More importantly, reported travel times from surveys are both unreliable and idiosyncratic. In the case of the HRVS data set we believe the variable accuracy of recorded GPS locations disproportionately influenced the rate of convergence where average travel times were low. GPS error within the HRVS data set was high and inconsistent due to the use of multiple tablet models when collecting survey data. As reported above, we discarded 117 GPS points (2% of the total) that fell outside the reported village (ward) boundary, deeming them erroneous. However, we could not assess the quality of points that fell within ward boundaries, many of which may have fallen far from the actual household location.

We believe this led to dramatic relative differences where average travel times were low. For example, 12 minutes modeled and 2 minutes reported yields a 600% difference in results, whereas 130 minutes modeled and 140 minutes reported elsewhere yields only a 7% difference. When assessed comparatively the former will return a poor coefficient of correlation even though in absolute terms the difference is insignificant.

This explains the substantially poorer R value for Province 2, where mean and maximum travel times are much shorter than in other provinces. Subdividing by geography (see Table 14 and Figure 28, Annex III) reinforces this analysis; correlations for households in the Terai are low, yet Terai data clusters close to the regression line in absolute terms. Explaining the low coefficient of correlation in mountain areas is more difficult. It may be that erroneous points there are more likely to return very high travel times because the majority of mountainous areas are very remote. Maps of model results clearly show islands of good access to services clustering near the very few roads and paths in mountainous areas, amidst figurative seas of high travel times (see Figure 27, Annex III). A misplaced GPS point in this environment could easily return an extreme value.

Paradoxically, in Figure 7 the estimated Loess regression lines and a 45 ° line are close in _absolute_ terms for short distances (roughly 30‐45 minutes). This indicates that survey based data may be used confidently in this range (for example, for analysis of accessibility to schools) but researchers and policy makers should employ alternative data, such as our model, for analysis of larger distances. Importantly, this finding does not appear to hold for banks.

When survey‐specific shortcomings are considered alongside the household idiosyncrasies noted in the literature review, the reported coefficients of correlation for model data vs. the HRVS data appear reasonably strong. The clearest point of comparison is a study in Sri Lanka, where comparison between cost time models and surveyed travel times using a ground‐validated model and GPS points still yielded 30% variation in reported travel times around the model mean for the area of study (Ahlström _et al_ . 2011). Despite limited ability to validate model inputs and household survey points, and the above‐mentioned GPS inaccuracies, our reported R values do not greatly exceed this 30% band. Therefore, while it is impossible to guarantee the accuracy of the model at every point in Nepal, we are confident in its analytical validity.