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Modelling geographic access and school catchment areas across public primary schools to support subnational planning in Kenya

Datos Bibliográficos

ID12711007
AutoresPeter M Macharia (0000-0003-3410-1881, Lancaster University, autor de correspondencia), Angela K Moturi (0000-0002-9614-5786, Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya), Eda Mumo (0000-0001-5856-5694, Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya), Emanuele Giorgi (0000-0002-3910-2261, Lancaster University), Emelda A Okiro (0000-0001-9543-8360, Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya), Ronald W Snow (0000-0003-3725-6088, Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya), Nicolas Ray (0000-0002-4696-5313, University of Geneva)
Año2022
Volumen21
Número5
Páginas832-848
Fecha de publicación2022-12-06
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaChildren s Geographies (JOURNAL)
Identificadores de la revistaISSN: 1473-3277 • E-ISSN: 1473-3285
EditorialTaylor & Francis (PUBLISHER • GB)
DOI10.1080/14733285.2022.2137388
OpenAlexW4311644741
IdiomaEN
Citas recibidas3
Referencias citadas33

Understanding the location of schools relative to the population they serve is important to contextualise the time, students must travel and to define school catchment areas (SCAs) for planning. We assembled a spatio-temporal database of public primary schools (PPS), population density of school-going children (SGC), and factors affecting travel in 2009 and 2020 in Kenya. We combined the assembled datasets within cost distance and cost allocation algorithms to compute travel time to the nearest PPS and define SCAs. We elucidated travel time and marginalised SGC living outside 24-minutes, government's threshold at sub-county level (decision-making units). Weassembled 2170 PPS in 2009 and 4682 in 2020, an increase of 115.8%, while the average travel time reduced from 28 to 17 minutes between 2009 and 2020. Nationally, 65% of SGC were within 24-minutes’ catchment in 2009, which increased to 89% in 2020. Subnationally, 19 and 61 out of 62 sub-counties had over 75% of SGC within the same threshold, in 2009 and 2020, respectively. Findings can be used to target the marginalised SGC, and monitor progress towards attainment of national and Sustainable Development Goals. The framework can be applied in other contexts to assemble geocoded school lists, characterise travel time and model SCA

Business · Cartography · Catchment area · Drainage basin · Economic growth · Economics · Educational attainment · Environmental health · Geocoding · Geographic information system · Geography · Government (linguistics · Population · Transport engineering · Travel time · Engineering · Human Mobility and Location-Based Analysis · Impact of Light on Environment and Health · Medicine · Urban Transport and Accessibility

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Obras citantes distintas3
Citas por año3
Intervalo de citas2026 - 2026 (1)
Velocidad de citacióncurrent
Altamente citadoNo
Tipos de citaNeutras: 2
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