TY - JOUR
T1 - A geospatial database of close-to-reality travel times to obstetric emergency care in 15 Nigerian conurbations
AU - Macharia, Peter M
AU - Wong, Kerry L M
AU - Olubodun, Tope
AU - Beňová, Lenka
AU - Stanton, Charlotte
AU - Sundararajan, Narayanan
AU - Shah, Yash
AU - Prasad, Gautam
AU - Kansal, Mansi
AU - Vispute, Swapnil
AU - Shekel, Tomer
AU - Gwacham-Anisiobi, Uchenna
AU - Ogunyemi, Olakunmi
AU - Wang, Jia
AU - Abejirinde, Ibukun-Oluwa Omolade
AU - Makanga, Prestige Tatenda
AU - Afolabi, Bosede B
AU - Banke-Thomas, Aduragbemi
N1 - FTX; DOAJ; (CC BY 4.0)
PY - 2023
Y1 - 2023
N2 - Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions. Here, we describe a database of travel times to comprehensive EmOC facilities in the 15 most populated extended urban areas of Nigeria. The travel times from cells of approximately 0.6 × 0.6 km to facilities were derived from Google Maps Platform's internal Directions Application Programming Interface, which incorporates traffic considerations to provide closer-to-reality travel time estimates. Computations were done to the first, second and third nearest public or private facilities. Travel time for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility.
AB - Travel time estimation accounting for on-the-ground realities between the location where a need for emergency obstetric care (EmOC) arises and the health facility capable of providing EmOC is essential for improving pregnancy outcomes. Current understanding of travel time to care is inadequate in many urban areas of Africa, where short distances obscure long travel times and travel times can vary by time of day and road conditions. Here, we describe a database of travel times to comprehensive EmOC facilities in the 15 most populated extended urban areas of Nigeria. The travel times from cells of approximately 0.6 × 0.6 km to facilities were derived from Google Maps Platform's internal Directions Application Programming Interface, which incorporates traffic considerations to provide closer-to-reality travel time estimates. Computations were done to the first, second and third nearest public or private facilities. Travel time for eight traffic scenarios (including peak and non-peak periods) and number of facilities within specific time thresholds were estimated. The database offers a plethora of opportunities for research and planning towards improving EmOC accessibility.
U2 - 10.1038/s41597-023-02651-9
DO - 10.1038/s41597-023-02651-9
M3 - A1: Web of Science-article
C2 - 37872185
SN - 2052-4463
VL - 10
JO - Scientific Data
JF - Scientific Data
M1 - 736
ER -