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 (SRT)
  National collaborative strategic research and
  technology agenda that looked beyond a modal
  focus harmonising national transportation data. 

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SRT 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

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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

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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

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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)

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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

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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

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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

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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?

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Comparison with ANZLIC Metadata Guidelines

• 400 metadata elements (24 core elements for
  geographic data?) fit into the 6 broad
  attributes, for example
• Quality described by
• Lineage
• Positional accuracy
• Attribute accuracy
• Logical/conceptual consistency
• Completeness/omission
• Currency
• Beginning Date
• End Date
• Maintenance and update frequency

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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