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

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

Testing the utility of displaced Demographic and Health Survey (DHS) Program datasets for measuring travel time to essential services.

This research is focused on the Demographic and Health Survey (DHS) Program datasets, which are some of the datasets most frequently used to measure progress towards the Sustainable Development Goals (Oxford Poverty and Human Development Initiative, 2020). To ensure respondent confidentiality, the survey geolocation data is aggregated and displaced before the data is released (Burgert et al., 2013). The impact of the geolocation displacement when linking with environment data or Euclidian distance measurements to services has been studied, but not the impact the displacement may have on estimating travel time (Burgert et al., 2013; Skiles et al., 2013; Warren et al., 2016; Grace et al., 2019). Euclidian distance measurements are known to overestimate access to services and therefore understanding how the geolocation displacement impacts travel time to services is essential (Noor et al., 2006). We have analysed how the displacement error impacts measuring travel time to services and how the displacement error can be reduced. We utilised pseudo data points to consider a comparison of the travel times to healthcare services measured for original location, displaced location, settlement nearest to displaced location and all settlements in a buffered region around the displaced point. The results showed that accuracy when measuring travel time to services was significantly impacted, with the displacement error being spatially heterogenous. Ancillary population and settlement data were shown to be useful in reducing the displacement error. The findings show that DHS geolocation displaced data should be used with extreme caution when measuring travel time to services