@article{8643273,
  abstract     = {{The map presented in this paper summarises the combined land- and airside accessibility within Australia. To this end, we calculate a bimodal accessibility index at the scale of statistical units by aggregating the (shortest) travel time for three route segments: (1) road travel from the origin to a departure airport, (2) air travel, and (3) road travel from an arrival airport to the destination. The average travel time from a statistical unit to all other statistical units is calculated for the units' population centroids, after which an accessibility surface is interpolated using kriging. The map shows that southeastern Australia is generally characterised by a high accessibility index with the most populated cities being hotspots of accessibility. Central and northern Australia are - with few exceptions - far less accessible. In addition to this largely-expected pattern, the map also reveals a number of specific and perhaps more surprising geographical patterns.}},
  author       = {{Meire, Sarah and Derudder, Ben and Ooms, Kristien}},
  issn         = {{1744-5647}},
  journal      = {{JOURNAL OF MAPS}},
  keywords     = {{Bimodal accessibility,air transport,road transport,web-based data,big data}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{77--83}},
  title        = {{A bimodal accessibility analysis of Australia's statistical areas}},
  url          = {{http://doi.org/10.1080/17445647.2019.1608598}},
  volume       = {{15}},
  year         = {{2019}},
}

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