The Australian Urban Observatory metadata provides information about the data and methods used to produce this research.  Data currency, descriptions of measures and links to supporting reference material are also included. Metadata is provided to enable members of the AUO to easily explore and apply this work in their own field and to ensure transparency of our research.

Common indicator components

Many of the indicators are constructed from components that are common. These include:

  • Data projection

  • Digital boundaries

  • Dwellings

  • Sample points

  • Pedestrian road network

  • Local walkable neighbourhood

  • Public transport stops

Data projection

All datasets used were projected to the GDA2020 Geoscience Australia Lambert Conic Conformal projection (GDA2020 GA LCC, EPSG 7845) using the NTv2 transformation grid shift binary prior to analysis.

Digital boundaries

Digital boundaries for all indicators were obtained from the Australian Bureau of Statistics (ABS) Australian Statistical Geography Standard (ASGS) structures.

Capital city boundaries were defined using the ABS Greater Capital City Statistic Areas (GCCSA) digital boundaries and the boundaries of other cities were defined using the ABS Significant Urban Areas (SUA) digital boundaries.

Residential urban areas within cities were defined using ABS Sections of State (SOS) where the SOS classification was ‘major urban’ or ‘other urban’. Major urban centres have a population of 100,000 people of more and other urban centres have a population between 1,000 and 99,999.

Other ABS GCCSA digital boundaries included ABS Mesh Block, SA1, suburbs, and LGA.

Dwellings

A dwelling is a structure that is habitable and intended to have people live in it. Dwellings include houses, motels, flats, caravans, prisons, tents, humpies, and houseboats (ABS 2016). Total dwelling counts were obtained from the 2016 Census, Mesh Block Counts. Counts include all dwellings whether occupied or not. Indicator summaries for LGA, suburb and Statisttical Area 1 neighbourhood regions included in the Australian Urban Observatory are all weighted with regard to urban dwellings.

Sample points

Sample points represent the base unit of all the Australian Urban Observatory indicators and represent a discrete location with a residential address. These locations were based on unique address locations from the Geocoded National Address File (G-NAF) within urban Mesh Blocks where there was as least one dwelling. Sample points in non-urban regions, and in neighbourhoods excluded from the ABS Socio-Economic Indices for Areas (SEIFA) were excluded when calculating the urban liveability indicators.

Pedestrian road network

The pedestrian road network represents the sections of the road network walkable by pedestrians, meaning that it excludes freeways, highways, proposed and private roads. It is assumed that all remaining roads contain a footpath or are otherwise walkable. Pedestrian networks were calculated for the 21 cities using the OSMnx package [1] using OpenStreetMap data from 1 October 2018, up to a distance of 10 kilometres beyond the city boundaries.

Local walkable neighbourhood

The local walkable neighbourhood represents the area around a sample point that is accessible within a 20-minute walk. This was calculated by determining for each sample point the subset of the pedestrian road network reachable within 1600 metres. This ‘walkable neighbourhood’ was buffered by 50 metres to determine its area, for calculation of measures, such as local neighbourhood dwelling density and street connectivity [2].

Public transport stops

In each state and territory in Australia the government department or authority responsible for planning public transport typically publishes data on public transport stops and timetables in a format known as the General Transit Feed Specification (GTFS). Public transport stops and timetables were sourced in 2018 from the OpenMobilityData GTFS feed portal where possible, or direct from transit agencies.

Boeing, G. (2017). OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks.” Computers, Environment and Urban Systems 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004

Forsyth, A., Van Riper, D., Larson, N. et al. (2012). Creating a replicable, valid cross-platform buffering technique: The sausage network buffer for measuring food and physical activity built environments. International Journal of Health Geographics 11, 14. https://doi.org/10.1186/1476-072X-11-14