Simplistic models of accessibility impose costs, as the failure to measure accessibility in units of time leads to incorrect or vague assessments of service facility catchment areas. Traditional linear measurements do not account for the impact of terrain types, slope, presence of roads, etc. on travel conditions. These factors are especially relevant in rural Nepal where a hypothetical child could live within 2 linear kilometers of a school, but be on the wrong side of a river, valley or mountain, or simply 1000m below the school.

# Measurement approaches

Most country‐scale research into remoteness in Nepal uses basic weighted indices of subnational units, suitable for high‐level analysis but not for measuring localized travel times or facility level accessibility. A recent example comes from Dempsey (2016), who assessed remoteness at the Village Development Committee (VDC) level using a simple weighted linear combination (WLC) model. She graded different input factors by their level of remoteness and weighted them based on expert judgments from staff at the United States Agency for International Development (USAID). Elsewhere, the World Bank has calculated a Rural Access Index for subnational administrative units in Nepal and other countries based on road network coverage and quality (Roberts et al. 2006 / Iimi et al. 2016). Huber (2015) went one step further and created a rasterized cost time travel model for Nepal, even including a separate monsoon model to reflect the sharp seasonal changes in accessibility where roads are poor. But to create this raster Huber interpolated missing values from the results of a network analysis (Rodrigue et al. 2009) of road vector lines, ignoring off‐road travel and travel impedances from terrain, landcover, bridges, etc. He also calculated remoteness in terms of travel times to Kathmandu, not in terms of services. Reaching further back, Donner (1972) published a map showing path lengths in units of porter days. All the above studies use incomplete and partial transportation data sets, particularly of pathways, implying a degree of inherent error.

Researchers in Nepal not working off custom models tend to use reported travel times from household surveys, especially the Nepal Living Standards Survey (e.g. Jacoby 2000, Fafchamps and Hill 2005). There are departures from this approach. Thapa and Shively (2017) estimate the relationship between accessibility and agricultural and food security indicators using paved road densities (per km2). Elsewhere, the International Labor Organization (ILO) recommends the use of Integrated Rural Accessibility planning in Nepal (ILO 2005). This is a participatory approach to assessing access and planning rural infrastructure development accordingly. On a local level Devkota et al. (2012) built a gravity model of interactions over trail bridges using network analysis to indicate access to various services and optimum locations for additional bridges. The model is promising for small‐scale applications but too reliant on rich local data to easily scale.

The government of Nepal’s treatment of remoteness varies considerably between ministries. Traditionally, ministries and development actors capture such metrics using self‐reported travel times from surveys or administrative questionnaires sent to local officials (MOE and UNESCO 2015). For instance, the Ministry of Health’s Second Long Term Health Plan (2007, pg. 10), called for:

- _“Essential Health Services at the District...[to be] available to 90 percent of the population living within 30 minutes travel time”_

Similarly, the Ministry of Education repeatedly references the number and types of children within 30 minutes walking distance to primary schools and 1 hour to secondary schools in its Consolidated Equity Strategy (MOE 2014).

The Department of Roads (inconsistently) embraces more sophisticated approaches to measuring accessibility when planning new infrastructure. This is largely to comply with the Government of Nepal’s 2007 goal to bring the entire population of the Terai and Hills within 2 and 4 hours walking distance of paved roads, respectively. Consultants working for the Department of Roads (DOR) accordingly constructed their own 90 meter resolution cost time model and gridded (raster) population data set for focus areas of the Strategic Road