The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather (Roberts, Shyam, & Rastogi, 2006). This dataset implements and expands on the most recent official methodology put forward by the World Bank, ReCAP's 2019 RAI Supplemental Guidelines. This is, to date, the only publicly available application of this method at a global scale.MethodologyReCAP's methodology provided new insight on what makes a road all-season and how this data should be handled: instead of removing unpaved roads from the network, the ones that are classified as unpaved are to be intersected with topographic and climatic conditions and, whenever there’s an overlap with excess precipitation and slope, a multiplying factor ranging from 0% to 100% is applied to the population that would access to that road. This present dataset developed by SDSN's SDG Transformation Centre proposes that authorities ability to maintain and remediate road conditions also be taken into account.Data sourcesThe indicator relies on four major items of geospatial data: land cover (rural or urban), population distribution, road network extent and the “all-season” status of those roads.Land cover data (urban/rural distinction)Since the indicator measures the acess rural populations, it's necessary to define what is and what isn't rural. This dataset uses the DegUrba Methodology, proposed by the United Nations Expert Group on Statistical Methodology for Delineating Cities and Rural Areas (United Nations Expert Group, 2019). This approach has been developed by the European Commission Global Human Settlement Layer (GHSL-SMOD) project, and is designed to instil some consistency into the definitions based on population density on a 1-km grid, but adjusted for local situations.Population distributionThe source for population distribution data is WorldPop. This uses national census data, projections and other ancillary data from countries to produce aggregated, 100 m2 population data. Road extentTwo widely recognized road datasets are used: the real-time updated crowd-sourced OpenStreetMap (OSM) or the GLOBIO’s 2018 GRIP database, which draws data from official national sources. The reasons for picking the latter are mostly related to its ability to provide information on the surface (pavement) of these roads, to the detriment of the timeliness of the data, which is restrained to the year 2018. Additionally, data from Microsoft Bing's recent Road Detection project is used to ensure completeness. This dataset is completely derived from machine learning methods applied over satellite imagery, and detected 1,165 km of roads missing from OSM.Roads’ all-season statusThe World Bank's original 2006 methodology defines the term all-season as “… a road that is motorable all year round by the prevailing means of rural transport, allowing for occasional interruptions of short duration”. ReCAP's 2019 methodology makes a case for passability equating to the all-season status of a road, along with the assumption that typically the wet season is when roads become impassable, especially so in steep roads that are more exposed to landslides.This dataset follows the ReCAP methodology by creating an passability index. The proposed use of passability factors relies on the following three aspects:• Surface type. Many rural roads in LICs (and even in large high-income countries including the USA and Australia) are unpaved. As mentioned before, unpaved roads deteriorate rapidly and in a different way to paved roads. They are very susceptible to water ingress to the surface, which softens the materials and makes them very vulnerable to the action of traffic. So, when a road surface becomes saturated and is subject to traffic, the deterioration is accelerated. • Climate. Precipitation has a significant effect on the condition of a road, especially on unpaved roads, which predominate in LICs and provide much of the extended connectivity to rural and poor areas. As mentioned above, the rainfall on a road is a significant factor in its deterioration, but the extent depends on the type of rainfall in terms of duration and intensity, and how well the roadside drainage copes with this. While ReCAP suggested the use of general climate zones, we argue that better spatial and temporal resolutions can be acquired through the Copernicus Programme precipitation data, which is made available freely at ~30km pixel size for each month of the year.• Terrain. The gradient and altitude of roads also has an effect on their accessibility. Steep roads become impassable more easily due to the potential for scour during heavy rainfall, and also due to slipperiness as a result of the road surface materials used. Here this is drawn from slope calculated from SRTM Digital Terrain data.• Road maintenance. The ability of local authorities to remediate damaged caused by precipitation and landslides is proposed as a correcting factor to the previous ones. Ideally this would be measured by the % of GDP invested in road construction and maintenance, but this isn't available for all countries. For this reason, GDP per capita is adopted as a proxy instead. The data range is normalized in such a way that a road maxed out in terms of precipitation and slope (accessibility score of 0.25) in a country at the top of the GDP per capita range is brought back at to the higher end of the accessibility score (0.95), while the accessibility score of a road meeting the same passability conditions in a country which GDP per capita is towards the lower end is kept unchanged.Data processingThe roads from the three aforementioned datasets (Bing, GRIP and OSM) are merged together to them is applied a 2km buffer. The populations falling exclusively on unpaved road buffers are multiplied by the resulting passability index, which is defined as the normalized sum of the aforementioned components, ranging from 0.25 to. 0.9, with 0.95 meaning 95% probability that the road is all-season. The index applied to the population data, so, when calculated, the RAI includes the probability that the roads which people are using in each area will be all-season or not. For example, an unpaved road in a flat area with low rainfall would have an accessibility factor of 0.95, as this road is designed to be accessible all year round and the environmental effects on its impassability are minimal.The code for generating this dataset is available on Github at: https://github.com/sdsna/rai

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