The SDG Indicator 9.1.1: The Rural Access Index (RAI), 2023 Release data set, part of the SDGI collection, measures the proportion of the rural population who live within 2 kilometers of an all-season road for a given statistical area. UN SDG 9 is "build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation". Addressing inadequate access to roads, especially in rural areas, is critical to achieving SDG 9. According to the UN Sustainable Transport, Sustainable Development 2021 Interagency Report, sustainable transportation helps to eliminate poverty, promote food security, improve access to key health services, increase trade competitiveness, and bolster human rights. As one measure of progress towards SDG 9, the UN has established SDG indicator 9.1.1. The indicator was computed as the proportion of WorldPop gridded population within 2 kilometers to an OpenStreetMap (OSM) all-season road. The SDG indicator 9.1.1 data set provides estimates for the proportion of the rural population with access to all-season roads for 209 countries and 45,073 subnational Units. The data set is available at both national and level 2 subnational resolutions.

Proportion of the rural population who live within 2 km of an all-season road, total rural population, total rural area, etc., 2023, in support of the Sustainable Development Goals - Indicator 9.1.1.

This survey was conducted to obtain baseline data as part of an evaluation of the Southern Rural Access Program (SRAP), a Robert Wood Johnson Foundation initiative to improve access to health care services in select rural areas of eight states: Alabama, Arkansas, Georgia, Louisiana, Mississippi, South Carolina, West Virginia, and eastern Texas. Within these states, 150 nonmetropolitan counties were selected for SRAP participation based on perceived local health needs, willingness of local organizations and providers to partner with the program's efforts, and prospects for long-term program viability. The SRAP counties demonstrated greater socioeconomic need than other nonmetropolitan counties in the eight states: approximately 50 percent higher poverty rates, 30 percent higher unemployment, and 40 percent greater minority proportions. Topics covered by the survey include health status, health insurance coverage, health care access challenges, confidence in and satisfaction with health care, and utilization of outpatient services including specific disease prevention services. Personal demographic characteristics collected by the survey include age, sex, race, Hispanic origin, primary language spoken at home, marital status, educational achievement, work status, income, number of children at home, and the state, county, town, and ZIP code of residence. The data file also contains county-level and Primary Care Service Area (PCSA)-level contextual variables from external sources, such as population size, population composition by race, number of hospital beds, and variables indicating the presence of short term hospitals and Federally Qualified Health Centers.

CLICK ON THE ABOVE IMAGE TO LAUNCH THE MAP - Healthcare access issues vary greatly between urban and rural areas of New Mexico. Launch the map to explore alternate ways to classify geographies as urban or rural. These classifications are often used for food access as well as healthcare access.BIBLIOGRAPHY WITH LINKS:US Census Bureau, Urban Area - Urban Cluster FAQ - https://www2.census.gov/geo/pdfs/reference/ua/2010ua_faqs.pdfAre the problems with Rural areas actually just a result of definitions that change?: "When a rural county grows, it transmutes into an urban one." - The real (surprisingly comforting) reason rural America is doomed to decline, https://www.washingtonpost.com/business/2019/05/24/real-surprisingly-comforting-reason-rural-america-is-doomed-decline/ (See also the complete study - http://programme.exordo.com/2018annualmeeting/delegates/presentation/130/ )Rural Definitions for Health Policy, Harvey Licht, a presentation for the University of New Mexico Center for Health Policy: : http://nmcdc.maps.arcgis.com/home/item.html?id=7076f283b8de4bb69bf3153bc42e0402Rural Definitions for Health Policy, update of 2019, Harvey Licht, a presentation to the NMDOH Quarterly Epidemiology Meeting, November, 2019 - http://www.arcgis.com/home/item.html?id=a60a73f4e5614eb3ab01e2f96227ce4bNew Mexico Rural-Urban Counties Comparison Tables - October 2017, Harvey Licht, A preliminary compilation for the National Conference of State Legislators Rural Health Plan Taskforce : https://nmcdc.maps.arcgis.com/home/item.html?id=d3ca56e99f8b45c58522b2f9e061999eNew Mexico Rural Health Plan - Report of the Rural Health Planning Workgroup convened by the NM Department of Health 2018-2019 - http://nmcdc.maps.arcgis.com/home/item.html?id=d4b9b66a5ca34ec9bbe90efd9562586aFrontier and Remote Areas Zip Code Map - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=56b4005256244499a58f863c17bbac8aHOUSING ISSUES, RURAL & URBAN, 2017 - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=3e3aeabc04ac4672994e25a1ec94df83FURTHER READING:What is Rural? Rural Health Information Hub: https://www.ruralhealthinfo.org/topics/what-is-ruralDefining Rural. Research and Training Center on Disability in Rural Communities: http://rtc.ruralinstitute.umt.edu/resources/defining-rural/What is Rural? USDA: https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/National Center for Health Statistics Urban–Rural Classification Scheme: https://www.cdc.gov/nchs/data_access/urban_rural.htm.Health-Related Behaviors by Urban-Rural County Classification — United States, 2013, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6605a1.htm?s_cid=ss6605a1_wExtending Work on Rural Health Disparities, The Journal of Rural Health: http://onlinelibrary.wiley.com/doi/10.1111/jrh.12241/fullMinority Populations Driving Community Growth in the Rural West, Headwaters Economics: https://headwaterseconomics.org/economic-development/trends-performance/minority-populations-driving-county-growth/ Methodology - https://headwaterseconomics.org/wp-content/uploads/Minorities_Methods.pdfThe Role of Medicaid in Rural America, Kaiser Family Foundation: http://www.kff.org/medicaid/issue-brief/the-role-of-medicaid-in-rural-america/The Future of the Frontier: Water, Energy & Climate Change in America’s Most Remote Communities: http://frontierus.org/wp-content/uploads/2017/09/FUTURE-OF-THE-FRONTIER_Final-Version_Spring-2017.pdfRural and Urban Differences in Passenger-Vehicle–Occupant Deaths and Seat Belt Use Among Adults — United States, 2014, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6617a1.htm

