Centaur VGI: a hybrid human-machine approach to address global inequalities in map coverageCitation formats

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Centaur VGI: a hybrid human-machine approach to address global inequalities in map coverage. / Huck, Jonny; Perkins, Christopher; Haworth, Billy Tusker; Moro, Emmanuel; Nirmalan, Mahesh .

In: Annals of the Association of American Geographers, Vol. 111, No. 1, 01.01.2021, p. 231-251.

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Huck, Jonny ; Perkins, Christopher ; Haworth, Billy Tusker ; Moro, Emmanuel ; Nirmalan, Mahesh . / Centaur VGI: a hybrid human-machine approach to address global inequalities in map coverage. In: Annals of the Association of American Geographers. 2021 ; Vol. 111, No. 1. pp. 231-251.

Bibtex

@article{21f8c120118847b8b2512d1603a45ba9,
title = "Centaur VGI: a hybrid human-machine approach to address global inequalities in map coverage",
abstract = "Despite advances in mapping technologies and spatial data capabilities, global mapping inequalities are not declining. Inequalities in the coverage, quality, and currency of mapping persist, with significant gaps in remote and rural parts of the Global South. These regions, representing some of the most economically and resource-disadvantaged societies in the world, need high-quality mapping to aid in the delivery of essential services, such as health care, in response to severe challenges such as poverty, conflict, and global climate change. Volunteered geographic information (VGI) has shown potential as a solution to mapping inequalities. Contributions have largely been made in urban areas or in response to acute emergencies (e.g., earthquakes or floods), however, leaving rural regions that suffer from chronic humanitarian crises undermapped. An alternative solution is needed that harnesses the power of volunteer mapping more effectively to address regions in most need. Machine learning holds promise. In this article we propose centaur VGI, a hybrid system that combines the spatial cognitive abilities of human volunteers with the speed and efficiency of a machine. We argue that centaur VGI can contribute to mitigating some of the political and technological factors that produce inequalities in VGI mapping coverage and do so in the context of a case study in Acholi, northern Uganda, an inadequately mapped region in which the authors have been working since 2017 to provide outreach health care services to victims of major limb loss during conflict.",
keywords = "OpenStreetMap, VGI, humanitarian mapping, machine learning, mapping inequalities",
author = "Jonny Huck and Christopher Perkins and Haworth, {Billy Tusker} and Emmanuel Moro and Mahesh Nirmalan",
year = "2021",
month = jan,
day = "1",
doi = "https://doi.org/10.1080/24694452.2020.1768822",
language = "English",
volume = "111",
pages = "231--251",
journal = "Annals of the Association of American Geographers",
issn = "0004-5608",
publisher = "Routledge",
number = "1",

}

RIS

TY - JOUR

T1 - Centaur VGI: a hybrid human-machine approach to address global inequalities in map coverage

AU - Huck, Jonny

AU - Perkins, Christopher

AU - Haworth, Billy Tusker

AU - Moro, Emmanuel

AU - Nirmalan, Mahesh

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Despite advances in mapping technologies and spatial data capabilities, global mapping inequalities are not declining. Inequalities in the coverage, quality, and currency of mapping persist, with significant gaps in remote and rural parts of the Global South. These regions, representing some of the most economically and resource-disadvantaged societies in the world, need high-quality mapping to aid in the delivery of essential services, such as health care, in response to severe challenges such as poverty, conflict, and global climate change. Volunteered geographic information (VGI) has shown potential as a solution to mapping inequalities. Contributions have largely been made in urban areas or in response to acute emergencies (e.g., earthquakes or floods), however, leaving rural regions that suffer from chronic humanitarian crises undermapped. An alternative solution is needed that harnesses the power of volunteer mapping more effectively to address regions in most need. Machine learning holds promise. In this article we propose centaur VGI, a hybrid system that combines the spatial cognitive abilities of human volunteers with the speed and efficiency of a machine. We argue that centaur VGI can contribute to mitigating some of the political and technological factors that produce inequalities in VGI mapping coverage and do so in the context of a case study in Acholi, northern Uganda, an inadequately mapped region in which the authors have been working since 2017 to provide outreach health care services to victims of major limb loss during conflict.

AB - Despite advances in mapping technologies and spatial data capabilities, global mapping inequalities are not declining. Inequalities in the coverage, quality, and currency of mapping persist, with significant gaps in remote and rural parts of the Global South. These regions, representing some of the most economically and resource-disadvantaged societies in the world, need high-quality mapping to aid in the delivery of essential services, such as health care, in response to severe challenges such as poverty, conflict, and global climate change. Volunteered geographic information (VGI) has shown potential as a solution to mapping inequalities. Contributions have largely been made in urban areas or in response to acute emergencies (e.g., earthquakes or floods), however, leaving rural regions that suffer from chronic humanitarian crises undermapped. An alternative solution is needed that harnesses the power of volunteer mapping more effectively to address regions in most need. Machine learning holds promise. In this article we propose centaur VGI, a hybrid system that combines the spatial cognitive abilities of human volunteers with the speed and efficiency of a machine. We argue that centaur VGI can contribute to mitigating some of the political and technological factors that produce inequalities in VGI mapping coverage and do so in the context of a case study in Acholi, northern Uganda, an inadequately mapped region in which the authors have been working since 2017 to provide outreach health care services to victims of major limb loss during conflict.

KW - OpenStreetMap

KW - VGI

KW - humanitarian mapping

KW - machine learning

KW - mapping inequalities

U2 - https://doi.org/10.1080/24694452.2020.1768822

DO - https://doi.org/10.1080/24694452.2020.1768822

M3 - Article

VL - 111

SP - 231

EP - 251

JO - Annals of the Association of American Geographers

JF - Annals of the Association of American Geographers

SN - 0004-5608

IS - 1

ER -