A Data Quality Framework for National Transportation Data

1. A Data Quality Framework for National Transportation Data Flori Mihai Manager Data Planning and Standards Main Roads WA

2. Content Outline • National Transport Policies: Australia Transport Council (ATC) • Strategic Research and Technology: purpose and deliverables • National Transportation Data Quality Framework • Challenges/Opportunities

3. ATC Policies A national transport policy, seamless national transport system, national reform addressing: • Economic Framework for Efficient Transportation Market Place – single national registration scheme for trucks and single driver licence for truck drivers, a key step to introduce the principle of ‘the money following the truck’ - Intelligent Access Program (IAP) • Infrastructure Planning and Investment - interrelationship between land use planning and the passenger transport task in the planning stage of transport infrastructure investments.

4. ATC Policies (cont.) • Urban Congestion -improving the quality of information and transport and land use modelling, enhancing integration of transport and land use planning • Supply Chain - 12 supply chains which are of national significance: coal, iron ore, livestock and meat, copper, grain, seafood, processed foods (including dairy), wine, forestry automotive, petroleum and general freight (including containers) , need good intelligence on the demand for transport services

5. ATC Policies (cont.) • Road Safety - In-vehicle and at-roadside technology, including the digital tachograph technology and other potential solutions that use global positioning systems (GPS); Investigation of a national ‘stars on cars’ program to provide consumers with safety ratings on new light vehicles • Climate Change - A mandatory CO2 emissions standard for cars, light commercial vehicles and sports utility vehicles; research and development of low emissions fuels and transport technologies • Strategic Research and Technology (SR&T) – National collaborative strategic research and technology agenda that looked beyond a modal focus; harmonising national transportation data. 

6. SR&T: National Transportation Data Action Plan • Identify strategically important national transportation data • Identify data gaps (incl. analysis gaps) and data procurement methods - by Oct. 2009 • Define “level One” metadata attributes and “desirable” metadata attributes - by Oct. 2009 • Improve the institutional environment for managing transportation data - by October 2009 • Improve the visibility of the data through a web portal - by Oct. 2009

7. National Transportation Data Quality Framework (QF) Based on Australia Bureau of Statistics QF – six elements; can be used for each data set but also for strategic purposes to assess how existing transportation data meets national policy needs • Relevance • Reflects the degree to which it meets the real needs of clients • Described in terms of key user needs, key concepts and the scope of the collection including the reference period. These components are then compared against specific user needs to assess relevance • Lack of coordination between user needs and available data, or in other words data not relevant to the needs, indicate data gaps

8. National Transportation Data QF (cont.) • Coherence • Reflects the degree to which it can be successfully brought together with other statistical information within a broad analytic framework and over time • Coherence encompasses the internal consistency of a collection as well as its comparability both over time and with other data sources • The use of standard concepts, classifications and target populations, coverage information and data presentation standards promote coherence, as does the use of common methodology across same type surveys

9. National Transportation Data QF (cont.) • Accuracy • The degree to which the information correctly describes the phenomena it was designed to measure • Usually characterised in terms of error in statistical estimates and is traditionally decomposed into bias (systematic error) and variance (random error) components • It may also be described in terms of major sources of error that potentially cause inaccuracy (e.g. sampling, non-response)

10. National Transportation Data QF (cont.) 4. Currency/timeliness • Refers to the delay between the reference point (or the end of the reference period) to which the information pertains, and the date on which the information becomes available • Also refers to data collection frequency

11. National Transportation Data QF (cont.) • Institutional Environment • Refers to the data provider and includes institutional and organisational factors that have a significant influence on the effectiveness and credibility of the agency producing the data • Examples of the information that may be contained within this dimension are professional independence, mandate for data collection, adequate resources, quality commitment, statistical confidentiality, impartiality and objectivity • In a broader context, it can be applied to data governance structures

12. National Transportation Data QF (cont.) • Accessibility • Includes the ease with which the existence of information can be ascertained • The suitability of the form or medium through which the information can be accessed such as file format, compression and exchange protocols • The cost of the information may also be an aspect of accessibility for some users

13. …In a nut shell • Relevance - title and description of the dataset and why was created, what is its purpose? • Coherence - providing information on time series and dataset comparability, logical consistency • Accessibility - how to access information, contact details, data formats available, if any constraints on use • Institutional Environment - who created the dataset, which organisation? • Accuracy - including a description of collection method, quantify sampling error and coverage, and describing accuracy of administrative data • Timeliness and Currency - what is the reference period for the dataset?

14. Comparison with ANZLIC Metadata Guidelines • 400 metadata elements (24 core elements for geographic data?) fit into the 6 broad attributes, for example: Qualitydescribed by: • Lineage • Positional accuracy • Attribute accuracy • Logical/conceptual consistency • Completeness/omission Currency: • Beginning Date • End Date • Maintenance and update frequency

15. Challenges/Opportunities • Right balance between usefulness of the metadata information for the user and the burden on the metadata provider (processes, systems, time, $) • Harmonisation of metadata requirements, start with minimum requirements • Visibility of the data and metadata