A bimodal accessibility analysis of Australia’s statistical areas

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...

Full description

Bibliographic Details
Main Authors: Sarah Meire, Ben Derudder, Kristien Ooms
Format: Article
Language:English
Published: Taylor & Francis Group 2019-01-01
Series:Journal of Maps
Subjects:
Online Access:http://dx.doi.org/10.1080/17445647.2019.1608598
id doaj-d79f2e317ef340c7aefa5365cfcea401
record_format Article
spelling doaj-d79f2e317ef340c7aefa5365cfcea4012020-11-25T02:17:07ZengTaylor & Francis GroupJournal of Maps1744-56472019-01-01151778310.1080/17445647.2019.16085981608598A bimodal accessibility analysis of Australia’s statistical areasSarah Meire0Ben Derudder1Kristien Ooms2Ghent UniversityGhent UniversityGhent UniversityThe 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.http://dx.doi.org/10.1080/17445647.2019.1608598bimodal accessibilityair transportroad transportweb-based databig data
collection DOAJ
language English
format Article
sources DOAJ
author Sarah Meire
Ben Derudder
Kristien Ooms
spellingShingle Sarah Meire
Ben Derudder
Kristien Ooms
A bimodal accessibility analysis of Australia’s statistical areas
Journal of Maps
bimodal accessibility
air transport
road transport
web-based data
big data
author_facet Sarah Meire
Ben Derudder
Kristien Ooms
author_sort Sarah Meire
title A bimodal accessibility analysis of Australia’s statistical areas
title_short A bimodal accessibility analysis of Australia’s statistical areas
title_full A bimodal accessibility analysis of Australia’s statistical areas
title_fullStr A bimodal accessibility analysis of Australia’s statistical areas
title_full_unstemmed A bimodal accessibility analysis of Australia’s statistical areas
title_sort bimodal accessibility analysis of australia’s statistical areas
publisher Taylor & Francis Group
series Journal of Maps
issn 1744-5647
publishDate 2019-01-01
description 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.
topic bimodal accessibility
air transport
road transport
web-based data
big data
url http://dx.doi.org/10.1080/17445647.2019.1608598
work_keys_str_mv AT sarahmeire abimodalaccessibilityanalysisofaustraliasstatisticalareas
AT benderudder abimodalaccessibilityanalysisofaustraliasstatisticalareas
AT kristienooms abimodalaccessibilityanalysisofaustraliasstatisticalareas
AT sarahmeire bimodalaccessibilityanalysisofaustraliasstatisticalareas
AT benderudder bimodalaccessibilityanalysisofaustraliasstatisticalareas
AT kristienooms bimodalaccessibilityanalysisofaustraliasstatisticalareas
_version_ 1724888045306511360