Using open-source data to construct 20 metre resolution maps of children’s travel time to the nearest health facility

Gary R. Watmough; Magnus Hagdorn; Jodie Brumhead; Sohan Seth; Enrique Delamónica; Charlotte Haddon; William C. Smith

doi:10.1038/s41597-022-01274-w

Using open-source data to construct 20 metre resolution maps of children’s travel time to the nearest health facility

Gary R. Watmough, Magnus Hagdorn, Jodie Brumhead, Sohan Seth, Enrique Delamónica, Charlotte Haddon, William C. Smith

Research output: Contribution to journal › Article › peer-review

Abstract

Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.

Original language	English
Article number	217
Journal	Scientific Data
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41597-022-01274-w
Publication status	Published - 17 May 2022

Access to Document

10.1038/s41597-022-01274-wLicence: Creative Commons: Attribution (CC-BY)

Watmough et al.
© The Author(s) 2022
Final published version, 3.38 MBLicence: Creative Commons: Attribution (CC-BY)

https://www.nature.com/articles/s41597-022-01274-wLicence: Creative Commons: Attribution (CC-BY)

1 Finished
1 Active

DCC: Data for children collaborative
Hutchison, A. (Principal Investigator)
19/09/19 → …
Project: Research Collaboration with external organisation
Child Access to Services: Estimating Travel time to health centres in Sub-Saharan Africa
Watmough, G. (Principal Investigator), Seth, S. (Co-investigator), Mlambo, R. (Researcher), Ó Héir, S. (Researcher) & Delamónica , E. (Project Partner)
1/02/24 → 31/08/24
Project: Research

1 Chapter (peer-reviewed)
1 Master's Thesis

Examining the links between child multi-dimensional deprivation and distance to health centres and schools
Watmough, G., Seth, S., Hagdorn, M., Brumhead, J., Delamónica , E., Haddon, C. & Smith, W. C., 28 Jul 2025, Handbook on Child Poverty and Inequality. Minujin, A. & Delamonica, E. (eds.). Edgar Elgar, p. 304–320
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
Testing the utility of displaced Demographic and Health Survey (DHS) Program datasets for measuring travel time to essential services.
Brumhead, J., Watmough, G. & Delamónica , E., 2020
Research output: Thesis › Master's Thesis

Open Access

South Sudan Motorised Travel Time (in seconds) to nearest health centre
Watmough, G. (Creator), Humanitarian Data Exchange, 2024
Dataset
Guinea-Bissau Motorised Travel Time (in seconds) to nearest health centre
Watmough, G. (Creator), Humanitarian Data Exchange, 2025
Dataset
Democratic Republic of the Congo Motorised Travel Time (in seconds) to nearest health centre
Watmough, G. (Creator), Humanitarian Data Exchange, 2025
Dataset

1 Oral presentation

A 100m Resolution Travel Time Map for 54 African Countries
Watmough, G. (Assessor)
Aug 2024
Activity: Academic talk or presentation types › Oral presentation

Cite this

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title = "Using open-source data to construct 20 metre resolution maps of children{\textquoteright}s travel time to the nearest health facility",

abstract = "Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.",

author = "Watmough, \{Gary R.\} and Magnus Hagdorn and Jodie Brumhead and Sohan Seth and Enrique Delam{\'o}nica and Charlotte Haddon and Smith, \{William C.\}",

