Modelling geographical accessibility to urban centres in Kenya in 2019

Abstract

Background Access to major services, often located in urban centres, is key to the realisation of numerous Sustainable Development Goals (SDGs). In Kenya, there are no up-to-date and localised estimates of spatial access to urban centres. We estimate the travel time to urban centres and identify marginalised populations for prioritisation and targeting. Methods Urban centres were mapped from the 2019 Kenya population census and combined with spatial databases of road networks, elevation, land use and travel barriers within a cost-friction algorithm to compute travel time. Seven travel scenarios were considered: i) walking only (least optimistic), ii) bicycle only, iii) motorcycle only, iv) vehicle only (most optimistic), v) walking followed by motorcycle transport, vi) walking followed by vehicle transport, and vii) walking followed by motorcycle and then vehicle transport (most pragmatic). Mean travel time, and proportion of the population within 1-hour and 2-hours of the urban centres were summarized at sub-national units (counties) used for devolved planning. Inequities were explored and correlations between the proportion of the population within 1-hour of an urban centre and ten SDG indicators were computed. Results A total of 307 urban centres were digitised. Nationally, the mean travel time was 4.5-hours for the walking-only scenario, 1.0-hours for the vehicle only (most optimistic) scenario and 1.5-hours for the walking-motorcycle-vehicle (most pragmatic) scenario. Forty-five per cent (21.3 million people) and 87% (41.6 million people) of Kenya’s population resided within 1-hour of the nearest urban centre for the least optimistic and most pragmatic scenarios respectively. Over 3.2 million people were considered marginalised or living outside the 2-hour threshold in the pragmatic scenario, 16.0 million Kenyans for walking only, and 2.2 million for the most optimistic scenario. County-level spatial access was highly heterogeneous ranging between 8%-100% and 32%-100% of people within the 1-hour threshold for the least and most optimistic scenarios, respectively. Counties in northern and eastern parts of Kenya were generally most marginalised. The correlation coefficients for nine SDG indicators ranged between 0.45 to 0.78 and were statistically significant. Conclusion Travel time to urban centres in Kenya is heterogeneous. Therefore, marginalised populations should be prioritised during resource allocation and policies should be formulated to enhance equitable access to public services and opportunities in urban areas.

Unmet need for COVID-19 vaccination coverage in Kenya

“…Vaccination coverage in urban areas was higher by 27.8% as compared to rural areas. There are several reasons for higher coverages in urban areas, including but not limited to shorter travel times also reported in other studies [54] , [55] , increased availability of vaccines, higher literacy [23] , [24] , occupation [23] , [24] , [25] , [26] , and low hesitancy rates [5] . Despite the higher rates of vaccination coverage in urban areas, it is projected to reach 73.75% in Nairobi and 44.56% in Mombasa by March 2022.…”

“…Fine-scale mapping could refine geographical targeting to reach physically marginalized population. The effect of travel time to the nearest vaccination site was not assessed for changing road conditions in rural areas, effects of by-passing due to vaccine stock-outs [54] , [55] and perceived quality of services offered by the vaccination site [84] . Previous studies that examined changing road conditions showed longer journeys during wet seasons as compared to drier seasons [62] , [85] .…”

“…Geographic information system (GIS) data were assembled for estimating travel time to COVID-19 vaccination centres. These included roads assembled from the ministry of transport and updated via OpenStreetMap and Google Map Maker as detailed elsewhere [54] , [55] , rivers and lakes [56] , [57] , [58] , national parks and reserves [56] , [57] , [58] , Copernicus Sentinel-2 landcover at 20m x 20m spatial resolution, and digital elevation model (DEM) data at 30m spatial resolution available from the RCMRD GeoPortal [59] . The methodology for estimating theoretical travel times to vaccination sites has been demonstrated elsewhere [54] , [55] , [60] , [61] , [62] , [63] and was adopted in this manuscript.…”

“…These included roads assembled from the ministry of transport and updated via OpenStreetMap and Google Map Maker as detailed elsewhere [54] , [55] , rivers and lakes [56] , [57] , [58] , national parks and reserves [56] , [57] , [58] , Copernicus Sentinel-2 landcover at 20m x 20m spatial resolution, and digital elevation model (DEM) data at 30m spatial resolution available from the RCMRD GeoPortal [59] . The methodology for estimating theoretical travel times to vaccination sites has been demonstrated elsewhere [54] , [55] , [60] , [61] , [62] , [63] and was adopted in this manuscript. In brief, a cost distance algorithm based on walking (along footpaths or landcover classified as bare and agricultural) and motorized travel time (along major roads) was used.…”

“…In brief, a cost distance algorithm based on walking (along footpaths or landcover classified as bare and agricultural) and motorized travel time (along major roads) was used. Travel speeds were based on previous studies [54] , [55] , [62] . A correction for slope uphill or downhill was applied based on Tobler’s formulation [64] .…”