# Road density

Road density summaries per administrative area convey useful information about infrastructure coverage but are inexact proxies for accessibility. These data sets must be collated, sometimes tediously, from individual District Transport Master Plans (DTMPs) in Nepal. Given that many Nepali roads operate poorly or not at all due to bad maintenance, simple roadway lengths may obscure poor accessibility caused by quality issues in the roadways (RAP3 2018). Researchers can ameliorate such problems by more detailed modeling of road quality or reliance on a reliably maintained subset of roads (e.g. paved ones), as with Thapa and Shively (2017). But this raises the burden of data collection, introduces sources of error from erroneously reported road conditions or reduces the detail of the metric. Additionally, such models implicitly discount the importance of off‐road travel and the varying difficulty of such travel in different areas.

In any case such a summary measure suffers from the same resolution and repurposing limitations of Remoteness Indices: such summaries enable comparisons between areas but not individual, localized analysis, e.g. calculating the shortest route between a given set of points, or local accessibility to a particular type of service. They also obscure accessibility dynamics within such areas; assessing whether new roads reach an important economic center or just a politically powerful constituency is impossible. Road length summaries _do_ communicate facts about remoteness easily to users and a non‐technical audience can quickly grasp their means of tabulation. By using a common unit of measure (kilometers) they are also easily incorporated into spatial models. Therefore, they balance well cost, technical complexity and communicative value, and may be appropriate to analyses oriented at non‐ technical audiences, especially where data is readily available. Overall however they are less detailed, more abstract and more limited in their applications than cost time models.

# Survey responses

Reported travel times to services from survey instruments are easy to collect within a standard survey instrument but are subject to such instruments’ limitations. To begin, collecting quality survey data is a complex, expensive and time‐consuming process vulnerable to various types of survey error (for example, see below commentary on GPS error in the Household Vulnerability and Reconstruction Survey (HRVS)). Any of these sources of error can undermine the reliability and validity of the results. Even when surveys are performed well, reported survey times are heavily influenced by individual household dynamics (Ahlstrom et al. 2013). Controlling for fixed effects can offset some of this error but not all effects can be detected. Even then the findings are impossible to generalize beyond the sample frame employed or outside the study area.

Reported travel times offer the precision of a time‐based measure and thus have similar advantages to cost time models in terms of communicative efficiency and analytical flexibility. For this reason, economists commonly employ reported travel times to services from survey instruments to assess the relationship between accessibility and market, household or individual characteristics. Thus, they offer useful but inherently limited looks at accessibility and are most useful when accessibility must be correlated with such characteristics. Cost time models are preferable to reported travel times except where a specific households’ characteristics must be correlated with its specific set of reported times.

# Network analysis

Network analysis is a method of calculating travel times or distances over a road network in a GIS software, measurements which can then be used to look at accessibility in the same manner as a cost time model. It principally requires an accurate, complete roads data set in the area of interest. Accuracy here specifically references the geo‐location of the road centerlines, their surface and quality attributes, and the road speed modifiers attached to these attributes. Where data requirements are met the precision of estimates is high and conveniently scalable to any geographic unit of analysis. By contrast, an incomplete or inaccurate roads data set will yield incorrect routes and misleading distance / time measurements, sometimes dramatically so where