note = "Funding Information: We thank the constructive comments of the anonymous reviewers and editor for helping to improve this manuscript. This work was funded by the Data for Children Collaborative with UNICEF under the Child Poverty Access to Services project (https://www.dataforchildrencollaborative.com/) a collaboration between the Scottish Government, The DataLab, UNICEF and the University of Edinburgh. This work came about as part of the Data for Children Collaborative (DfCC) with UNICEF. DfCC uses a co-constructive process to bring together expertise across sectors to address questions targeted at improving the lives of children across the globe using innovative data science techniques. The research was compliant with ethics approvals in the school of geosciences at the University of Edinburgh and the DataLab{\textquoteright}s ethical assessment. We thank Alex Hutchison and Alex Fassio for support in designing the project, on-going project management and Ethical advice. We thank Charlotte Marcinko \& Peter Hargreaves for testing the code and providing feedback on the user instructions and Bob Sanders and team for uploading and checking the data files in Edinburgh DataShare facility. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). Funding Information: We thank the constructive comments of the anonymous reviewers and editor for helping to improve this manuscript. This work was funded by the Data for Children Collaborative with UNICEF under the Child Poverty Access to Services project ( https://www.dataforchildrencollaborative.com/ ) a collaboration between the Scottish Government, The DataLab, UNICEF and the University of Edinburgh. This work came about as part of the Data for Children Collaborative (DfCC) with UNICEF. DfCC uses a co-constructive process to bring together expertise across sectors to address questions targeted at improving the lives of children across the globe using innovative data science techniques. The research was compliant with ethics approvals in the school of geosciences at the University of Edinburgh and the DataLab{\textquoteright}s ethical assessment. We thank Alex Hutchison and Alex Fassio for support in designing the project, on-going project management and Ethical advice. We thank Charlotte Marcinko \& Peter Hargreaves for testing the code and providing feedback on the user instructions and Bob Sanders and team for uploading and checking the data files in Edinburgh DataShare facility. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) ( http://www.ecdf.ed.ac.uk/ ). Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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doi = "10.1038/s41597-022-01274-w",

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AU - Seth, Sohan

AU - Delamónica, Enrique

AU - Haddon, Charlotte

AU - Smith, William C.

N1 - Funding Information: We thank the constructive comments of the anonymous reviewers and editor for helping to improve this manuscript. This work was funded by the Data for Children Collaborative with UNICEF under the Child Poverty Access to Services project (https://www.dataforchildrencollaborative.com/) a collaboration between the Scottish Government, The DataLab, UNICEF and the University of Edinburgh. This work came about as part of the Data for Children Collaborative (DfCC) with UNICEF. DfCC uses a co-constructive process to bring together expertise across sectors to address questions targeted at improving the lives of children across the globe using innovative data science techniques. The research was compliant with ethics approvals in the school of geosciences at the University of Edinburgh and the DataLab’s ethical assessment. We thank Alex Hutchison and Alex Fassio for support in designing the project, on-going project management and Ethical advice. We thank Charlotte Marcinko & Peter Hargreaves for testing the code and providing feedback on the user instructions and Bob Sanders and team for uploading and checking the data files in Edinburgh DataShare facility. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/). Funding Information: We thank the constructive comments of the anonymous reviewers and editor for helping to improve this manuscript. This work was funded by the Data for Children Collaborative with UNICEF under the Child Poverty Access to Services project ( https://www.dataforchildrencollaborative.com/ ) a collaboration between the Scottish Government, The DataLab, UNICEF and the University of Edinburgh. This work came about as part of the Data for Children Collaborative (DfCC) with UNICEF. DfCC uses a co-constructive process to bring together expertise across sectors to address questions targeted at improving the lives of children across the globe using innovative data science techniques. The research was compliant with ethics approvals in the school of geosciences at the University of Edinburgh and the DataLab’s ethical assessment. We thank Alex Hutchison and Alex Fassio for support in designing the project, on-going project management and Ethical advice. We thank Charlotte Marcinko & Peter Hargreaves for testing the code and providing feedback on the user instructions and Bob Sanders and team for uploading and checking the data files in Edinburgh DataShare facility. This work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) ( http://www.ecdf.ed.ac.uk/ ). Publisher Copyright: © 2022, The Author(s).

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N2 - Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.

AB - Physical access to health facilities is an important factor in determining treatment seeking behaviour and has implications for targets within the Sustainable Development Goals, including the right to health. The increased availability of high-resolution land cover and road data from satellite imagery offers opportunities for fine-grained estimations of physical access which can support delivery planning through the provision of more realistic estimates of travel times. The data presented here is of travel time to health facilities in Uganda, Zimbabwe, Tanzania, and Mozambique. Travel times have been calculated for different facility types in each country such as Dispensaries, Health Centres, Clinics and Hospitals. Cost allocation surfaces and travel times are provided for child walking speeds but can be altered easily to account for adult walking speeds and motorised transport. With a focus on Uganda, we describe the data and method and provide the travel maps, software and intermediate datasets for Uganda, Tanzania, Zimbabwe and Mozambique.

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Using open-source data to construct 20 metre resolution maps of children’s travel time to the nearest health facility

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