Paper Process Drafting 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.

5. 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

6. 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.

7. 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

8. 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.

9. 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)

10. 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)

11. 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.

12. Use cases ESDIN ERM

13. What are the objectives? The INSPIRE data quality and metadata discussions are expected to reach the following objectives: Find evidence whether specifying data quality requirements are appropriate for INSPIRE; If yes, propose a methodological approach and data quality measures together with target values; Fix how metadata on data quality has to be presented; Raise awareness about the role of data quality and metadata in spatial data infrastructures.