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CARA Access Metrics

This page provide a brief technical summary of how CARA calculates national distance & time access metrics.

Context

The time and distance it takes to travel to the services is one of the core characteristics of the built environment. Many of the key decisions people make, such as the purchase of a home, revolve around access to the education, health and government services they rely on.

Detailed and accurate measures of access to services are important to understanding of the supply and demand for services and central to the development of policy to fairly distribute the availability of services across a population

The Centre for Australian Research into Access (CARA) has applied modern data and computing processing techniques to develop infrastructure that rapidly calculates access to services metrics for every Australian private dwelling. Further, CARA has developed spatial micro-simulation models to simulates people, families and households within these dwellings, which allows access to different services to be evaluated within different groups of the Australian population.

The ability to calculate metrics directly from every dwelling in Australia to the building within which a service is located, allows CARA to produce metrics that are independent of any arbitrary spatial unit and therefore can be aggregated to any type of area. This allows these metrics to be free from the statistical biases and constraints imposed by arbitrarily aggregation such as the Modifiable Areal Unit Problem (MAUP) and the Ecological Fallacy (Openshaw, 1984).

Calculation Process

The inputs to the CARA time/distance metrics are;

  • A collection of starting points or origins from which to measures are made.
  • A network of lines composed of nodes and segments that model the topology (connectiveness), restrictions (one/two-way, overpasses etc.) and traffic speeds associated with the Australian road network.
  • A collection of end points or destinations (services) to which measure are made.

The origins (dwelling locations) are derived from the CARA Dwelling Location Reference Frame (DLRF). This is a modelled uses the 2021 Australian Census of Population & Housing’s mesh block count of private dwellings which are distributed to individual building footprints based on a range of criteria tailored to the nature of the mesh block.

The destinations (service locations) are sourced from either national registers, such as ACARA or from commercially curated data sets of points of interest, when no official sources are available. Table X provides a listing of the registers used by CARA.

The network topologically models the roads, junctions, and traffic directions of the physical road system. Each road segment is associated with an impedance (distance, speed). Data on road speeds is derived through aggregate measurement of active vehicles by TomTom. Time is derived from the minimum speeds during either the morning or evening peak periods.

Shortest time/distance network times and distances are calculated by traversing the network using the Dijkstra’s algorithm which is enhanced by applying a Contraction Hierarchy Heuristic. This algorithm is implemented in C++ and enabled as a Python library called Pandana. Pandana it was developed by UrbanSim at the University of Berkley (Foti & Waddell, 2012)
The Pandana library operates by linking the point location of origins (and destinations) to their nearest node on the network. The distance of this straight line is derived, and the associated time is calculated based on a mean speed value across the whole of the network (20km/h). These times and distances are added to each of the shortest routes calculated by Pandana. For the majority of origins and destinations this distance is small in both absolute and relative terms because of the density of the road network in urban areas where the majority of origins, destinations and their associated routes exist.

References

Foti, F., Waddell, P. & Luxen, D., (2012) A Generalized Computational Framework for Accessibility: From the Pedestrian to the Metropolitan Scale., Transportation Research Board Annual Conference.
Openshaw, S. (1984). Ecological Fallacies and the Analysis of Areal Census Data. Environment and Planning A: Economy and Space, 16(1), 17-31. https://doi.org/10.1068/a160017
Openshaw, S. (1984), The Modifiable Areal Unit Problem, Concepts and Techniques in Modern Geography, Geobooks.