A spatial generalized ordered-response model with skew normal kernel error terms with an application to bicycling frequency

This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is estimated using Bhat's (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an individual's immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and young individuals (18–34 years of age) in particular “warm up” to bicycling as more investment is made in bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments in bicycling infrastructure projects.

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