some road segments do not connect, and the GIS therefore assigns unnecessary detours. Importantly, network analysis assumes all travel happens over the road network and thus cannot factor off‐road travel. Interpolation is required to incorporate off‐road travel into network analysis calculations.

Network analysis can calculate travel in units of time or distance, offering an attractive package of analytical flexibility and communicative efficiency. Like cost time models, it can also handle trips spanning multiple destinations and optimize the order of visits. Therefore, where reliable data exist, it is a strong option for policy makers, analysts and researchers alike. For use cases like logistics planning or routing that must manage multiple travel destinations along established road networks it is arguably the default, preferred method.

Network analysis poorly fits our needs given the importance of off‐road travel and terrain in Nepal and the frequent inaccuracy of its roads geospatial data. In a preliminary analysis of options at the outset of this project the routes and times returned by network analysis over our road network were visibly incorrect even to researchers unfamiliar with the Nepali context.

Furthermore, we had the good fortune of inheriting a complete governmental roads data set from the Rural Access Index; such fortune is unlikely to repeat, and data availability would thus block updates to our eventual model. This limitation also applies to the cost time model but is less severe given the importance of terrain, land cover and other inputs in it, and the imperfect but easy to manage substitute of OpenStreetMap data. OpenStreetMap (OSM) is an ever‐growing open access, volunteer contributed global geographic data set and map, best summarized as “the Wikipedia of maps.” OSM data cannot be dropped so easily into a network analysis as it does not align perfectly with governmental roads data sets, requiring significant tedious labor to manually align both data sets on each update.

This is less important in our 30m x 30m grid where 1‐5 meters of separation between roadways will generally fit within a single cell and thus cause no impact.

# Weighted indices

While not strictly models of physical accessibility, we consider weighted indices here as there are several prominent ones currently used to measure remoteness in Nepal.

Weighted indices simplify accessibility for a facility or area to a relative score by weighting various data points and mathematically integrating them. The data gathering and complex model building required for remoteness indices like the Rural Access Index makes them costly and time‐consuming to compute, particularly at finely detailed levels of analysis. The accuracy of these indices is difficult to verify, dependent as they are on assumptions about the relative weights of inputs and the quality of underlying data. Unless re‐weighted to account for population, service area or other relevant factors indices are also not applicable beyond their specific level of analysis. Area‐based indices for instance obscure local accessibility dynamics when they summarize information at their level of analysis. Dempsey’s analysis, for instance, rates remoteness on a scale of 1‐9 for each village development committee (VDC, old administrative level 3) in Nepal. It is therefore impossible to assess dynamics at the level of households or wards (the new third administrative level, covering several thousand people).

Accessibility indices are very useful for comparisons between VDCs or higher administrative areas and for balancing multiple accessibility criteria. For example, a quick glance at the Rural Access Index allows a policy maker to identify where in Nepal the need is greatest for additional infrastructure investment. Through weighting indices can look beyond simple physical measures of accessibility to consider social dynamics, historical investment patterns, education levels, and other relevant determinants of overall accessibility (albeit at a greater data gathering cost).

However, the resolution of most indices is inappropriate to local applications like planning the actual