Network (SRN) (DHV _et al._ 2007). The consultants combined these data sets to calculate populations within 2 and 4 hours walking distance of new and existing paved roads and the total person‐hours of travel thus saved by roadway extensions. The DOR’s effort was notable as the only technically analogous accessibility analysis to our own in Nepal we encountered in our literature review. Unfortunately, we could not find evidence that this approach was updated or replicated by the DOR for infrastructure Priority Investment Plans after 2007.

## Our approach

The traditional measures described above all impose some form of penalty in terms of imprecision, lack of generalizability and/or cost. However, geospatial analysis technologies and increasingly high‐quality open data enable more accurate, generalizable and cost‐effective alternative measurements of physical accessibility using earth science technology. The major consideration when using a Geographic Information System (GIS) will be the choice of indicator. Some studies (such as S. Hasan, 2017) use distance, whereas recent studies on global accessibility employ travel time (Weiss D.J., 2018).

This paper favors the latter approach, quantifying accessibility and remoteness to services in Nepal by developing a model of travel costs across a surface of Nepal and using it to calculate the minimum travel time to various facilities from every point in Nepal. To do so we adapted a similar recent model produced for Afghanistan by Kosmidou‐ Bradley and Blankespoor to the Nepali context. The most notable modifications were the inclusion of switchback routes over Nepal’s steepest terrain and a separate monsoon season model to reflect the serious impact of heavy rains on movement over Nepal’s poor roads. The latter echoes Hubert’s work in Nepal and the work of many geographers studying Sub‐Saharan Africa (Hirvonen _et al._ 2017).

We convert the terrain and transport infrastructure to raster travel speeds and conduct appropriate analysis in units of time at a 30m x 30m cell resolution. Doing so requires intricate modeling of various travel modalities and modifiers to standard travel times. The accuracy and reliability of the result was tested and improved through consultations with professionals and organizations well‐acquainted with travel patterns in diverse locations of Nepal. For additional validation we compared model results to reported service travel times from households surveyed in the Nepal Household Risk and Vulnerability Survey (HRVS).

Finally, we consider the accessibility of various service facilities at national, provincial and municipal scales. We compare modeled accessibility levels for each service at each scale to the published standards of the responsible ministry and highlight areas of significant concern.

# Alternative Models of Physical Accessibility

Before describing the methodology underlying our cost time model, we shall justify our belief in its superiority for general usage with a brief discussion of other methods and a comparison of each to cost time models.

The cost time model developed described in this paper is only one of many possible methods of measuring accessibility. Methods must balance thoroughness, data inputs and level of effort, accuracy, generalizability and ease of adaptation / interpretation. In the context of Nepal, we believe our model strikes the strongest balance between these criteria. Other methods emphasize some of these criteria above others, in the process often making them more suitable for specific use cases than general usage (see Table 1 for a summary).