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

Access to intercity transportation in rural areas. Interactive map and graphs with data available for different time periods.

Total and percent of rural population with access to scheduled intercity bus, rail, and air transportation. Rural areas are Census block groups with their centroid (center) outside of all Census urban areas. Summarized to county level. Facilities used available at: https://data.transportation.gov/Research-and-Statistics/Intercity-Air-Bus-and-Rail-Transportation-Faciliti/xnub-2sc4.

Interactive map showing access to intercity transportation in rural areas: https://datahub.transportation.gov/stories/s/Rural-Access-to-Intercity-Transportation/gr9y-9gjq

Methodology: https://datahub.transportation.gov/stories/s/dbb4-pr2c

Access to electricity, rural (% of rural population) in Philippines was reported at 97.6 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Philippines - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

The share of town and suburban households with internet access in Hungary amounted to approximately 91.88 percent in 2024. In a steady upward trend, the share rose by about 85.23 percentage points from 2004.

The share of town and suburban households with internet access in Finland was approximately 96.36 percent in 2024. Between 2004 and 2024, the share rose by around 51.72 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend.

This data shows the number of people who have access with water in rural areas. The coverage of this data is Tanzania mainland

Access to electricity, rural (% of rural population) in World was reported at 84.27 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Access to electricity, rural (% of rural population) in Burundi was reported at 2.3 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Burundi - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

As of 2024, an estimated ** percent of Europeans living in rural areas accessed the internet, compared to ** percent of the Commonwealth of Independent States (CIS) rural population. The lowest internet usage reach was in rural areas in Africa, at ** percent. Overall, urban areas presented a higher percentage of internet penetration.

The share of town and suburban households with internet access in Poland stood at approximately 95.32 percent in 2024. Between 2004 and 2024, the share rose by around 80.21 percentage points, though the increase followed an uneven trajectory rather than a consistent upward trend.

As of April 2025, there were a total of ***** critical access hospitals in the United States. Most of these were found in Texas, followed by Kansas, Iowa, and Minnesota. The Centers for Medicare and Medicaid services (CMS) gives eligible rural hospitals the designation critical access hospital (CAH) to reduce their financial vulnerability and improve access to healthcare.

Report summarizes grant projects funded through the Rural Access to Health through Healthy Active Built Environment grant program.

Access to electricity, rural (% of rural population) in United States was reported at 100 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Access to electricity, rural (% of rural population) in Mexico was reported at 99.1 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mexico - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Access to electricity, rural (% of rural population) in Turkey was reported at 100 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Turkey - Access to electricity, rural (% of rural population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.